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Abuliezi D, She Y, Liao Z, Luo Y, Yang Y, Huang Q, Tao A, Zhuang H. Combined transrectal ultrasound and radiomics model for evaluating the therapeutic effects of neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Int J Colorectal Dis 2025; 40:7. [PMID: 39762476 PMCID: PMC11703880 DOI: 10.1007/s00384-024-04792-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/21/2024] [Indexed: 01/11/2025]
Abstract
PURPOSE This study aimed to explore a combined transrectal ultrasound (TRUS) and radiomics model for predicting tumor regression grade (TRG) after neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced rectal cancer (LARC). METHODS Among 190 patients with LARC, 53 belonged to GRG and 137 to PRG. Eight TRUS parameters were identified as statistically significant (P < 0.05) for distinguishing between the groups, including PSVpre, LDpost, TDpost, CEUS-IGpost, LD change rate, TD change rate, RI change rate, and CEUS-IG downgrade. The accuracies of these individual parameters in predicting TRG were 0.42, 0.62, 0.56, 0.68, 0.67, 0.70, 0.63, and 0.71, respectively. The AUC values were 0.596, 0.597, 0.630, 0.752, 0.686, 0.660, 0.650, and 0.666, respectively. The multi-parameter ultrasonic logistic regression (MPU-LR) model achieved an accuracy of 0.816 and an AUC of 0.851 (95% CI: [0.792-0.909]). The optimal pre- and post-treatment radiomics models were RF (Mean-PCA-RFE-6) and AE (Zscore-PCA-RFE-12), with accuracies of 0.563 and 0.596 and AUCs of 0.601 (95% CI: [0.561-0.641]) and 0.662 (95% CI: [0.630-0.694]), respectively. The combined model (US-RADpre-RADpost) showed the highest predictive power with accuracy and AUC of 0.863 and 0.913. CONCLUSIONS The combined model based on TRUS and radiomics demonstrated remarkable predictive capability for TRG after NCRT. It serves as a precision tool for assessing NCRT response in patients with LARC, impacting treatment strategies.
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Affiliation(s)
- Dilimire Abuliezi
- Department of Medical Ultrasound, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Yufen She
- Department of Gastroenterology and Hepatology, West China Hospital of Sichuan University, 37#Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Zhongfan Liao
- Department of Medical Ultrasound, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Yuan Luo
- Department of Medical Ultrasound, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Yin Yang
- Department of Medical Ultrasound, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Qin Huang
- Department of Medical Ultrasound, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Anqi Tao
- Department of Medical Ultrasound, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Hua Zhuang
- Department of Medical Ultrasound, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
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Chandola S, Soni A, Banerjee S, Bhattacharjee HK, Sharma R, Phulia A, Pathy S, Das CJ. Comparison of intravoxel incoherent motion and diffusion kurtosis imaging and 18- FDG PET/CT in response assessment in rectosigmoid carcinoma. Abdom Radiol (NY) 2024:10.1007/s00261-024-04689-8. [PMID: 39585380 DOI: 10.1007/s00261-024-04689-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 11/05/2024] [Accepted: 11/06/2024] [Indexed: 11/26/2024]
Abstract
OBJECTIVE To evaluate the performance of intravoxel incoherent motion and diffusion kurtosis imaging (IVIM- DKI) in response assessment of rectosigmoid carcinoma to chemo-radiotherapy (CRT) and compare with 18-FDG PET/CT parameters. METHODS A total of 30 patients of recto-sigmoid cancer on CRT underwent baseline staging and follow-up with IVIM - DKI. Out of this cohort, 20 patients underwent 18-FDG PET/CT. IVIM- DKI MRI and PET/CT parameters were noted from both pre and post-chemoradiotherapy (done at 6 weeks after completion) scans. Quantitative IVIM-DKI parameters, viz. apparent (ADC) and molecular (D) diffusion coefficient, perfusion coefficient (f), and kurtosis (K) were measured from non-necrotic areas and semi-quantitative PET parameters including SUV max, SUV ratio, metabolic tumor volume (MTV), total lesion glycolysis (TLG) were also measured. All these parameters correlated with the patient's response keeping RECIST 1.1 criteria as reference standard. RESULTS A statistically significant increase in D and ADC with a significant decline in K was noted after therapy in the entire cohort. These changes were observed in both responders as well as non-responders. No significant differences were observed in the percentage changes of these parameters post therapy amongst both groups. Among 20 patients with follow-up PET/CT imaging, a significant decline in all parameters of primary lesion was seen post-therapy. Responders (n = 12) showed a significant decline in MTV and TLG from baseline after therapy, whereas non-responders did not show any such decline. Change in TLG (ɗ TLG), followed by ɗ MTV had the strongest correlation with a positive response. A ɗ TLG value of ≥ 54.19 carried a 79% sensitivity and 83% specificity in differentiating responders from non responders. CONCLUSION 18-FDG PET/CT is a more accurate single modality for assessing both response and tumor burden post therapy, while ADC and D from IVIM MRI are useful adjuncts to response assessment.
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Affiliation(s)
- Stuti Chandola
- All India Institute of Medical Sciences, New Delhi, India
| | - Abhishek Soni
- All India Institute of Medical Sciences, New Delhi, India
| | - Soham Banerjee
- All India Institute of Medical Sciences, New Delhi, India
| | | | - Raju Sharma
- All India Institute of Medical Sciences, New Delhi, India
| | | | - Sushmita Pathy
- All India Institute of Medical Sciences, New Delhi, India
| | - Chandan J Das
- All India Institute of Medical Sciences, New Delhi, India.
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Zheng Y, Zhou L, Huang W, Han N, Zhang J. Histogram analysis of multiple diffusion models for predicting advanced non-small cell lung cancer response to chemoimmunotherapy. Cancer Imaging 2024; 24:71. [PMID: 38863062 PMCID: PMC11167789 DOI: 10.1186/s40644-024-00713-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 05/28/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND There is an urgent need to find a reliable and effective imaging method to evaluate the therapeutic efficacy of immunochemotherapy in advanced non-small cell lung cancer (NSCLC). This study aimed to investigate the capability of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis based on different region of interest (ROI) selection methods for predicting treatment response to chemoimmunotherapy in advanced NSCLC. METHODS Seventy-two stage III or IV NSCLC patients who received chemoimmunotherapy were enrolled in this study. IVIM and DKI were performed before treatment. The patients were classified as responders group and non-responders group according to the Response Evaluation Criteria in Solid Tumors 1.1. The histogram parameters of ADC, Dslow, Dfast, f, Dk and K were measured using whole tumor volume ROI and single slice ROI analysis methods. Variables with statistical differences would be included in stepwise logistic regression analysis to determine independent parameters, by which the combined model was also established. And the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of histogram parameters and the combined model. RESULTS ADC, Dslow, Dk histogram metrics were significantly lower in the responders group than in the non-responders group, while the histogram parameters of f were significantly higher in the responders group than in the non-responders group (all P < 0.05). The mean value of each parameter was better than or equivalent to other histogram metrics, where the mean value of f obtained from whole tumor and single slice both had the highest AUC (AUC = 0.886 and 0.812, respectively) compared to other single parameters. The combined model improved the diagnostic efficiency with an AUC of 0.968 (whole tumor) and 0.893 (single slice), respectively. CONCLUSIONS Whole tumor volume ROI demonstrated better diagnostic ability than single slice ROI analysis, which indicated whole tumor histogram analysis of IVIM and DKI hold greater potential than single slice ROI analysis to be a promising tool of predicting therapeutic response to chemoimmunotherapy in advanced NSCLC at initial state.
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Affiliation(s)
- Yu Zheng
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Liang Zhou
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Wenjing Huang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Na Han
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Jing Zhang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China.
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China.
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Ma Q, Liu Z, Zhang J, Fu C, Li R, Sun Y, Tong T, Gu Y. Multi-task reconstruction network for synthetic diffusion kurtosis imaging: Predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer. Eur J Radiol 2024; 174:111402. [PMID: 38461737 DOI: 10.1016/j.ejrad.2024.111402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/12/2024] [Accepted: 02/29/2024] [Indexed: 03/12/2024]
Abstract
PURPOSE To assess the feasibility and clinical value of synthetic diffusion kurtosis imaging (DKI) generated from diffusion weighted imaging (DWI) through multi-task reconstruction network (MTR-Net) for tumor response prediction in patients with locally advanced rectal cancer (LARC). METHODS In this retrospective study, 120 eligible patients with LARC were enrolled and randomly divided into training and testing datasets with a 7:3 ratio. The MTR-Net was developed for reconstructing Dapp and Kapp images from apparent diffusion coefficient (ADC) images. Tumor regions were manually segmented on both true and synthetic DKI images. The synthetic image quality and manual segmentation agreement were quantitatively assessed. The support vector machine (SVM) classifier was used to construct radiomics models based on the true and synthetic DKI images for pathological complete response (pCR) prediction. The prediction performance for the models was evaluated by the receiver operating characteristic (ROC) curve analysis. RESULTS The mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) for tumor regions were 0.212, 24.278, and 0.853, respectively, for the synthetic Dapp images and 0.516, 24.883, and 0.804, respectively, for the synthetic Kapp images. The Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), and Hausdorff distance (HD) for the manually segmented tumor regions were 0.786, 0.844, 0.755, and 0.582, respectively. For predicting pCR, the true and synthetic DKI-based radiomics models achieved area under the curve (AUC) values of 0.825 and 0.807 in the testing datasets, respectively. CONCLUSIONS Generating synthetic DKI images from DWI images using MTR-Net is feasible, and the efficiency of synthetic DKI images in predicting pCR is comparable to that of true DKI images.
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Affiliation(s)
- Qiong Ma
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, China
| | - Zonglin Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, China
| | - Jiadong Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong 999077, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen 518057, China
| | - Rong Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, China
| | - Yiqun Sun
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, China.
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, China.
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, China.
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Jiang X, Hu Z, Wang S, Zhang Y. Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers (Basel) 2023; 15:3608. [PMID: 37509272 PMCID: PMC10377683 DOI: 10.3390/cancers15143608] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/10/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.
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Grants
- RM32G0178B8 BBSRC
- MC_PC_17171 MRC, UK
- RP202G0230 Royal Society, UK
- AA/18/3/34220 BHF, UK
- RM60G0680 Hope Foundation for Cancer Research, UK
- P202PF11 GCRF, UK
- RP202G0289 Sino-UK Industrial Fund, UK
- P202ED10, P202RE969 LIAS, UK
- P202RE237 Data Science Enhancement Fund, UK
- 24NN201 Fight for Sight, UK
- OP202006 Sino-UK Education Fund, UK
- RM32G0178B8 BBSRC, UK
- 2023SJZD125 Major project of philosophy and social science research in colleges and universities in Jiangsu Province, China
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Affiliation(s)
- Xiaoyan Jiang
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China; (X.J.); (Z.H.)
| | - Zuojin Hu
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China; (X.J.); (Z.H.)
| | - Shuihua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK;
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK;
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Granata V, Fusco R, Setola SV, Cozzi D, Rega D, Petrillo A. Diffusion and Perfusion Imaging in Rectal Cancer Restaging. Semin Ultrasound CT MR 2023; 44:117-125. [PMID: 37245878 DOI: 10.1053/j.sult.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
The assessment of tumor response, after neoadjuvant radiochemotherapy (n-CRT), permits the stratification of patients for the proper therapeutical management. Although histopathology analysis of the surgical speciemen is considered the gold standard for assessing tumor response, magnetic resonance imaging (MRI), with its significant developments in technical imaging, have allowed an increase in accuracy for the evaluation of response. MRI provides a radiological tumor regression grade (mrTRG) that is correlated with the pathologic tumor regression grade (pTRG). Functional MRI parameters have additional impending in early prediction of the efficacy of therapy. Some of functional methodologies are already part of clinical practice: diffusion-weighted MRI (DW-MRI) and perfusion imaging (dynamic contrast enhanced MRI [DCE-MRI]).
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | | | - Sergio Venazio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy; Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
| | - Daniela Rega
- Division of Gastrointestinal Surgical Oncology, "Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale", Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
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Quantitative synthetic MRI for predicting locally advanced rectal cancer response to neoadjuvant chemoradiotherapy. Eur Radiol 2023; 33:1737-1745. [PMID: 36380196 DOI: 10.1007/s00330-022-09191-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/08/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVES To investigate the value of pre-treatment quantitative synthetic MRI (SyMRI) for predicting a good response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer. METHODS This prospective study enrolled 63 patients with locally advanced rectal cancer scheduled to undergo preoperative chemoradiotherapy from January 2019 to June 2021. T1 relaxation time (T1), T2 relaxation time (T2), proton density (PD) from synthetic MRI, and apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) were measured. Independent-sample t-test, the Mann-Whitney U test, the Delong test, and receiver operating characteristic curve (ROC) analyses were used to predict the pathologic complete response (pCR) and T-downstaging. RESULTS Among the 63 patients, 19 (30%) achieved pCR and 44 (70%) did not, and 24 (38%) achieved T-downstaging, while 44 (62%) did not. The mean T1 and T2 values were significantly lower in the pCR group compared with those in the non-pCR group and in the T-downstage group compared with those in the non-T-downstage group (all p < 0.05). There were no significant differences in the PD and ADC values between the two groups. There were no significant differences between the mean values of T1 and T2 for predicting pCR after CRT (AUC, 0.767 vs. 0.831, p = 0.37). There were no significant differences between the AUC values of T1 and T2 values for the assessment of post-CRT T-downstaging (AUC, 0.746 vs. 0.820, p = 0.506). CONCLUSIONS In patients with locally advanced rectal cancer, the synthetic MRI-derived T1 relaxation time and T2 relaxation time values are promising imaging markers for predicting a good response to neoadjuvant chemoradiotherapy. KEY POINTS • Mean T1 and T2 values were significantly lower in the pathologic complete response group and the T-downstage group. • There were no significant differences in the proton density and apparent diffusion coefficient values between the two groups.
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Ingle M, Blackledge M, White I, Wetscherek A, Lalondrelle S, Hafeez S, Bhide S. Quantitative analysis of diffusion weighted imaging in rectal cancer during radiotherapy using a magnetic resonance imaging integrated linear accelerator. Phys Imaging Radiat Oncol 2022; 23:32-37. [PMID: 35756883 PMCID: PMC9214864 DOI: 10.1016/j.phro.2022.06.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 05/16/2022] [Accepted: 06/02/2022] [Indexed: 11/25/2022] Open
Abstract
Background and purpose Magnetic resonance imaging integrated linear accelerator (MR-Linac) platforms enable acquisition of diffusion weighted imaging (DWI) during treatment providing potential information about treatment response. Obtaining DWI on these platforms is technically different from diagnostic magnetic resonance imaging (MRI) scanners. The aim of this project was to determine feasibility of obtaining DWI and calculating apparent diffusion coefficient (ADC) parameters longitudinally in rectal cancer patients on the MR-Linac. Materials and methods Nine patients undergoing treatment on MR-Linac had DWI acquired using b-values 0, 30, 150, 500 s/mm2. Gross tumour volume (GTV) and normal tissue was delineated on DWI throughout treatment and median ADC was calculated using an in-house tool (pyOsirix ®). Results Seven out of nine patients were included in the analysis; all demonstrated downstaging at follow-up. A total of 63 out of 70 DWI were analysed (7 excluded due to poor image quality). An increasing trend of ADC median for GTV (1.15 × 10-3 mm2/s interquartile range (IQ): 1.05-1.17 vs 1.59 × 10-3 mm2/s IQ: 1.37 - 1.64; p = 0.0156), correlating to treatment response. In comparison ADC median for normal tissue remained the same between first and last fraction (1.61 × 10-3 mm2/s IQ: 1.56-1.71 vs 1.67 × 10-3 mm2/s IQ: 1.37-2.00; p = 0.9375). Conclusions DWI assessment in rectal cancer patients on MR-Linac is feasible. Initial results provide foundations for further studies to determine DWI use for treatment adaptation in rectal cancer.
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Affiliation(s)
- Manasi Ingle
- The Royal Marsden Hospital NHS Trust, 203 Fulham Road, London SW3 6JJ, UK
- The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Matthew Blackledge
- The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Ingrid White
- Guys and St Thomas NHS Trust, Great Maze Pond, London SE1 9RT, UK
| | - Andreas Wetscherek
- The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Susan Lalondrelle
- The Royal Marsden Hospital NHS Trust, 203 Fulham Road, London SW3 6JJ, UK
- The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Shaista Hafeez
- The Royal Marsden Hospital NHS Trust, 203 Fulham Road, London SW3 6JJ, UK
- The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Shreerang Bhide
- The Royal Marsden Hospital NHS Trust, 203 Fulham Road, London SW3 6JJ, UK
- The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
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Jiménez de los Santos ME, Reyes-Pérez JA, Domínguez Osorio V, Villaseñor-Navarro Y, Moreno-Astudillo L, Vela-Sarmiento I, Sollozo-Dupont I. Whole lesion histogram analysis of apparent diffusion coefficient predicts therapy response in locally advanced rectal cancer. World J Gastroenterol 2022; 28:2609-2624. [PMID: 35949349 PMCID: PMC9254137 DOI: 10.3748/wjg.v28.i23.2609] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/25/2021] [Accepted: 04/25/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Whole-tumor apparent diffusion coefficient (ADC) histogram analysis is relevant to predicting the neoadjuvant chemoradiation therapy (nCRT) response in patients with locally advanced rectal cancer (LARC).
AIM To evaluate the performance of ADC histogram-derived parameters for predicting the outcomes of patients with LARC.
METHODS This is a single-center, retrospective study, which included 48 patients with LARC. All patients underwent a pre-treatment magnetic resonance imaging (MRI) scan for primary tumor staging and a second restaging MRI for response evaluation. The sample was distributed as follows: 18 responder patients (R) and 30 non-responders (non-R). Eight parameters derived from the whole-lesion histogram analysis (ADCmean, skewness, kurtosis, and ADC10th, 25th, 50th, 75th, 90th percentiles), as well as the ADCmean from the hot spot region of interest (ROI), were calculated for each patient before and after treatment. Then all data were compared between R and non-R using the Mann-Whitney U test. Two measures of diagnostic accuracy were applied: the receiver operating characteristic curve and the diagnostic odds ratio (DOR). We also reported intra- and interobserver variability by calculating the intraclass correlation coefficient (ICC).
RESULTS Post-nCRT kurtosis, as well as post-nCRT skewness, were significantly lower in R than in non-R (both P < 0.001, respectively). We also found that, after treatment, R had a larger loss of both kurtosis and skewness than non-R (∆%kurtosis and ∆skewness, P < 0.001). Other parameters that demonstrated changes between groups were post-nCRT ADC10th, ∆%ADC10th, ∆%ADCmean, and ROI ∆%ADCmean. However, the best diagnostic performance was achieved by ∆%kurtosis at a threshold of 11.85% (Area under the receiver operating characteristic curve [AUC] = 0.991, DOR = 376), followed by post-nCRT kurtosis = 0.78 × 10-3 mm2/s (AUC = 0.985, DOR = 375.3), ∆skewness = 0.16 (AUC = 0.885, DOR = 192.2) and post-nCRT skewness = 1.59 × 10-3 mm2/s (AUC = 0.815, DOR = 168.6). Finally, intraclass correlation coefficient analysis showed excellent intraobserver and interobserver agreement, ensuring the implementation of histogram analysis into routine clinical practice.
CONCLUSION Whole-tumor ADC histogram parameters, particularly kurtosis and skewness, are relevant biomarkers for predicting the nCRT response in LARC. Both parameters appear to be more reliable than ADCmean from one-slice ROI.
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Affiliation(s)
| | | | | | | | | | - Itzel Vela-Sarmiento
- Department of Gastrointestinal Surgery, National Cancer Institute, Mexico 14080, Mexico
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Granata V, Fusco R, Belli A, Danti G, Bicci E, Cutolo C, Petrillo A, Izzo F. Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when. Infect Agent Cancer 2022; 17:25. [PMID: 35681237 PMCID: PMC9185934 DOI: 10.1186/s13027-022-00441-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 05/30/2022] [Indexed: 12/13/2022] Open
Abstract
This article provides an overview of diffusion kurtosis (DKI) imaging in abdominal oncology. DKI allows for more data on tissue structures than the conventional diffusion model (DWI). However, DKI requires high quality images at b-values greater than 1000 s/mm2 and high signal-to-noise ratio (SNR) that traditionally MRI systems are not able to acquire and therefore there are generally amplified anatomical distortions on the images due to less homogeneity of the field. Advances in both hardware and software on modern MRI scanners have currently enabled ultra-high b-value imaging and offered the ability to apply DKI to multiple extracranial sites. Previous studies have evaluated the ability of DKI to characterize and discriminate tumor grade compared to conventional DWI. Additionally, in several studies the DKI sequences used were based on planar echo (EPI) acquisition, which is susceptible to motion, metal and air artefacts and prone to low SNRs and distortions, leading to low quality images for some small lesions, which may affect the accuracy of the results. Another problem is the optimal b-value of DKI, which remains to be explored and not yet standardized, as well as the manual selection of the ROI, which could affect the accuracy of some parameters.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy.
| | | | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
| | - Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology, SIRM Foundation, Milan, Italy
| | - Eleonora Bicci
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
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Lundholm L, Montelius M, Jalnefjord O, Forssell-Aronsson E, Ljungberg M. VERDICT MRI for radiation treatment response assessment in neuroendocrine tumors. NMR IN BIOMEDICINE 2022; 35:e4680. [PMID: 34957637 DOI: 10.1002/nbm.4680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 12/09/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Noninvasive methods to study changes in tumor microstructure enable early assessment of treatment response and thus facilitate personalized treatment. The aim of this study was to evaluate the diffusion MRI model, Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT), for early response assessment to external radiation treatment and to compare the results with those of more studied sets of parameters derived from diffusion-weighted MRI data. Mice xenografted with human small intestine tumors were treated with external radiation treatment, and diffusion MRI experiments were performed on the day before and up to 2 weeks after treatment. The diffusion models VERDICT, ADC, IVIM, and DKI were fitted to MRI data, and the treatment response of each tumor was calculated based on pretreatment tumor growth and post-treatment tumor volume regression. Linear regression and correlation analysis were used to evaluate each model and their respective parameters for explaining the treatment response. VERDICT analysis showed significant changes from day -1 to day 3 for the intracellular and extracellular volume fraction, as well as the cell radius index (p < 0.05; Wilcoxon signed-rank test). The strongest correlation between the diffusion model parameters and the tumor treatment response was seen for the ADC, kurtosis-corrected diffusion coefficient, and intracellular volume fraction on day 3 (τ = 0.47, 0.52, and -0.49, respectively, p < 0.05; Kendall rank correlation coefficient). Of all the tested models, VERDICT held the strongest explanatory value for the tumor treatment response on day 3 (R2 = 0.75, p < 0.01; linear regression). In conclusion, VERDICT has potential for early assessment of external radiation treatment and may provide further insights into the underlying biological effects of radiation on tumor tissue. In addition, the results suggest that the time window for assessment of treatment response using dMRI may be narrow.
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Affiliation(s)
- Lukas Lundholm
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Mikael Montelius
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Oscar Jalnefjord
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, MRI Center, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Eva Forssell-Aronsson
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, MRI Center, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Maria Ljungberg
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, MRI Center, Sahlgrenska University Hospital, Gothenburg, Sweden
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12
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Borgheresi A, De Muzio F, Agostini A, Ottaviani L, Bruno A, Granata V, Fusco R, Danti G, Flammia F, Grassi R, Grassi F, Bruno F, Palumbo P, Barile A, Miele V, Giovagnoni A. Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective. J Clin Med 2022; 11:2599. [PMID: 35566723 PMCID: PMC9104021 DOI: 10.3390/jcm11092599] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 12/12/2022] Open
Abstract
The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.
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Affiliation(s)
- Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
| | - Letizia Ottaviani
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale IRCCS di Napoli, 80131 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
| | - Ginevra Danti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Federica Flammia
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Francesca Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Abruzzo Health Unit 1, Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, 67100 L’Aquila, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
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Guo J, Dong C, Wu Z, Sun W, Li X, Zhou R, Xu W. Diffusion kurtosis imaging assessment of the response to radiotherapy in a VX2 bone tumor model: an animal study. Acta Radiol 2022; 63:182-191. [PMID: 33535770 DOI: 10.1177/0284185121989519] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND Neoadjuvant radiotherapy plays a vital role in the treatment of malignant bone tumors, and non-invasive imaging methods are needed to evaluate the response to treatment. PURPOSE To assess the value of diffusion kurtosis imaging (DKI) for monitoring early response to radiotherapy in malignant bone tumors. MATERIAL AND METHODS Treatment response was evaluated in a rabbit VX2 bone tumor model (n = 35) using magnetic resonance imaging (MRI), DKI, and histopathologic examinations. Subjects were divided into three groups: pre-treatment, post-treatment, and control groups. The post-treatment group was subclassified into good response and poor response groups according to the results of histopathologic examination. Apparent diffusion coefficient (ADC) and DKI parameters (mean diffusion coefficient [MD] and mean kurtosis [MK]) were recorded. The relationship between ADC, DKI parameters, and histopathologic changes after radiotherapy was determined using Pearson's correlation coefficient. The diagnostic performance of these parameters was assessed using receiver operating characteristic analysis. RESULTS MD in the good response group was higher after treatment than before treatment (P < 0.001) and higher than that in the poor response group (P = 0.009). MD was highly correlated with tumor cell density and apoptosis rate (r = -0.771, P < 0.001 and r = 0.625, P < 0.001, respectively). MD was superior to other parameters for determining the curative effect of radiotherapy, with a sensitivity of 75.0%, specificity of 100.0%, and area under the curve of 0.917 (P < 0.001). CONCLUSION The correlations between MD, tumor cell density, and apoptosis suggest that MD could be useful for assessing the early response to radiotherapy in rabbit VX2 malignant bone tumors.
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Affiliation(s)
- Jia Guo
- Department of Radiology; The Affiliated Hospital of Qingdao University Qingdao, Shandong, PR China
| | - Cheng Dong
- Department of Radiology; The Affiliated Hospital of Qingdao University Qingdao, Shandong, PR China
| | - Zengjie Wu
- Department of Radiology; The Affiliated Hospital of Qingdao University Qingdao, Shandong, PR China
| | - Weikai Sun
- Department of Radiology; The Affiliated Hospital of Qingdao University Qingdao, Shandong, PR China
| | - Xiaoli Li
- Department of Radiology; The Affiliated Hospital of Qingdao University Qingdao, Shandong, PR China
| | - Ruizhi Zhou
- Department of Radiology; The Affiliated Hospital of Qingdao University Qingdao, Shandong, PR China
| | - Wenjian Xu
- Department of Radiology; The Affiliated Hospital of Qingdao University Qingdao, Shandong, PR China
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Munk NE, Bondeven P, Pedersen BG. Diagnostic performance of MRI and endoscopy for assessing complete response in rectal cancer after neoadjuvant chemoradiotherapy: a systematic review of the literature. Acta Radiol 2021; 64:20-31. [PMID: 34928715 DOI: 10.1177/02841851211065925] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND The diagnostic performance of magnetic resonance imaging (MRI) modalities and/or endoscopy for assessing complete response in rectal cancer after neoadjuvant chemoradiotherapy (nCRT) is unclear. PURPOSE To summarize existing evidence on the diagnostic performance of diffusion-weighted MRI, perfusion-weighted MRI, T2-weighted MR tumor regression grade, and/or endoscopy for assessing complete tumor response after nCRT. MATERIAL AND METHODS MEDLINE and Embase databases were searched. The PRISMA guidelines were followed. Sensitivity, specificity, negative predictive, and positive predictive values were retrieved from included studies. RESULTS In total, 81 studies were eligible for inclusion. Evidence suggests that combined use of MRI and endoscopy tends to improve the diagnostic performance compared to single imaging modality. The positive predictive value of a complete response varies substantially between studies. There is considerable heterogeneity between studies. CONCLUSION Combined re-staging tends to improve diagnostic performance compared to single imaging modality, but the vast majority of studies fail to offer true clinical value due to the study heterogeneity.
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Affiliation(s)
| | - Peter Bondeven
- Department of Surgery, Regional Hospital Randers, Randers, Denmark
| | - Bodil Ginnerup Pedersen
- Department of Radiology, Aarhus University Hospital, Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus N, Denmark
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Zhu HT, Zhang XY, Shi YJ, Li XT, Sun YS. The Conversion of MRI Data With Multiple b-Values into Signature-Like Pictures to Predict Treatment Response for Rectal Cancer. J Magn Reson Imaging 2021; 56:562-569. [PMID: 34913210 DOI: 10.1002/jmri.28033] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/05/2021] [Accepted: 12/08/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Diffusion weighted imaging (DWI) at multiple b-values has been used to predict the pathological complete response (pCR) to neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Non-Gaussian models fit the signal decay of diffusion by several physical values from different approaches of approximation. PURPOSE To develop a deep learning method to analyze DWI data scanned at multiple b-values independent on Gaussian or non-Gaussian models and to apply to a rectal cancer neoadjuvant chemoradiotherapy model. STUDY TYPE Retrospective. POPULATION A total of 472 participants (age: 56.6 ± 10.5 years; 298 males and 174 females) with locally advanced adenocarcinoma were enrolled and chronologically divided into a training group (n = 200; 42 pCR/158 non-pCR), a validation group (n = 72; 11 pCR/61 non-pCR) and a test group (n = 200; 44 pCR/156 non-pCR). FIELD STRENGTH/SEQUENCE A 3.0 T MRI scanner. DWI with a single-shot spin echo-planar imaging pulse sequence at 12 b-values (0, 20, 50, 100, 200, 400, 600, 800, 1000, 1200, 1400, and 1600 sec/mm2 ). ASSESSMENT DWI signals from manually delineated tumor region were converted into a signature-like picture by concatenating all histograms from different b-values. Pathological results (pCR/non-pCR) were used as the ground truth for deep learning. Gaussian and non-Gaussian methods were used for comparison. STATISTICAL TESTS Analysis of variance for age; Chi-square for gender and pCR/non-pCR; area under the receiver operating characteristic (ROC) curve (AUC); DeLong test for AUC. P < 0.05 for significant difference. RESULTS The AUC in the test group is 0.924 (95% CI: 0.866-0.983) for the signature-like pictures converted from 35 bins, and it is 0.931 (95% CI: 0.884-0.979) for the signature-like pictures converted from 70 bins, which is significantly (Z = 3.258, P < 0.05) larger than Dapp , the best predictor in non-Gaussian methods with AUC = 0.773 (95% CI: 0.682-0.865). DATA CONCLUSION The proposed signature-like pictures provide more accurate pretreatment prediction of the response to neoadjuvant chemoradiotherapy than the fitted methods for locally advanced rectal cancer. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Hai-Tao Zhu
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, China
| | - Xiao-Yan Zhang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, China
| | - Yan-Jie Shi
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, China
| | - Xiao-Ting Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, China
| | - Ying-Shi Sun
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, China
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Recent Advances in Functional MRI to Predict Treatment Response for Locally Advanced Rectal Cancer. CURRENT COLORECTAL CANCER REPORTS 2021. [DOI: 10.1007/s11888-021-00470-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Ng SP, Ngan SY, Leong T. Current State of Neoadjuvant Radiotherapy for Rectal Cancer. Clin Colorectal Cancer 2021; 21:63-70. [PMID: 34852972 DOI: 10.1016/j.clcc.2021.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 10/27/2021] [Accepted: 10/31/2021] [Indexed: 11/11/2022]
Abstract
Colorectal cancer is the third most commonly diagnosed cancer, with rectal cancer accounting for 30% of cases. The current standard of care curative treatment for locally advanced rectal cancer is (chemo)radiotherapy followed by surgery and adjuvant chemotherapy. Although neoadjuvant radiotherapy has reduced the risk of local recurrence to less than 10%, the risk of distant metastasis remained high at 30% affecting patient survival. In addition, there is a recognition that there is heterogeneity in tumor biology and treatment response with good responders potentially suitable for treatment de-escalation. Therefore, new treatment sequencing and regimens were investigated. Here, we reviewed the evidence for current neoadjuvant treatment options in patients with locally advanced rectal adenocarcinoma, and highlight the new challenges in this new treatment landscape.
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Affiliation(s)
- Sweet Ping Ng
- Department of Radiation Oncology, Olivia Newton-John Cancer Centre, Austin Health, Melbourne, Australia; School of Molecular Sciences, La Trobe University, Melbourne, Australia; Department of Surgery, The University of Melbourne, Melbourne, Australia.
| | - Samuel Y Ngan
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia
| | - Trevor Leong
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia
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18
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Li D, Cui Y, Hou L, Bian Z, Yang Z, Xu R, Jia Y, Wu Z, Yang X. Diffusion kurtosis imaging-derived histogram metrics for prediction of resistance to neoadjuvant chemoradiotherapy in rectal adenocarcinoma: Preliminary findings. Eur J Radiol 2021; 144:109963. [PMID: 34562744 DOI: 10.1016/j.ejrad.2021.109963] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 09/14/2021] [Accepted: 09/16/2021] [Indexed: 01/04/2023]
Abstract
PURPOSE This study aimed to evaluate the potential role of diffusion kurtosis imaging (DKI)-derived parameters for assessing resistance to CRT in patients with Locally advanced rectal cancer (LARC) by using histogram analysis derived from whole-tumor volumes. METHOD 136 consecutive patients with histologically confirmed rectal adenocarcinoma who underwent MRI examination before and after chemoradiotherapy were enrolled in our retrospective study. The parameters D, K, and conventional apparent diffusion coefficient (ADC) were measured using whole-tumor volume histogram analysis. The AJCC tumor regression grading (TRG) system was the standard reference (resistance: TRG 3; non-resistance: TRG 0-2). Receiver operating characteristic (ROC) curves were used for evaluating the diagnostic performance. RESULTS Aside from the skew and kurtosis values, we found all the histogram metrics of D and ADC values significantly increased after CRT (all p < 0.001). In contrast, the histogram metrics of K values significantly decreased after CRT. The majority of percentiles metrics of D, K, and ADC values were correlated with tumor resistance before and after CRT (P < 0.05), except for the skew and kurtosis values. Regarding the comparison of the diagnostic performance of all the histogram metrics, the percentage Dmean change (ΔDmean) showed the highest AUC value of 0.939, and the corresponding sensitivity, specificity, PPV, and NPV were 84.1% and 94.6%, 88.1% and 92.6%, respectively. CONCLUSIONS These preliminary results demonstrated that DKI-derived histogram metrics, especially the pre-treatment metrics and ΔDmean, were useful to assess tumoral resistance to CRT and individual clinical management for patients with LARC.
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Affiliation(s)
- Dandan Li
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan 030013, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan 030013, China
| | - Lina Hou
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan 030013, China
| | - Zeyu Bian
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan 030013, China
| | - Zhao Yang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan 030013, China
| | - Ruxin Xu
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan 030013, China
| | - Yaju Jia
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan 030013, China
| | - Zhifang Wu
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan 030001, Shanxi, China; Shanxi Medical University, Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Taiyuan 030001, Shanxi, China.
| | - Xiaotang Yang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan 030013, China.
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Xu Q, Xu Y, Sun H, Jiang T, Xie S, Ooi BY, Ding Y. MRI Evaluation of Complete Response of Locally Advanced Rectal Cancer After Neoadjuvant Therapy: Current Status and Future Trends. Cancer Manag Res 2021; 13:4317-4328. [PMID: 34103987 PMCID: PMC8179813 DOI: 10.2147/cmar.s309252] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 05/08/2021] [Indexed: 12/29/2022] Open
Abstract
Complete tumor response can be achieved in a certain proportion of patients with locally advanced rectal cancer, who achieve maximal response to neoadjuvant therapy (NAT). For these patients, a watch-and-wait (WW) or nonsurgical strategy has been proposed and is becoming widely practiced in order to avoid unnecessary surgical complications. Therefore, a non-invasive, reliable diagnostic tool for accurately evaluating complete tumor response is needed. Magnetic resonance imaging (MRI) plays a crucial role in both primary staging and restaging tumor response to NAT in rectal cancer without relying on resected specimen. In recent years, numerous efforts have been made to research the value of MRI in predicting and evaluating complete response in rectal cancer. Current MRI evaluation is mainly based on morphological and functional images. Morphologic MRI yields high soft tissue resolution, multiplanar images, and provides detailed depictions of rectal cancer and its surrounding structures. Functional MRI may help to distinguish residual tumor from fibrosis, therefore improving the diagnostic performance of morphologic MRI in identifying complete tumor response. Both morphologic and functional MRI have several promising parameters that may help accurately evaluate and/or predict complete response of rectal cancer. However, these parameters still have limitations and the results remain inconsistent. Recent development of new techniques, such as textural analysis, radiomics analysis and deep learning, demonstrate great potential based on MRI-derived parameters. This article aimed to review and help better understand the strengths, limitations, and future trends of these MRI-derived methods in evaluating complete response in rectal cancer.
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Affiliation(s)
- Qiaoyu Xu
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yanyan Xu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Hongliang Sun
- Department of Radiology, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Tao Jiang
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Sheng Xie
- Department of Radiology, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Bee Yen Ooi
- Department of Radiology, Hospital Seberang Jaya, Penang, Malaysia
| | - Yi Ding
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
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Surov A, Pech M, Powerski M, Woidacki K, Wienke A. Pretreatment Apparent Diffusion Coefficient Cannot Predict Histopathological Features and Response to Neoadjuvant Radiochemotherapy in Rectal Cancer: A Meta-Analysis. Dig Dis 2021; 40:33-49. [PMID: 33662962 PMCID: PMC8820443 DOI: 10.1159/000515631] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 02/24/2021] [Indexed: 02/02/2023]
Abstract
AIM Our purpose was to perform a systemic literature review and meta-analysis regarding use of apparent diffusion coefficient (ADC) for prediction of histopathological features in rectal cancer (RC) and to prove if ADC can predict treatment response to neoadjuvant radiochemotherapy (NARC) in RC. METHODS MEDLINE library, EMBASE, Cochrane, and SCOPUS database were screened for associations between ADC and histopathology and/or treatment response in RC up to June 2020. Authors, year of publication, study design, number of patients, mean value, and standard deviation of ADC were acquired. The methodological quality of the collected studies was checked according to the Quality Assessment of Diagnostic Studies instrument. The meta-analysis was undertaken by using the RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used to account the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated. RESULTS Overall, 37 items (2,015 patients) were included. ADC values of tumors with different T and N stages and grades overlapped strongly. ADC cannot distinguish RC with a high- and low-carcinoembryonic antigen level. Regarding KRAS status, ADC cannot discriminate mutated and wild-type RC. ADC did not correlate significantly with expression of vascular endothelial growth factor and hypoxia-inducible factor 1a. ADC correlates with Ki 67, with the calculated correlation coefficient: -0.52. The ADC values in responders and nonresponders overlapped significantly. CONCLUSION ADC correlates moderately with expression of Ki 67 in RC. ADC cannot discriminate tumor stages, grades, and KRAS status in RC. ADC cannot predict therapy response to NARC in RC.
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Affiliation(s)
- Alexey Surov
- Clinic for Radiology and Nuclear Medicine, Otto-von-Guericke University, Magdeburg, Germany
| | - Maciej Pech
- Clinic for Radiology and Nuclear Medicine, Otto-von-Guericke University, Magdeburg, Germany
| | - Maciej Powerski
- Clinic for Radiology and Nuclear Medicine, Otto-von-Guericke University, Magdeburg, Germany
| | - Katja Woidacki
- Experimental Radiology, Clinic for Radiology and Nuclear Medicine, Otto-von-Guericke University, Magdeburg, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
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21
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Chen K, She HL, Wu T, Hu F, Li T, Luo LP. Comparison of percentage changes in quantitative diffusion parameters for assessing pathological complete response to neoadjuvant therapy in locally advanced rectal cancer: a meta-analysis. Abdom Radiol (NY) 2021; 46:894-908. [PMID: 32975646 DOI: 10.1007/s00261-020-02770-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 09/02/2020] [Accepted: 09/10/2020] [Indexed: 02/01/2023]
Abstract
PURPOSE To evaluate and compare the diagnostic performance of percentage changes in apparent diffusion coefficient (∆ADC%) and slow diffusion coefficient (∆D%) for assessing pathological complete response (pCR) to neoadjuvant therapy in patients with locally advanced rectal cancer (LARC). METHODS A systematic search in PubMed, EMBASE, the Web of Science, and the Cochrane Library was performed to retrieve related original studies. For each parameter (∆ADC% and ∆D%), we pooled the sensitivity, specificity and calculated the area under summary receiver operating characteristic curve (AUROC) values. Meta-regression and subgroup analyses were performed to explore heterogeneity among the studies on ∆ADC%. RESULTS 15 original studies (804 patients with 805 lesions, 15 studies on ∆ADC%, 4 of the studies both on ∆ADC% and ∆D%) were included. pCR was observed in 213 lesions (26.46%). For the assessment of pCR, the pooled sensitivity, specificity and AUROC of ∆ADC% were 0.83 (95% confidence intervals [CI] 0.76, 0.89), 0.74 (95% CI 0.66, 0.81), 0.87 (95% CI 0.83, 0.89), and ∆D% were 0.70 (95% CI 0.52, 0.84), 0.81 (95% CI 0.65, 0.90), 0.81 (95% CI 0.77, 0.84), respectively. In the four studies on the both metrics, ∆ADC% yielded an equivalent diagnostic performance (AUROC 0.80 [95% CI 0.76, 0.83]) to ∆D%, but lower than in the studies (n = 11) only on ∆ADC% (AUROC 0.88 [95% CI 0.85, 0.91]). Meta-regression and subgroup analyses showed no significant factors affecting heterogeneity. CONCLUSIONS Our meta-analysis confirms that ∆ADC% could reliably evaluate pCR in patients with LARC after neoadjuvant therapy. ∆D% may not be superior to ∆ADC%, which deserves further investigation.
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Affiliation(s)
- Kai Chen
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, 613 Huangpu Street, Guangzhou, 510630, China
- Department of Radiology, Affiliated Hospital of Xiangnan University (Clinical College), 25 Renmin West Road, Chenzhou, 423000, China
| | - Hua-Long She
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, 613 Huangpu Street, Guangzhou, 510630, China
- Department of Radiology, Affiliated Hospital of Xiangnan University (Clinical College), 25 Renmin West Road, Chenzhou, 423000, China
| | - Tao Wu
- Department of Radiology, Affiliated Hospital of Xiangnan University (Clinical College), 25 Renmin West Road, Chenzhou, 423000, China
| | - Fang Hu
- College of Medical Imaging and Medical Examination, Xiangnan University, 25 Renmin West Road, Chenzhou, 423000, China
| | - Tao Li
- College of Medical Imaging and Medical Examination, Xiangnan University, 25 Renmin West Road, Chenzhou, 423000, China.
| | - Liang-Ping Luo
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, 613 Huangpu Street, Guangzhou, 510630, China.
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22
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Zhang AD, Su XH, Wang YF, Shi GF, Han C, Zhang N. Predicting the effects of radiotherapy based on diffusion kurtosis imaging in a xenograft mouse model of esophageal carcinoma. Exp Ther Med 2021; 21:327. [PMID: 33732300 PMCID: PMC7903468 DOI: 10.3892/etm.2021.9758] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 11/20/2020] [Indexed: 02/06/2023] Open
Abstract
The aim of the present study was to assess the predictive value of diffusion kurtosis imaging (DKI) on the effects of radiotherapy in a xenograft model of esophageal cancer. A total of 40 tumor-bearing mice, established by injection of Eca-109 cells in nude mice, were used. The experimental group (n=24) received a single dose of 15 Gy (6 MV by X-ray), and the control group (n=16) did not receive any treatment. Tumor volume, apparent diffusion coefficient (ADC), mean kurtosis (MK) and mean diffusivity (MD) of the two groups were compared, and the expression of aquaporin (AQP) 3 and necrosis ratio at matched time points in xenografts were also observed. There was a significant difference between the two groups from the 7th day of radiotherapy onwards; the xenograft volume of the experimental group was significantly smaller compared with the control group (P<0.05). On the 3rd day, the ADC and MD of the experimental group was significantly higher compared with the control group, and MK was significantly lower compared with the control group (P<0.05). On the 3rd day, AQP3 expression in the experimental group was lower compared with the control group, and the proportion of necrotic cells was higher compared with the control group (P<0.05). Single large fraction dose radiotherapy inhibited the growth of a xenografted esophageal tumor. Changes in ADC, MK and MD were observed prior to morphological changes in the tumor. The change in AQP3 expression and necrosis ratio was in also agreement with the DKI parameters assessed. DKI may thus provide early predictive ability on the effect of radiotherapy in esophageal carcinoma.
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Affiliation(s)
- An-Du Zhang
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital/Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei 050011, P.R. China
| | - Xiao-Hua Su
- Department of Oncology, Hebei General Hospital, Shijiazhuang, Hebei 050011, P.R. China
| | - Yan-Fei Wang
- Department of CT and MRI, Hebei Medical University Fourth Affiliated Hospital/Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei 050011, P.R. China
| | - Gao-Feng Shi
- Department of CT and MRI, Hebei Medical University Fourth Affiliated Hospital/Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei 050011, P.R. China
| | - Chun Han
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital/Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei 050011, P.R. China
| | - Nan Zhang
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital/Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei 050011, P.R. China
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23
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Di Re AM, Sun Y, Sundaresan P, Hau E, Toh JWT, Gee H, Or M, Haworth A. MRI radiomics in the prediction of therapeutic response to neoadjuvant therapy for locoregionally advanced rectal cancer: a systematic review. Expert Rev Anticancer Ther 2021; 21:425-449. [PMID: 33289435 DOI: 10.1080/14737140.2021.1860762] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Introduction: The standard of care for locoregionally advanced rectal cancer is neoadjuvant therapy (NA CRT) prior to surgery, of which 10-30% experience a complete pathologic response (pCR). There has been interest in using imaging features, also known as radiomics features, to predict pCR and potentially avoid surgery. This systematic review aims to describe the spectrum of MRI studies examining high-performing radiomic features that predict NA CRT response.Areas covered: This article reviews the use of pre-therapy MRI in predicting NA CRT response for patients with locoregionally advanced rectal cancer (T3/T4 and/or N1+). The primary outcome was to identify MRI radiomic studies; secondary outcomes included the power and the frequency of use of radiomic features.Expert opinion: Advanced models incorporating multiple radiomics categories appear to be the most promising. However, there is a need for standardization across studies with regards to; the definition of NA CRT response, imaging protocols, and radiomics features incorporated. Further studies are needed to validate current radiomics models and to fully ascertain the value of MRI radiomics in the response prediction for locoregionally advanced rectal cancer.
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Affiliation(s)
- Angelina Marina Di Re
- Colorectal Department, Westmead Hospital, Cnr Hawkesbury, Westmead, NSW.,School of Physics, University of Sydney, Camperdown, NSW, Australia
| | - Yu Sun
- School of Physics, University of Sydney, Camperdown, NSW, Australia
| | - Purnima Sundaresan
- Radiation Oncology Network, Western Sydney Local Health District, Cnr Hawkesbury, Westmead, NSW, Australia.,Sydney Medical School, University of Sydney, Camperdown, NSW, Australia
| | - Eric Hau
- Radiation Oncology Network, Western Sydney Local Health District, Cnr Hawkesbury, Westmead, NSW, Australia.,Sydney Medical School, University of Sydney, Camperdown, NSW, Australia.,Centre for Cancer Research, Westmead Institute of Medical Research, Westmead, NSW, Australia
| | - James Wei Tatt Toh
- Colorectal Department, Westmead Hospital, Cnr Hawkesbury, Westmead, NSW.,Sydney Medical School, University of Sydney, Camperdown, NSW, Australia.,Centre for Cancer Research, Westmead Institute of Medical Research, Westmead, NSW, Australia
| | - Harriet Gee
- Radiation Oncology Network, Western Sydney Local Health District, Cnr Hawkesbury, Westmead, NSW, Australia.,Sydney Medical School, University of Sydney, Camperdown, NSW, Australia
| | - Michelle Or
- Radiation Oncology Network, Western Sydney Local Health District, Cnr Hawkesbury, Westmead, NSW, Australia
| | - Annette Haworth
- School of Physics, University of Sydney, Camperdown, NSW, Australia
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24
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Yang L, Xia C, Zhao J, Zhou X, Wu B. The value of intravoxel incoherent motion and diffusion kurtosis imaging in the assessment of tumor regression grade and T stages after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer. Eur J Radiol 2020; 136:109504. [PMID: 33421885 DOI: 10.1016/j.ejrad.2020.109504] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 12/09/2020] [Accepted: 12/20/2020] [Indexed: 02/08/2023]
Abstract
PURPOSE To evaluate the role of IVIM and diffusion kurtosis imaging (DKI) in identifying pathologic complete response (pCR) and T stages after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). METHOD Forty-two patients with biopsy-proven rectal adenocarcinoma, who underwent both pre-and post-CRT MRI with IVIM and DKI sequences on a 3 T scanner, were enrolled prospectively. According to the pathologic ypTNM stages and tumor regression grade (TRG), patients were grouped into pCR (TRG0) and non-pCR (TRG1-3) groups and low T stage (ypT0-2) and high T stage (ypT3-4) groups. IVIM parameters (the slow diffusion coefficient [D], fast diffusion coefficient [D*], perfusion fraction [f]), DKI parameters (mean diffusivity [MD] and mean kurtosis [MK]), and mono-exponential ADC were calculated and analyzed between groups. RESULTS The pCR group had significantly higher post-CRT ADC, D*, f, and MD values than non-pCR group, and higher percent changes in the ADC, f, and MD values (all P < 0.05). The post-CRT MD values yielded the highest AUC (0.788) with higher sensitivity than post-ADC values (82.9 % vs. 77.1 %, respectively). Post-CRT ADC and MD values and the percent changes in the ADC and MD values were also negatively correlated with TRG (all P < 0.05). Besides, negative correlations were found among the pre-CRT MD, post-CRT ADC, D, f, and MD values and the ypT stages (all P < 0.05). CONCLUSIONS Both IVIM and DKI parameters could provide more information when evaluating pCR and T stages after nCRT. In particular, the diagnostic performance of the MD values was more valuable than ADC values in being able to determine pCR.
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Affiliation(s)
- Lanqing Yang
- From the Departments of Radiology, West China Hospital, Sichuan University, Guoxue Xiang No. 37, Chengdu, Sichuan, 610041, PR China
| | - Chunchao Xia
- From the Departments of Radiology, West China Hospital, Sichuan University, Guoxue Xiang No. 37, Chengdu, Sichuan, 610041, PR China
| | - Jin Zhao
- From the Departments of Radiology, West China Hospital, Sichuan University, Guoxue Xiang No. 37, Chengdu, Sichuan, 610041, PR China
| | - Xiaoyue Zhou
- MR Collaboration, Siemens Healthcare Ltd., Shanghai, PR China
| | - Bing Wu
- From the Departments of Radiology, West China Hospital, Sichuan University, Guoxue Xiang No. 37, Chengdu, Sichuan, 610041, PR China.
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25
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Haak HE, Maas M, Trebeschi S, Beets-Tan RGH. Modern MR Imaging Technology in Rectal Cancer; There Is More Than Meets the Eye. Front Oncol 2020; 10:537532. [PMID: 33117678 PMCID: PMC7578261 DOI: 10.3389/fonc.2020.537532] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 09/02/2020] [Indexed: 12/29/2022] Open
Abstract
MR imaging (MRI) is now part of the standard work up of patients with rectal cancer. Restaging MRI has been traditionally used to plan the surgical approach. Its role has recently increased and been adopted as a valuable tool to assist the clinical selection of clinical (near) complete responders for organ preserving treatment. Recently several studies have addressed new imaging biomarkers that combined with morphological provides a comprehensive picture of the tumor. Diffusion-weighted MRI (DWI) has entered the clinics and proven useful for response assessment after chemoradiotherapy. Other functional (quantitative) MRI technologies are on the horizon including artificial intelligence modeling. This narrative review provides an overview of recent advances in rectal cancer (re)staging by imaging with a specific focus on response prediction and evaluation of neoadjuvant treatment response. Furthermore, directions are given for future research.
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Affiliation(s)
- Hester E Haak
- Department of Radiology, Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, Netherlands.,Department of Surgery, Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Monique Maas
- Department of Radiology, Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, Netherlands
| | - Stefano Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands.,Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
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26
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Palmisano A, Di Chiara A, Esposito A, Rancoita PMV, Fiorino C, Passoni P, Albarello L, Rosati R, Del Maschio A, De Cobelli F. MRI prediction of pathological response in locally advanced rectal cancer: when apparent diffusion coefficient radiomics meets conventional volumetry. Clin Radiol 2020; 75:798.e1-798.e11. [PMID: 32712007 DOI: 10.1016/j.crad.2020.06.023] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 06/17/2020] [Indexed: 12/16/2022]
Abstract
AIM To investigate the role of diffusion-weighted imaging (DWI), T2-weighted (W) imaging, and apparent diffusion coefficient (ADC) histogram analysis before, during, and after neoadjuvant chemoradiotherapy (CRT) in the prediction of pathological response in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS Magnetic resonance imaging (MRI) at 1.5 T was performed in 43 patients with LARC before, during, and after CRT. Tumour volume was measured on both T2-weighted (VT2W) and on DWI at b=1,000 images (Vb,1,000) at each time point, hence the tumour volume reduction rate (ΔVT2W and ΔVb,1,000) was calculated. Whole-lesion (three-dimensional [3D]) first-order texture analysis of the ADC map was performed. Imaging parameters were compared to the pathological tumour regression grade (TRG). The diagnostic performance of each parameter in the identification of complete responders (CR; TRG4), partial responders (PR; TRG3) and non-responders (NR; TRG0-2) was evaluated by multinomial regression analysis and receiver operating characteristics curves. RESULTS After surgery, 11 patients were CR, 22 PR, and 10 NR. Before CRT, predictions of CR resulted in an ADC value of the 75th percentile and median, with good accuracy (74% and 86%, respectively) and sensitivity (73% and 82%, respectively). During CRT, the best predictor of CR was ΔVT2W (-58.3%) with good accuracy (81%) and excellent sensitivity (91%). After CRT, the best predictors of CR were ΔVT2W (-82.8%) and ΔVb, 1,000 (-86.8%), with 84% accuracy in both cases and 82% and 91% sensitivity, respectively. CONCLUSIONS The median ADC value at pre-treatment MRI and ΔVT2W (from pre-to-during CRT MRI) may have a role in early and accurate prediction of response to treatment. Both ΔVT2W and ΔVb,1,000 (from pre-to-post CRT) can help in the identification of CR after CRT.
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Affiliation(s)
- A Palmisano
- Unit of Clinical Research in Radiology, Experimental Imaging Center, IRCCS Ospedale San Raffaele, Milano, Italy.
| | - A Di Chiara
- Unit of Clinical Research in Radiology, Experimental Imaging Center, IRCCS Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
| | - A Esposito
- Unit of Clinical Research in Radiology, Experimental Imaging Center, IRCCS Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
| | - P M V Rancoita
- University Centre of Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milan, Italy
| | - C Fiorino
- Medical Physics, San Raffaele Hospital, Milano, Italy
| | - P Passoni
- Unit of Radiotherapy, IRCCS Ospedale San Raffaele, Milano, Italy
| | - L Albarello
- Department of Pathology, IRCCS Ospedale San Raffaele, Milano, Italy
| | - R Rosati
- Vita-Salute San Raffaele University, Milano, Italy; Department of Gastrointestinal Surgery, San Raffaele Hospital, Milano, Italy
| | - A Del Maschio
- Unit of Clinical Research in Radiology, Experimental Imaging Center, IRCCS Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
| | - F De Cobelli
- Unit of Clinical Research in Radiology, Experimental Imaging Center, IRCCS Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
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27
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Roberts TA, Hyare H, Agliardi G, Hipwell B, d'Esposito A, Ianus A, Breen-Norris JO, Ramasawmy R, Taylor V, Atkinson D, Punwani S, Lythgoe MF, Siow B, Brandner S, Rees J, Panagiotaki E, Alexander DC, Walker-Samuel S. Noninvasive diffusion magnetic resonance imaging of brain tumour cell size for the early detection of therapeutic response. Sci Rep 2020; 10:9223. [PMID: 32514049 PMCID: PMC7280197 DOI: 10.1038/s41598-020-65956-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 05/07/2020] [Indexed: 01/09/2023] Open
Abstract
Cancer cells differ in size from those of their host tissue and are known to change in size during the processes of cell death. A noninvasive method for monitoring cell size would be highly advantageous as a potential biomarker of malignancy and early therapeutic response. This need is particularly acute in brain tumours where biopsy is a highly invasive procedure. Here, diffusion MRI data were acquired in a GL261 glioma mouse model before and during treatment with Temozolomide. The biophysical model VERDICT (Vascular Extracellular and Restricted Diffusion for Cytometry in Tumours) was applied to the MRI data to quantify multi-compartmental parameters connected to the underlying tissue microstructure, which could potentially be useful clinical biomarkers. These parameters were compared to ADC and kurtosis diffusion models, and, measures from histology and optical projection tomography. MRI data was also acquired in patients to assess the feasibility of applying VERDICT in a range of different glioma subtypes. In the GL261 gliomas, cellular changes were detected according to the VERDICT model in advance of gross tumour volume changes as well as ADC and kurtosis models. VERDICT parameters in glioblastoma patients were most consistent with the GL261 mouse model, whilst displaying additional regions of localised tissue heterogeneity. The present VERDICT model was less appropriate for modelling more diffuse astrocytomas and oligodendrogliomas, but could be tuned to improve the representation of these tumour types. Biophysical modelling of the diffusion MRI signal permits monitoring of brain tumours without invasive intervention. VERDICT responds to microstructural changes induced by chemotherapy, is feasible within clinical scan times and could provide useful biomarkers of treatment response.
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Affiliation(s)
- Thomas A Roberts
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Harpreet Hyare
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
- Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK
| | - Giulia Agliardi
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Ben Hipwell
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Angela d'Esposito
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Andrada Ianus
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | | | - Rajiv Ramasawmy
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Valerie Taylor
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Mark F Lythgoe
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Bernard Siow
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | | | - Jeremy Rees
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Simon Walker-Samuel
- Centre for Advanced Biomedical Imaging, University College London, London, UK.
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28
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Koh DM. Using Deep Learning for MRI to Identify Responders to Chemoradiotherapy in Rectal Cancer. Radiology 2020; 296:65-66. [PMID: 32319859 DOI: 10.1148/radiol.2020200417] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Dow-Mu Koh
- From the Department of Radiology, Royal Marsden Hospital, Downs Road, Sutton SM2 5PT, England
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29
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Lambregts DMJ, Min LA, Schurink N, Beets-Tan RGH. Multiparametric Imaging for the Locoregional Follow-up of Rectal Cancer. CURRENT COLORECTAL CANCER REPORTS 2020. [DOI: 10.1007/s11888-020-00450-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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30
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Fusco R, Sansone M, Granata V, Grimm R, Pace U, Delrio P, Tatangelo F, Botti G, Avallone A, Pecori B, Petrillo A. Diffusion and perfusion MR parameters to assess preoperative short-course radiotherapy response in locally advanced rectal cancer: a comparative explorative study among Standardized Index of Shape by DCE-MRI, intravoxel incoherent motion- and diffusion kurtosis imaging-derived parameters. Abdom Radiol (NY) 2019; 44:3683-3700. [PMID: 30361867 DOI: 10.1007/s00261-018-1801-z] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
PURPOSE To assess preoperative short-course radiotherapy (SCR) tumor response in locally advanced rectal cancer (LARC) by means of Standardized Index of Shape (SIS) by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters derived from diffusion-weighted MRI (DW-MRI). MATERIALS AND METHODS Thirty-four patients with LARC who underwent MRI scans before and after SCR followed by delayed surgery, retrospectively, were enrolled. SIS, ADC, IVIM parameters [tissue diffusion (Dt), pseudo-diffusion (Dp), perfusion fraction (fp)] and DKI parameters [mean diffusivity (MD), mean of diffusional kurtosis (MK)] were calculated for each patient. IVIM parameters were estimated using two methods, namely conventional biexponential fitting (CBFM) and variable projection (VARPRO). After surgery, the pathological TNM and tumor regression grade (TRG) were estimated. For each parameter, percentage changes between before and after SCR were evaluated. Furthermore, an artificial neural network was trained for outcome prediction. Nonparametric sample tests and receiver operating characteristic curve (ROC) analysis were performed. RESULTS Fifteen patients were classified as responders (TRG ≤ 2) and 19 as not responders (TRG > 3). Seven patients had TRG 1 (pathological complete response, pCR). Mean and standard deviation values of pre-treatment CBFM Dp and mean value of VARPRO Dp pre-treatment showed statistically significant differences to predict pCR. (p value at Mann-Whitney test was 0.05, 0.03 and 0.008, respectively.) Exclusively SIS percentage change showed significant differences between responder and non-responder patients after SCR (p value << 0.001) and to assess pCR after SCR (p value << 0.001). The best results to predict pCR were obtained by VARPRO Fp mean value pre-treatment with area under ROC of 0.84, a sensitivity of 96.4%, a specificity of 71.4%, a positive predictive value (PPV) of 92.9%, a negative predictive value (NPV) of 83.3% and an accuracy of 91.2%. The best results to assess after treatment complete pathological response were obtained by SIS with an area under ROC of 0.89, a sensitivity of 85.7%, a specificity of 92.6%, a PPV of 75.0%, a NPV of 96.1% and an accuracy of 91.2%. Moreover, the best results to differentiate after treatment responders vs. non-responders were obtained by SIS with an area under ROC of 0.94, a sensitivity of 93.3%, a specificity of 84.2%, a PPV of 82.4%, a NPV of 94.1% and an accuracy of 88.2%. Promising initial results were obtained using a decision tree tested with all ADC, IVIM and DKI extracted parameter: we reached high accuracy to assess pathological complete response after SCR in LARC (an accuracy of 85.3% to assess pathological complete response after SCR using VARPRO Dp mean value post-treatment, ADC standard deviation value pre-treatment, MD standard deviation value post-treatment). CONCLUSION SIS is a hopeful DCE-MRI angiogenic biomarker to assess preoperative treatment response after SCR with delayed surgery. Furthermore, an important prognostic role was obtained by VARPRO Fp mean value pre-treatment and by a decision tree composed by diffusion parameters derived by DWI and DKI to assess pathological complete response.
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Affiliation(s)
- Roberta Fusco
- Division of Radiology, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, 80131, Naples, Italy.
| | - Mario Sansone
- Department of Electrical Engineering and Information Technologies (DIETI), Via Claudio 21, 80125, Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, 80131, Naples, Italy
| | | | - Ugo Pace
- Division of Gastrointestinal Surgical Oncology, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, 80131, Naples, Italy
| | - Paolo Delrio
- Division of Gastrointestinal Surgical Oncology, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, 80131, Naples, Italy
| | - Fabiana Tatangelo
- Division of Diagnostic Pathology, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, 80131, Naples, Italy
| | - Gerardo Botti
- Division of Diagnostic Pathology, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, 80131, Naples, Italy
| | - Antonio Avallone
- Division of Gastrointestinal Medical Oncology, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, 80131, Naples, Italy
| | - Biagio Pecori
- Division of Radiotherapy, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, 80131, Naples, Italy
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Bates DDB, Mazaheri Y, Lobaugh S, Golia Pernicka JS, Paroder V, Shia J, Zheng J, Capanu M, Petkovska I, Gollub MJ. Evaluation of diffusion kurtosis and diffusivity from baseline staging MRI as predictive biomarkers for response to neoadjuvant chemoradiation in locally advanced rectal cancer. Abdom Radiol (NY) 2019; 44:3701-3708. [PMID: 31154482 DOI: 10.1007/s00261-019-02073-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE To evaluate the role of diffusion kurtosis and diffusivity as potential imaging biomarkers to predict response to neoadjuvant chemoradiation therapy (CRT) from baseline staging magnetic resonance imaging (MRI) in locally advanced rectal cancer (LARC). MATERIALS AND METHODS This retrospective study included 45 consecutive patients (31 male/14 female) who underwent baseline MRI with high b-value sequences (up to 1500 mm/s2) for LARC followed by neoadjuvant chemoradiation and surgical resection. The mean age was 57.4 years (range 34.2-72.9). An abdominal radiologist using open source software manually segmented T2-weighted images. Segmentations were used to derive diffusion kurtosis and diffusivity from diffusion-weighted images as well as volumetric data. These data were analyzed with regard to tumor regression grade (TRG) using the four-tier American Joint Committee on Cancer (AJCC) classification, TRG 0-3. Proportional odds regression was used to analyze the four-level ordinal outcome. A sensitivity analysis was performed using univariable logistic regression for binary TRG groups, TRG 0/1 (> 90% response), or TRG 2/3 (< 90% response). p < 0.05 was considered significant throughout. RESULTS In the univariable proportional odds regression analysis, higher diffusivity summary (Dsum) values were observed to be significantly associated with higher odds of being in one or more favorable TRG group (TRG 0 or 1). In other words, on average, patients with higher Dsum values were more likely to be in a more favorable TRG group. These results are mostly consistent with the sensitivity analysis, in which higher values for most Dsum values [all but region of interest (ROI)-max D median (p = 0.08)] were observed to be significantly associated with higher odds of being TRG 0 or 1. Tumor volume of interest (VOI) and ROI volume, ROI kurtosis mean and median, and VOI kurtosis mean and median were not significantly associated with TRG. CONCLUSION Diffusivity derived from the baseline staging MRI, but not diffusion kurtosis or volumetric data, is associated with TRG and therefore shows promise as a potential imaging biomarker to predict the response to neoadjuvant chemotherapy in LARC. CLINICAL RELEVANCE STATEMENT Diffusivity shows promise as a potential imaging biomarker to predict AJCC TRG following neoadjuvant CRT, which has implications for risk stratification. Patients with TRG 0/1 have 5-year disease-free survival (DFS) of 90-98%, as opposed to those who are TRG 2/3 with 5-year DFS of 68-73%.
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Affiliation(s)
- David D B Bates
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
| | - Yousef Mazaheri
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Stephanie Lobaugh
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jennifer S Golia Pernicka
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Viktoriya Paroder
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Jinru Shia
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Junting Zheng
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marinela Capanu
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Iva Petkovska
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Marc J Gollub
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
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Guo R, Yang SH, Lu F, Han ZH, Yan X, Fu CX, Zhao ML, Lin J. Evaluation of intratumoral heterogeneity by using diffusion kurtosis imaging and stretched exponential diffusion-weighted imaging in an orthotopic hepatocellular carcinoma xenograft model. Quant Imaging Med Surg 2019; 9:1566-1578. [PMID: 31667142 DOI: 10.21037/qims.2019.08.18] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background To investigate the value of diffusion kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) with a stretched exponential model (SEM) in the evaluation of tumor heterogeneity in an orthotopic hepatocellular carcinoma (HCC) xenograft model. Methods Thirty orthotopic HCC xenograft nude mice models were established and randomly divided into two groups, the sorafenib induction group (n=15) and control group (n=15). Every mouse in each group underwent MRI with DKI and SEM on a 1.5T MR scanner at 7, 14, and 21 days after sorafenib intervention. DKI and SEM parameters including mean kurtosis (MK), mean diffusivity (MD), α, and distributed diffusion coefficient (DDC) were measured, calculated, and compared between the two groups and among different time points. Sequential correlations between histopathological results including necrotic fraction (NF), micro-vessel density (MVD), Ki-67 index, standard deviation (SD), and kurtosis from hematoxylin-eosin staining, and DKI and SEM parameters were analyzed. Results MK, MD, and DDC of HCC in the sorafenib induction group were significantly higher than those in the control group at each time point (P<0.05), while α was significantly lower (P<0.05). Significantly positive correlations were found between MK and NF (r=0.693, P=0.010), SD (r =0.785, P=0.003), kurtosis (r=0.779, P=0.003), between MD and NF (r=0.794, P=0.003), SD (r=0.629, P=0.020), kurtosis (r=0.645, P=0.018), and between DDC and NF (r=0.800, P=0.003), SD (r=0.636, P=0.020), kurtosis (r=0.664, P=0.016), and significantly negative correlations were observed between α and NF (r=-0.704, P=0.009), SD (r=-0.754, P=0.003), and kurtosis (r=-0.792, P=0.003) in the sorafenib induction group. Conclusions DKI and SEM parameters may be potentially useful for evaluating intratumoral heterogeneity in HCC.
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Affiliation(s)
- Ran Guo
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Shuo-Hui Yang
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200021, China
| | - Fang Lu
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200021, China
| | - Zhi-Hong Han
- Department of Pathology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200021, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthcare, Shanghai 201318, China
| | - Cai-Xia Fu
- Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen 518057, China
| | - Meng-Long Zhao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Jiang Lin
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,Shanghai Institute of Medical Imaging, Shanghai 200032, China
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Optimal b-values for diffusion kurtosis imaging in invasive ductal carcinoma versus ductal carcinoma in situ breast lesions. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:871-885. [DOI: 10.1007/s13246-019-00773-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 06/27/2019] [Indexed: 12/13/2022]
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Granata V, Fusco R, Reginelli A, Delrio P, Selvaggi F, Grassi R, Izzo F, Petrillo A. Diffusion kurtosis imaging in patients with locally advanced rectal cancer: current status and future perspectives. J Int Med Res 2019; 47:2351-2360. [PMID: 31032670 PMCID: PMC6567719 DOI: 10.1177/0300060519827168] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Morphological magnetic resonance imaging is currently the best imaging technique for local staging in patients with rectal cancer. However, morphological sequences have some limitations, especially after preoperative chemoradiotherapy (pCRT). Diffusion-weighted imaging has been applied to rectal cancer for detection of lesions, characterization of tissue, and evaluation of the response to therapy. In 2005, a non-Gaussian diffusion model called diffusion kurtosis imaging (DKI) was suggested. Several electronic databases were evaluated in the present review. The search included articles published from January 2000 to May 2018. The references of all articles were also evaluated. All titles and abstracts were assessed, and only the studies of DKI in patients with rectal cancer were retained. We identified 35 potentially relevant references through the electronic search. According to the inclusion and exclusion criteria, we retained five clinical studies that met the inclusion criteria. DKI is a useful tool for assessment of tumor aggressiveness, the nodal status, and the risk of early metastases as well as prediction of the response to pCRT. The results of DKI should be considered in treatment decision-making during the work-up of patients with rectal cancer.
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Affiliation(s)
- Vincenza Granata
- 1 Division of Radiology, Istituto Nazionale Tumori - IRCCS "Fondazione G. Pascale," Napoli, Italy
| | - Roberta Fusco
- 1 Division of Radiology, Istituto Nazionale Tumori - IRCCS "Fondazione G. Pascale," Napoli, Italy
| | - Alfonso Reginelli
- 2 Division of Radiology, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Paolo Delrio
- 3 Division of Gastrointestinal Surgical Oncology, Istituto Nazionale Tumori - IRCCS "Fondazione G. Pascale," Napoli, Italy
| | - Francesco Selvaggi
- 4 Division of Colorectal Surgery, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Roberto Grassi
- 2 Division of Radiology, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Francesco Izzo
- 5 Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori - IRCCS "Fondazione G. Pascale," Napoli, Italy
| | - Antonella Petrillo
- 1 Division of Radiology, Istituto Nazionale Tumori - IRCCS "Fondazione G. Pascale," Napoli, Italy
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35
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Cao L, Chen J, Duan T, Wang M, Jiang H, Wei Y, Xia C, Zhou X, Yan X, Song B. Diffusion kurtosis imaging (DKI) of hepatocellular carcinoma: correlation with microvascular invasion and histologic grade. Quant Imaging Med Surg 2019; 9:590-602. [PMID: 31143650 PMCID: PMC6511714 DOI: 10.21037/qims.2019.02.14] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 02/25/2019] [Indexed: 02/05/2023]
Abstract
BACKGROUND The aim of this study was to prospectively evaluate the diagnostic efficacy of diffusion kurtosis imaging (DKI) in predicting microvascular invasion (MVI) and histologic grade of hepatocellular carcinoma (HCC) with comparison to the conventional diffusion-weighted imaging (DWI). METHODS This prospective study was approved by the Institutional Review Board, and written informed consent was obtained from all patients. From September 2015 to January 2017, 74 consecutive HCC patients were enrolled in this study. Preoperative magnetic resonance imaging including DKI protocol was performed, and patients were followed up for at least one year after surgery. Diffusion parameters including the mean corrected apparent diffusion coefficient (MD), mean apparent kurtosis coefficient (MK), and apparent diffusion coefficient (ADC) were calculated. Differences of diffusion parameters among different histopathological groups were compared. For parameters that were significantly different between pathological groups, receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic efficiency for identifying MVI and predicting high-grade HCC. Univariate and multivariate logistic regression analyses were used to evaluate the relative value of clinical and laboratory variables and diffusion parameters as risk factors for early recurrence (≤1 year). RESULTS Among all the studied diffusion parameters, only MK differed significantly between the MVI-positive and MVI-negative group (0.91±0.10 vs. 0.82±0.09, P<0.001), and showed moderate diagnostic efficacy (AUC =0.77) for identifying MVI. High-grade HCCs showed significantly higher MK values (0.93±0.10 vs. 0.82±0.09, P<0.001), along with MD (1.34±0.18 vs. 1.54±0.22, P<0.001) and ADC values (1.17±0.15 vs. 1.30±0.16, P=0.001) than low-grade HCCs. For differentiating high-grade from low-grade HCCs, MK demonstrated a higher area under the ROC curve (AUC) and significantly higher specificity than MD and ADC (AUC =0.81 vs. 0.76 and 0.74; specificity =82.2% vs. 60.0% and 60.0%, P=0.02). In addition, higher MK (OR =5.700, P=0.002) and Barcelona Clinic Liver Cancer (BCLC) stage C (OR =6.329, P=0.005) were independent risk factors for early HCC recurrence. CONCLUSIONS DKI-derived MK values outperformed conventional ADC values for predicting MVI and histologic grade of HCC, and are associated with increased risk of early tumor recurrence.
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Affiliation(s)
- Likun Cao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Jie Chen
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Ting Duan
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Min Wang
- Department of Radiology, Inner Mongolia People’s Hospital, Hohhot 010017, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yi Wei
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | | | - Xu Yan
- Siemens Healthcare Ltd., Shanghai 201318, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
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Schurink NW, Lambregts DMJ, Beets-Tan RGH. Diffusion-weighted imaging in rectal cancer: current applications and future perspectives. Br J Radiol 2019; 92:20180655. [PMID: 30433814 DOI: 10.1259/bjr.20180655] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
This review summarizes current applications and clinical utility of diffusion-weighted imaging (DWI) for rectal cancer and in addition provides a brief overview of more recent developments (including intravoxel incoherent motion imaging, diffusion kurtosis imaging, and novel postprocessing tools) that are still in more early stages of research. More than 140 papers have been published in the last decade, during which period the use of DWI have slowly moved from mainly qualitative (visual) image interpretation to increasingly advanced methods of quantitative analysis. So far, the largest body of evidence exists for assessment of tumour response to neoadjuvant treatment. In this setting, particularly the benefit of DWI for visual assessment of residual tumour in post-radiation fibrosis has been established and is now increasingly adopted in clinics. Quantitative DWI analysis (mainly the apparent diffusion coefficient) has potential, both for response prediction as well as for tumour prognostication, but protocols require standardization and results need to be prospectively confirmed on larger scale. The role of DWI for further clinical tumour and nodal staging is less well-defined, although there could be a benefit for DWI to help detect lymph nodes. Novel methods of DWI analysis and post-processing are still being developed and optimized; the clinical potential of these tools remains to be established in the upcoming years.
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Affiliation(s)
- Niels W Schurink
- 1 Radiology, Netherlands Cancer Institute , Amsterdam , The Netherlands.,2 GROW School for Oncology and Developmental Biology , Maastricht , The Netherlands
| | | | - Regina G H Beets-Tan
- 1 Radiology, Netherlands Cancer Institute , Amsterdam , The Netherlands.,2 GROW School for Oncology and Developmental Biology , Maastricht , The Netherlands
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37
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Olsson LE, Johansson M, Zackrisson B, Blomqvist LK. Basic concepts and applications of functional magnetic resonance imaging for radiotherapy of prostate cancer. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2019; 9:50-57. [PMID: 33458425 PMCID: PMC7807726 DOI: 10.1016/j.phro.2019.02.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 12/27/2018] [Accepted: 02/08/2019] [Indexed: 12/30/2022]
Abstract
Recently, the interest to integrate magnetic resonance imaging (MRI) in radiotherapy for prostate cancer has increased considerably. MRI can contribute in all steps of the radiotherapy workflow from diagnosis, staging, and target definition to treatment follow-up. Of particular interest is the ability of MRI to provide a wide range of functional measures. The complexity of MRI as an imaging modality combined with the growing interest of the application to prostate cancer radiotherapy, emphasize the need for dedicated education within the radiation oncology community. In this context, an overview of the most common as well as a few upcoming functional MR imaging techniques is presented: the basic methodology and measurement is described, the link between the functional measures and the underlying biology is established, and finally relevant applications of functional MRI useful for prostate cancer radiotherapy are given.
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Affiliation(s)
- Lars E Olsson
- Department of Medical Radiation Physics, Translational Medicine, Lund University, Sweden.,Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Sweden
| | | | | | - Lennart K Blomqvist
- Department of Radiology, Molecular Medicine and Surgery, Karolinska University, Sweden
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Differentiation between malignant and benign musculoskeletal tumors using diffusion kurtosis imaging. Skeletal Radiol 2019; 48:285-292. [PMID: 29740660 DOI: 10.1007/s00256-018-2946-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 03/20/2018] [Accepted: 04/03/2018] [Indexed: 02/02/2023]
Abstract
OBJECTIVE The purpose of this study was to evaluate differences in parameters of diffusion kurtosis imaging (DKI) and minimum apparent diffusion coefficient (ADCmin) between benign and malignant musculoskeletal tumors. MATERIALS AND METHODS In this prospective study, 43 patients were scanned using a DKI protocol on a 3-T MR scanner. Eligibility criteria were: non-fatty, non-cystic soft tissue or osteolytic tumors; > 2 cm; location in the retroperitoneum, pelvis, leg, or neck; and no prior treatment. They were clinically or histologically diagnosed as benign (n = 27) or malignant (n = 16). In the DKI protocol, diffusion-weighted imaging was performed using four b values (0-2000 s/mm2) and 21 diffusion directions. Mean kurtosis (MK) values were calculated on the MR console. A recently developed software application enabling reliable calculation was used for DKI analysis. RESULTS MK showed a strong correction with ADCmin (Spearman's rs = 0.95). Both MK and ADCmin values differed between benign and malignant tumors (p < 0.01). For benign and malignant tumors, the mean MK values (± SD) were 0.49 ± 0.17 and 1.14 ± 0.30, respectively, and ADCmin values were 1.54 ± 0.47 and 0.49 ± 0.17 × 10-3 mm2/s, respectively. At cutoffs of MK = 0.81 and ADCmin = 0.77 × 10-3 mm2/s, the specificity and sensitivity for diagnosis of malignant tumors were 96.3 and 93.8% for MK and 96.3 and 93.8% for ADCmin, respectively. The areas under the curve were 0.97 and 0.99 for MK and ADCmin, respectively (p = 0.31). CONCLUSIONS MK and ADCmin showed high diagnostic accuracy and strong correlation, reflecting the accuracy of MK. However, no clear added value of DKI could be demonstrated in differentiating musculoskeletal tumors.
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Granata V, Fusco R, Setola SV, Palaia R, Albino V, Piccirillo M, Grimm R, Petrillo A, Izzo F. Diffusion kurtosis imaging and conventional diffusion weighted imaging to assess electrochemotherapy response in locally advanced pancreatic cancer. Radiol Oncol 2019; 53:15-24. [PMID: 30681974 PMCID: PMC6411027 DOI: 10.2478/raon-2019-0004] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 11/18/2018] [Indexed: 02/06/2023] Open
Abstract
Background The aim of the study was to evaluate diagnostic performance of functional parameters derived by conventional mono-exponential approach of diffusion weighted imaging (DWI) and by diffusion kurtosis imaging (DKI) in the assessment of pancreatic tumours treated with electrochemotherapy (ECT). Patients and methods Twenty-one consecutive patients with locally advanced pancreatic adenocarcinoma subjected to ECT were enrolled in a clinical approved trial. Among twenty-one enrolled patients, 13/21 (61.9%) patients were subjected to MRI before and after ECT. DWI was performed with a 1.5 T scanner; a free breathing axial single shot echo planar DWI pulse sequence parameters were acquired using seven b value = 0, 50, 100, 150, 400, 800, 1000 s/mm2. Apparent diffusion coefficient by conventional mono-exponential approach and mean of diffusion coefficient (MD) and mean of diffusional kurtosis (MK) by DKI approach were derived from DWI. Receiver operating characteristic (ROC) analysis was performed and sensitivity, specificity, positive and negative predictive value were calculated. Results Among investigated diffusion parameters, only the MD derived by DKI showed a significant variation of values between pre and post treatment (p = 0.02 at Wilcoxon test) and a significant statistically difference for percentage change between responders and not responders (p = 0.01 at Kruskal Wallis test). MD had a good diagnostic performance with a sensitivity of 80%, a specificity of 100% and area under ROC of 0.933. Conclusions MD derived by DKI allows identifying responders and not responders patients subject to ECT treatment. MD had higher diagnostic performance to assess ECT response compared to conventional DWI derived parameters.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Instituto Nazionale Tumori – IRCCS – Fondazione G. Pascale, Napoli, Italia
- Vincenza Granata, Division of Radiology, Instituto Nazionale Tumori – IRCCS – Fondazione G. Pascale, Napoli, Italia. Phone: +39 081 5903 714; Fax:+39 0815903825;
| | | | - Sergio Venanzio Setola
- Division of Radiology, Instituto Nazionale Tumori – IRCCS – Fondazione G. Pascale, Napoli, Italia
| | - Raffaele Palaia
- Division of Hepatobiliary Surgical Oncology, Unit, Instituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, Napoli, Italia
| | - Vittorio Albino
- Division of Hepatobiliary Surgical Oncology, Unit, Instituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, Napoli, Italia
| | - Mauro Piccirillo
- Division of Hepatobiliary Surgical Oncology, Unit, Instituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, Napoli, Italia
| | | | - Antonella Petrillo
- Division of Radiology, Instituto Nazionale Tumori – IRCCS – Fondazione G. Pascale, Napoli, Italia
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, Unit, Instituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, Napoli, Italia
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Meyer HJ, Höhn AK, Hamerla G, Surov A. Histogram parameters derived from T2 weighted images are associated with histopathological findings in rectal cancer - a preliminary study. Am J Transl Res 2018; 10:3790-3796. [PMID: 30662629 PMCID: PMC6291705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Accepted: 09/01/2018] [Indexed: 06/09/2023]
Abstract
Histogram analysis can better reflect tumor heterogeneity than conventional imaging analysis. The present study analyzed possible correlations between histogram parameters derived from T2 weighted images and histopathological features in rectal cancer. Seventeen patients with histopathological proven rectal adenocarcinoma were retrospectively acquired with prebioptic 3 T MRI and available histopathological specimens. Histogram analysis was performed using an in-house matlab tool conducting a whole lesion measurement. Histopathology was investigated using Ki67 specimens with calculation of Ki67-index as well as cellularity and nucleic areas and CD31 specimens, with estimation of microvessel density. Several histogram parameters correlated with average nucleic area. Skewness showed a moderate correlation with microvessel density (P = 0.54, P = 0.02). None of the parameters correlated with Ki67-index. Skewness derived from T2 weighted images might be used as a surrogate parameter for average nucleic area and microvessel density. However, none of the parameters were associated with proliferation index.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of LeipzigLeipzig 04103, Germany
| | - Anne Kathrin Höhn
- Department of Pathology, University of LeipzigLeipzig 04103, Germany
| | - Gordian Hamerla
- Department of Diagnostic and Interventional Radiology, University of LeipzigLeipzig 04103, Germany
| | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of LeipzigLeipzig 04103, Germany
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Zuo HD, Zhang XM. Could intravoxel incoherent motion diffusion-weighted magnetic resonance imaging be feasible and beneficial to the evaluation of gastrointestinal tumors histopathology and the therapeutic response? World J Radiol 2018; 10:116-123. [PMID: 30386496 PMCID: PMC6205843 DOI: 10.4329/wjr.v10.i10.116] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 08/02/2018] [Accepted: 08/26/2018] [Indexed: 02/06/2023] Open
Abstract
Gastrointestinal tumors (GTs) are among the most common tumors of the digestive system and are among the leading causes of cancer death worldwide. Functional magnetic resonance imaging (MRI) is crucial for assessment of histopathological changes and therapeutic responses of GTs before and after chemotherapy and radiotherapy. A new functional MRI technique, intravoxel incoherent motion (IVIM), could reveal more detailed useful information regarding many diseases. Currently, IVIM is widely used for various tumors because the derived parameters (diffusion coefficient, D; pseudo-perfusion diffusion coefficient, D*; and perfusion fraction, f) are thought to be important surrogate imaging biomarkers for gaining insights into tissue physiology. They can simultaneously reflect the microenvironment, microcirculation in the capillary network (perfusion) and diffusion in tumor tissues without contrast agent intravenous administration. The sensitivity and specificity of these parameters used in the evaluation of GTs vary, the results of IVIM in GTs are discrepant and the variability of IVIM measurements in response to chemotherapy and/or radiotherapy in these studies remains a source of controversy. Therefore, there are questions as to whether IVIM diffusion-weighted MRI is feasible and helpful in the evaluation of GTs, and whether it is worthy of expanded use.
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Affiliation(s)
- Hou-Dong Zuo
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
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Application of Diffusion Kurtosis Imaging and Histogram Analysis for Assessing Preoperative Stages of Rectal Cancer. Gastroenterol Res Pract 2018; 2018:9786932. [PMID: 29967642 PMCID: PMC6008759 DOI: 10.1155/2018/9786932] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 05/03/2018] [Indexed: 01/07/2023] Open
Abstract
Objective To explore the value of diffusion kurtosis imaging (DKI) and histogram analysis for assessing preoperative stages and heterogeneity in rectal cancer. Methods Fifty patients with pathologically confirmed rectal adenocarcinoma were enrolled. The value of DKI parameters and histogram metrics for assessing the preoperative stages and heterogeneity in rectal cancer was analyzed retrospectively. Results (1) ADC-10th percentile and ADC-25th percentile were significantly higher in T1-2 than in the T3-4 rectal cancer (the ADC values were 0.65 ± 0.08 × 10−3 mm2/s versus 0.58 ± 0.11 × 10−3 mm2/s and 0.73 ± 0.11 × 10−3 mm2/s versus 0.65 ± 0.11 × 10−3 mm2/s; p values were 0.035 and 0.024, resp.). (2) D-10th percentile and D-25th percentile were also significantly higher in T1-2 than in T3-4 rectal cancer (the D values were 0.96 ± 0.19 × 10−3 mm2/s versus 0.84 ± 0.16 × 10−3 mm2/s and 1.15 ± 0.27 × 10−3 mm2/s versus 0.99 ± 0.18 × 10−3 mm2/s; p values were 0.017 and 0.044, resp.). (3) K value and its histogram metrics showed no statistically significant difference between T1-2 and T3-4. (4) D-10th had the largest area under the curve (AUC 0.799) among all the parameters; the sensitivity and specificity were 84.2 and 61.3%, respectively. (5) DKI combined with traditional MRI had an accuracy of 68% while assessing the lymph node of rectal cancer. Conclusion DKI parameters and histogram metrics are rather valuable in assessing the preoperative stages of rectal cancer; D-10th percentile exhibits the highest diagnostic efficiency.
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Lee JW, Lee SM. Radiomics in Oncological PET/CT: Clinical Applications. Nucl Med Mol Imaging 2018; 52:170-189. [PMID: 29942396 PMCID: PMC5995782 DOI: 10.1007/s13139-017-0500-y] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 09/22/2017] [Accepted: 09/29/2017] [Indexed: 12/11/2022] Open
Abstract
18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used for staging, evaluating treatment response, and predicting prognosis in malignant diseases. FDG uptake and volumetric PET parameters such as metabolic tumor volume have been used and are still used as conventional PET parameters to assess biological characteristics of tumors. However, in recent years, additional features derived from PET images by computational processing have been found to reflect intratumoral heterogeneity, which is related to biological tumor features, and to provide additional predictive and prognostic information, which leads to the concept of radiomics. In this review, we focus on recent clinical studies of malignant diseases that investigated intratumoral heterogeneity on PET/CT, and we discuss its clinical role in various cancers.
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Affiliation(s)
- Jeong Won Lee
- Department of Nuclear Medicine, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, 25, Simgok-ro 100 Gil 25, Seo-gu, Incheon, 22711 South Korea
- Institute for Integrative Medicine, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, Incheon, South Korea
| | - Sang Mi Lee
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, South Korea
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Diffusion kurtosis imaging in the characterisation of rectal cancer: utilizing the most repeatable region-of-interest strategy for diffusion parameters on a 3T scanner. Eur Radiol 2018; 28:5211-5220. [DOI: 10.1007/s00330-018-5495-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Revised: 04/09/2018] [Accepted: 04/17/2018] [Indexed: 01/26/2023]
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Meyer HJ, Höhn A, Surov A. Histogram analysis of ADC in rectal cancer: associations with different histopathological findings including expression of EGFR, Hif1-alpha, VEGF, p53, PD1, and KI 67. A preliminary study. Oncotarget 2018; 9:18510-18517. [PMID: 29719621 PMCID: PMC5915088 DOI: 10.18632/oncotarget.24905] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 03/11/2018] [Indexed: 12/19/2022] Open
Abstract
Functional imaging modalities like Diffusion-weighted imaging are increasingly used to predict tumor behavior like cellularity and vascularity in different tumors. Histogram analysis is an emergent imaging analysis, in which every voxel is used to obtain a histogram and therefore statistically information about tumors can be provided. The purpose of this study was to elucidate possible associations between ADC histogram parameters and several immunhistochemical features in rectal cancer. Overall, 11 patients with histologically proven rectal cancer were included into the study. There were 2 (18.18%) females and 9 males with a mean age of 67.1 years. KI 67-index, expression of p53, EGFR, VEGF, and Hif1-alpha were semiautomatically estimated. The tumors were divided into PD1-positive and PD1-negative lesions. ADC histogram analysis was performed as a whole lesion measurement using an in-house matlab application. Spearman's correlation analysis revealed a strong correlation between EGFR expression and ADCmax (p=0.72, P=0.02). None of the vascular parameters (VEGF, Hif1-alpha) correlated with ADC parameters. Kurtosis and skewness correlated inversely with p53 expression (p=-0.64, P=0.03 and p=-0.81, P=0.002, respectively). ADCmedian and ADCmode correlated with Ki67 (p=-0.62, P=0.04 and p=-0.65, P=0.03, respectively). PD1-positive tumors showed statistically significant lower ADCmax values in comparison to PD1-negative tumors, 1.93 ± 0.36 vs 2.32 ± 0.47×10-3mm2/s, p=0.04. Several associations were identified between histogram parameter derived from ADC maps and EGFR, KI 67 and p53 expression in rectal cancer. Furthermore, ADCmax was different between PD1 positive and PD1 negative tumors indicating an important role of ADC parameters for possible future treatment prediction.
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Affiliation(s)
- Hans Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University Hospital of Leipzig, 04103 Leipzig, Germany
| | - Annekathrin Höhn
- Department of Pathology University Hospital of Leipzig, 04103 Leipzig, Germany
| | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University Hospital of Leipzig, 04103 Leipzig, Germany
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Gkika E, Oehlke O, Bunea H, Wiedenmann N, Adebahr S, Nestle U, Zamboglou C, Kirste S, Fennell J, Brunner T, Gainey M, Baltas D, Langer M, Urbach H, Bock M, Meyer PT, Grosu AL. Biological imaging for individualized therapy in radiation oncology: part II medical and clinical aspects. Future Oncol 2018. [DOI: 10.2217/fon-2017-0465] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Positron emission tomography and multiparametric MRI provide crucial information concerning tumor extent and normal tissue anatomy. Moreover, they are able to visualize biological characteristics of the tumor, which can be considered in the radiation treatment planning and monitoring. In this review we discuss the impact of biological imaging positron emission tomography and multiparametric MRI for radiation oncology, based on the data of the literature and on the experience of our own institution in this field.
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Affiliation(s)
- Eleni Gkika
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
| | - Oliver Oehlke
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
| | - Hatice Bunea
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
| | - Nicole Wiedenmann
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
| | - Sonja Adebahr
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
| | - Ursula Nestle
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
| | - Simon Kirste
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
| | - Jamina Fennell
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
| | - Thomas Brunner
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
| | - Mark Gainey
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
| | - Dimos Baltas
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
| | - Mathias Langer
- Department of Radiology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
| | - Michael Bock
- Department of Radiology – Medical Physics, Department of Radiology, Faculty of Medicine, Medical Center, University of Freiburg, D-79106, Germany
| | - Philipp T Meyer
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
- Department of Nuclear Medicine, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
| | - Anca-Ligia Grosu
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, D-79106, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, D-69120, Germany
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