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Yi N, Mo S, Zhang Y, Jiang Q, Wang Y, Huang C, Qin S, Jiang H. An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer. Sci Rep 2025; 15:3383. [PMID: 39870667 PMCID: PMC11772604 DOI: 10.1038/s41598-024-84749-7] [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: 08/27/2024] [Accepted: 12/26/2024] [Indexed: 01/29/2025] Open
Abstract
To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio. The least absolute shrinkage and selection operator algorithm was employed to reduce the dimensionality of deep learning (DL) features extracted from pre-standardized EUS images. The retained nonzero coefficient features were subsequently applied to develop predictive eight DL models based on distinct machine learning algorithms. The optimal DL model was identified and used to establish a clinical signature, which subsequently informed the construction and evaluation of a nomogram. Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) were implemented to interpret and visualize the model outputs. A total of 2048 DL features were initially extracted, from which only 27 features with coefficients greater than zero were retained. The support vector machine (SVM) DL model demonstrated exceptional performance, achieving area under the curve (AUC) values of 0.948 and 0.795 in the training and test groups, respectively. Additionally, a nomogram was developed, incorporating both DL and clinical signatures, and was visually represented for practical application. Finally, the calibration curves, decision curve analysis (DCA) plots, and clinical impact curves (CIC) exhibited by the DL model and nomogram indicated high accuracy. The application of Grad-CAM and SHAP enhanced the interpretability of these models. These methodologies contributed substantial net benefits to clinical decision-making processes. A novel interpretable DL model and nomogram were developed and validated using EUS images, cooperating with machine learning algorithms. This approach demonstrates significant potential for enhancing the clinical applicability of EUS in predicting PNETs from pancreatic cancer, thereby offering valuable insights for future research and implementation.
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Affiliation(s)
- Nan Yi
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Shuangyang Mo
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Yan Zhang
- The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Qi Jiang
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yingwei Wang
- Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Cheng Huang
- Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Shanyu Qin
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
| | - Haixing Jiang
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
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Mukund A, Afridi MA, Karolak A, Park MA, Permuth JB, Rasool G. Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence. Cancers (Basel) 2024; 16:2240. [PMID: 38927945 PMCID: PMC11201559 DOI: 10.3390/cancers16122240] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/03/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the most formidable challenges in oncology, characterized by its late detection and poor prognosis. Artificial intelligence (AI) and machine learning (ML) are emerging as pivotal tools in revolutionizing PDAC care across various dimensions. Consequently, many studies have focused on using AI to improve the standard of PDAC care. This review article attempts to consolidate the literature from the past five years to identify high-impact, novel, and meaningful studies focusing on their transformative potential in PDAC management. Our analysis spans a broad spectrum of applications, including but not limited to patient risk stratification, early detection, and prediction of treatment outcomes, thereby highlighting AI's potential role in enhancing the quality and precision of PDAC care. By categorizing the literature into discrete sections reflective of a patient's journey from screening and diagnosis through treatment and survivorship, this review offers a comprehensive examination of AI-driven methodologies in addressing the multifaceted challenges of PDAC. Each study is summarized by explaining the dataset, ML model, evaluation metrics, and impact the study has on improving PDAC-related outcomes. We also discuss prevailing obstacles and limitations inherent in the application of AI within the PDAC context, offering insightful perspectives on potential future directions and innovations.
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Affiliation(s)
- Ashwin Mukund
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (A.M.); (A.K.)
| | - Muhammad Ali Afridi
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan;
| | - Aleksandra Karolak
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (A.M.); (A.K.)
| | - Margaret A. Park
- Departments of Cancer Epidemiology and Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (M.A.P.); (J.B.P.)
| | - Jennifer B. Permuth
- Departments of Cancer Epidemiology and Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (M.A.P.); (J.B.P.)
| | - Ghulam Rasool
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (A.M.); (A.K.)
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Yan Q, Chen Y, Liu C, Shi H, Han M, Wu Z, Huang S, Zhang C, Hou B. Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis. Front Oncol 2024; 14:1332387. [PMID: 38725633 PMCID: PMC11080013 DOI: 10.3389/fonc.2024.1332387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 04/02/2024] [Indexed: 05/12/2024] Open
Abstract
Background Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients' prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis. Method A systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool. Results A total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%. Conclusion AI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications. Systematic Review Registration https://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.
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Affiliation(s)
- Qian Yan
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Yubin Chen
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Chunsheng Liu
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Hexian Shi
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Mingqian Han
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Zelong Wu
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Shanzhou Huang
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Chuanzhao Zhang
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Baohua Hou
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
- Department of General Surgery, Heyuan People’s Hospital, Heyuan, China
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Patel H, Zanos T, Hewitt DB. Deep Learning Applications in Pancreatic Cancer. Cancers (Basel) 2024; 16:436. [PMID: 38275877 PMCID: PMC10814475 DOI: 10.3390/cancers16020436] [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: 12/05/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
Pancreatic cancer is one of the most lethal gastrointestinal malignancies. Despite advances in cross-sectional imaging, chemotherapy, radiation therapy, and surgical techniques, the 5-year overall survival is only 12%. With the advent and rapid adoption of AI across all industries, we present a review of applications of DL in the care of patients diagnosed with PC. A review of different DL techniques with applications across diagnosis, management, and monitoring is presented across the different pathological subtypes of pancreatic cancer. This systematic review highlights AI as an emerging technology in the care of patients with pancreatic cancer.
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Affiliation(s)
- Hardik Patel
- Northwell Health—The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA;
| | - Theodoros Zanos
- Northwell Health—The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA;
| | - D. Brock Hewitt
- Department of Surgery, NYU Grossman School of Medicine, New York, NY 10016, USA;
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Ahn JC, Shah VH. Artificial intelligence in gastroenterology and hepatology. ARTIFICIAL INTELLIGENCE IN CLINICAL PRACTICE 2024:443-464. [DOI: 10.1016/b978-0-443-15688-5.00016-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Dromain C, Pavel M, Ronot M, Schaefer N, Mandair D, Gueguen D, Cheng C, Dehaene O, Schutte K, Cahané D, Jégou S, Balazard F. Response heterogeneity as a new biomarker of treatment response in patients with neuroendocrine tumors. Future Oncol 2023; 19:2171-2183. [PMID: 37497626 DOI: 10.2217/fon-2022-1137] [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] [Indexed: 07/28/2023] Open
Abstract
Aim: The RAISE project aimed to find a surrogate end point to predict treatment response early in patients with enteropancreatic neuroendocrine tumors (NET). Response heterogeneity, defined as the coexistence of responding and non-responding lesions, has been proposed as a predictive marker for progression-free survival (PFS) in patients with NETs. Patients & methods: Computerized tomography scans were analyzed from patients with multiple lesions in CLARINET (NCT00353496; n = 148/204). Cox regression analyses evaluated association between response heterogeneity, estimated using the standard deviation of the longest diameter ratio of target lesions, and NET progression. Results: Greater response heterogeneity at a given visit was associated with earlier progression thereafter: week 12 hazard ratio (HR; 95% confidence interval): 1.48 (1.20-1.82); p < 0.001; n = 148; week 36: 1.72 (1.32-2.24); p < 0.001; n = 108. HRs controlled for sum of longest diameter ratio: week 12: 1.28 (1.04-1.59); p = 0.020 and week 36: 1.81 (1.20-2.72); p = 0.005. Conclusion: Response heterogeneity independently predicts PFS in patients with enteropancreatic NETs. Further validation is required.
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Affiliation(s)
| | - Marianne Pavel
- Department of Medicine 1, Friedrich-Alexander-University of Erlangen-Nürnberg, Erlangen, Germany
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Feuerecker B, Heimer MM, Geyer T, Fabritius MP, Gu S, Schachtner B, Beyer L, Ricke J, Gatidis S, Ingrisch M, Cyran CC. Artificial Intelligence in Oncological Hybrid Imaging. Nuklearmedizin 2023; 62:296-305. [PMID: 37802057 DOI: 10.1055/a-2157-6810] [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: 10/08/2023]
Abstract
BACKGROUND Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes. METHODS AND RESULTS The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations. CONCLUSION AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation. KEY POINTS · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..
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Affiliation(s)
- Benedikt Feuerecker
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- German Cancer Research Center (DKFZ), Partner site Munich, DKTK German Cancer Consortium, Munich, Germany
| | - Maurice M Heimer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Thomas Geyer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | | | - Sijing Gu
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | | | - Leonie Beyer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Sergios Gatidis
- Department of Radiology, University Hospital Tübingen, Tübingen, Germany
- MPI, Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Clemens C Cyran
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
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Pavel M, Dromain C, Ronot M, Schaefer N, Mandair D, Gueguen D, Elvira D, Jégou S, Balazard F, Dehaene O, Schutte K. The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors. Future Oncol 2023; 19:2185-2199. [PMID: 37497644 DOI: 10.2217/fon-2022-1136] [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] [Indexed: 07/28/2023] Open
Abstract
Aim: The RAISE project assessed whether deep learning could improve early progression-free survival (PFS) prediction in patients with neuroendocrine tumors. Patients & methods: Deep learning models extracted features from CT scans from patients in CLARINET (NCT00353496) (n = 138/204). A Cox model assessed PFS prediction when combining deep learning with the sum of longest diameter ratio (SLDr) and logarithmically transformed CgA concentration (logCgA), versus SLDr and logCgA alone. Results: Deep learning models extracted features other than lesion shape to predict PFS at week 72. No increase in performance was achieved with deep learning versus SLDr and logCgA models alone. Conclusion: Deep learning models extracted relevant features to predict PFS, but did not improve early prediction based on SLDr and logCgA.
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Affiliation(s)
- Marianne Pavel
- Department of Medicine 1, Friedrich-Alexander-University of Erlangen-Nürnberg, Erlangen, Germany
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Ramaekers M, Viviers CGA, Janssen BV, Hellström TAE, Ewals L, van der Wulp K, Nederend J, Jacobs I, Pluyter JR, Mavroeidis D, van der Sommen F, Besselink MG, Luyer MDP. Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions. J Clin Med 2023; 12:4209. [PMID: 37445243 PMCID: PMC10342462 DOI: 10.3390/jcm12134209] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/08/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023] Open
Abstract
Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.
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Affiliation(s)
- Mark Ramaekers
- Department of Surgery, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands;
| | - Christiaan G. A. Viviers
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; (C.G.A.V.); (T.A.E.H.); (F.v.d.S.)
| | - Boris V. Janssen
- Department of Surgery, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (B.V.J.); (M.G.B.)
- Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Terese A. E. Hellström
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; (C.G.A.V.); (T.A.E.H.); (F.v.d.S.)
| | - Lotte Ewals
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands; (L.E.); (K.v.d.W.); (J.N.)
| | - Kasper van der Wulp
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands; (L.E.); (K.v.d.W.); (J.N.)
| | - Joost Nederend
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands; (L.E.); (K.v.d.W.); (J.N.)
| | - Igor Jacobs
- Department of Hospital Services and Informatics, Philips Research, 5656 AE Eindhoven, The Netherlands;
| | - Jon R. Pluyter
- Department of Experience Design, Philips Design, 5656 AE Eindhoven, The Netherlands;
| | - Dimitrios Mavroeidis
- Department of Data Science, Philips Research, 5656 AE Eindhoven, The Netherlands;
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; (C.G.A.V.); (T.A.E.H.); (F.v.d.S.)
| | - Marc G. Besselink
- Department of Surgery, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (B.V.J.); (M.G.B.)
- Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Misha D. P. Luyer
- Department of Surgery, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands;
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Histogram array and convolutional neural network of DWI for differentiating pancreatic ductal adenocarcinomas from solid pseudopapillary neoplasms and neuroendocrine neoplasms. Clin Imaging 2023; 96:15-22. [PMID: 36736182 DOI: 10.1016/j.clinimag.2023.01.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/20/2022] [Accepted: 01/23/2023] [Indexed: 01/28/2023]
Abstract
PURPOSE This study aimed to investigate the diagnostic performance of the histogram array and convolutional neural network (CNN) based on diffusion-weighted imaging (DWI) with multiple b-values under magnetic resonance imaging (MRI) to distinguish pancreatic ductal adenocarcinomas (PDACs) from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (PNENs). METHODS This retrospective study consisted of patients diagnosed with PDACs (n = 132), PNENs (n = 45) and SPNs (n = 54). All patients underwent 3.0-T MRI including DWI with 10 b values. The regions of interest (ROIs) of pancreatic tumor were manually drawn using ITK-SNAP software, which included entire tumor at DWI (b = 1500 s/m2). The histogram array was obtained through the ROIs from multiple b-value data. PyTorch (version 1.11) was used to construct a CNN classifier to categorize the histogram array into PDACs, PNENs or SPNs. RESULTS The area under the curves (AUCs) of the histogram array and the CNN model for differentiating PDACs from PNENs and SPNs were 0.896, 0.846, and 0.839 in the training, validation and testing cohorts, respectively. The accuracy, sensitivity and specificity were 90.22%, 96.23%, and 82.05% in the training cohort, 84.78%, 96.15%, and 70.0% in the validation cohort, and 81.72%, 90.57%, and 70.0% in the testing cohort. The performance of CNN with AUC of 0.865 for this differentiation was significantly higher than that of f with AUC = 0.755 (P = 0.0057) and α with AUC = 0.776 (P = 0.0278) in all patients. CONCLUSION The histogram array and CNN based on DWI data with multiple b-values using MRI provided an accurate diagnostic performance to differentiate PDACs from PNENs and SPNs.
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Jan Z, El Assadi F, Abd-Alrazaq A, Jithesh PV. Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review. J Med Internet Res 2023; 25:e44248. [PMID: 37000507 PMCID: PMC10131763 DOI: 10.2196/44248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/21/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer. OBJECTIVE This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature. METHODS A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms. CONCLUSIONS This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.
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Affiliation(s)
- Zainab Jan
- College of Health & Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Farah El Assadi
- College of Health & Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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Faur AC, Lazar DC, Ghenciu LA. Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis. World J Gastroenterol 2023; 29:1811-1823. [PMID: 37032728 PMCID: PMC10080704 DOI: 10.3748/wjg.v29.i12.1811] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/23/2022] [Accepted: 03/15/2023] [Indexed: 03/28/2023] Open
Abstract
Pancreatic cancer (PC) has a low incidence rate but a high mortality, with patients often in the advanced stage of the disease at the time of the first diagnosis. If detected, early neoplastic lesions are ideal for surgery, offering the best prognosis. Preneoplastic lesions of the pancreas include pancreatic intraepithelial neoplasia and mucinous cystic neoplasms, with intraductal papillary mucinous neoplasms being the most commonly diagnosed. Our study focused on predicting PC by identifying early signs using noninvasive techniques and artificial intelligence (AI). A systematic English literature search was conducted on the PubMed electronic database and other sources. We obtained a total of 97 studies on the subject of pancreatic neoplasms. The final number of articles included in our study was 44, 34 of which focused on the use of AI algorithms in the early diagnosis and prediction of pancreatic lesions. AI algorithms can facilitate diagnosis by analyzing massive amounts of data in a short period of time. Correlations can be made through AI algorithms by expanding image and electronic medical records databases, which can later be used as part of a screening program for the general population. AI-based screening models should involve a combination of biomarkers and medical and imaging data from different sources. This requires large numbers of resources, collaboration between medical practitioners, and investment in medical infrastructures.
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Affiliation(s)
- Alexandra Corina Faur
- Department of Anatomy and Embriology, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Timișoara 300041, Timiș, Romania
| | - Daniela Cornelia Lazar
- Department V of Internal Medicine I, Discipline of Internal Medicine IV, University of Medicine and Pharmacy “Victor Babes” Timișoara, Timișoara 300041, Timiș, Romania
| | - Laura Andreea Ghenciu
- Department III, Discipline of Pathophysiology, “Victor Babeș” University of Medicine and Pharmacy, Timișoara 300041, Timiș, Romania
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13
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Berbís MA, Paulano Godino F, Royuela del Val J, Alcalá Mata L, Luna A. Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver. World J Gastroenterol 2023; 29:1427-1445. [PMID: 36998424 PMCID: PMC10044858 DOI: 10.3748/wjg.v29.i9.1427] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 01/13/2023] [Accepted: 02/27/2023] [Indexed: 03/07/2023] Open
Abstract
Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’ workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.
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Affiliation(s)
- M Alvaro Berbís
- Department of Radiology, HT Médica, San Juan de Dios Hospital, Córdoba 14960, Spain
- Faculty of Medicine, Autonomous University of Madrid, Madrid 28049, Spain
| | | | | | - Lidia Alcalá Mata
- Department of Radiology, HT Médica, Clínica las Nieves, Jaén 23007, Spain
| | - Antonio Luna
- Department of Radiology, HT Médica, Clínica las Nieves, Jaén 23007, Spain
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14
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Feuerecker B, Heimer MM, Geyer T, Fabritius MP, Gu S, Schachtner B, Beyer L, Ricke J, Gatidis S, Ingrisch M, Cyran CC. Artificial Intelligence in Oncological Hybrid Imaging. ROFO-FORTSCHR RONTG 2023; 195:105-114. [PMID: 36170852 DOI: 10.1055/a-1909-7013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes. METHODS AND RESULTS The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations. CONCLUSION AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation. KEY POINTS · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making.. CITATION FORMAT · Feuerecker B, Heimer M, Geyer T et al. Artificial Intelligence in Oncological Hybrid Imaging. Fortschr Röntgenstr 2023; 195: 105 - 114.
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Affiliation(s)
- Benedikt Feuerecker
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Partner site Munich, DKTK German Cancer Consortium, Munich, Germany
| | - Maurice M Heimer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Thomas Geyer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | | | - Sijing Gu
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | | | - Leonie Beyer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Sergios Gatidis
- Department of Radiology, University Hospital Tübingen, Tübingen, Germany.,MPI, Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Clemens C Cyran
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
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15
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Dimitriadis A, Trivizakis E, Papanikolaou N, Tsiknakis M, Marias K. Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review. Insights Imaging 2022; 13:188. [PMID: 36503979 PMCID: PMC9742072 DOI: 10.1186/s13244-022-01315-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/24/2022] [Indexed: 12/14/2022] Open
Abstract
Contemporary deep learning-based decision systems are well-known for requiring high-volume datasets in order to produce generalized, reliable, and high-performing models. However, the collection of such datasets is challenging, requiring time-consuming processes involving also expert clinicians with limited time. In addition, data collection often raises ethical and legal issues and depends on costly and invasive procedures. Deep generative models such as generative adversarial networks and variational autoencoders can capture the underlying distribution of the examined data, allowing them to create new and unique instances of samples. This study aims to shed light on generative data augmentation techniques and corresponding best practices. Through in-depth investigation, we underline the limitations and potential methodology pitfalls from critical standpoint and aim to promote open science research by identifying publicly available open-source repositories and datasets.
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Affiliation(s)
- Avtantil Dimitriadis
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
| | - Eleftherios Trivizakis
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece
- Medical School, University of Crete, 71003 Heraklion, Greece
| | - Nikolaos Papanikolaou
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece
- Computational Clinical Imaging Group, Centre of the Unknown, Champalimaud Foundation, 1400-038 Lisbon, Portugal
- The Royal Marsden NHS Foundation Trust, THe Institute of Cancer Research, London, UK
| | - Manolis Tsiknakis
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
| | - Kostas Marias
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
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16
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Jan Z, El Assadi F, Abd-alrazaq A, Jithesh PV. Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review (Preprint).. [DOI: 10.2196/preprints.44248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
BACKGROUND
Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.
OBJECTIVE
This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.
METHODS
A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.
RESULTS
Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.
CONCLUSIONS
This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.
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17
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Development and validation of a combined nomogram model based on deep learning contrast-enhanced ultrasound and clinical factors to predict preoperative aggressiveness in pancreatic neuroendocrine neoplasms. Eur Radiol 2022; 32:7965-7975. [PMID: 35389050 DOI: 10.1007/s00330-022-08703-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/21/2022] [Accepted: 02/25/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVES This study aimed to develop and validate a combined nomogram model based on deep learning (DL) contrast-enhanced ultrasound (CEUS) and clinical factors to preoperatively predict the aggressiveness of pancreatic neuroendocrine neoplasms (PNENs). METHODS In this retrospective study, consecutive patients with histologically proven PNENs underwent CEUS examination at the initial work-up between January 2010 and October 2020. Patients were randomly allocated to the training and test sets. Typical sonographic and enhanced images of PNENs were selected to fine-tune the SE-ResNeXt-50 network. A combined nomogram model was developed by incorporating the DL predictive probability with clinical factors using multivariate logistic regression analysis. The utility of the proposed model was evaluated using receiver operator characteristic, calibration, and decision curve analysis. RESULTS A total of 104 patients were evaluated, including 80 (mean age ± standard deviation, 47 years ± 12; 56 males) in the training set and 24 (50 years ± 12; 14 males) in the test set. The DL model displayed effective image recognition with an AUC of 0.81 (95%CI: 0.62-1.00) in the test set. The combined nomogram model that incorporated independent clinical risk factors, such as tumor size, arterial enhancement level, and DL predictive probability, showed strong discrimination, with an AUC of 0.85 (95%CI: 0.69-1.00) in the test set with good calibration. Decision curve analysis verified the clinical usefulness of the combined nomogram. CONCLUSIONS The combined nomogram model could serve as a preoperative, noninvasive, and precise evaluation tool to differentiate aggressive and non-aggressive PNENs. KEY POINTS • Tumor size (odds ratio [OR], 1.58; p = 0.02), arterial enhancement level (OR, 0.04; p = 0.008), and deep learning predictive probability (OR, 288.46; p < 0.001) independently predicted aggressiveness of pancreatic neuroendocrine neoplasms preoperatively. • The combined model predicted aggressiveness better than the clinical model (AUC: 0.97 vs. 0.87, p = 0.009), achieving AUC values of 0.97 and 0.85 in the training set and the test set, respectively.
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18
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Staal FCR, Aalbersberg EA, van der Velden D, Wilthagen EA, Tesselaar MET, Beets-Tan RGH, Maas M. GEP-NET radiomics: a systematic review and radiomics quality score assessment. Eur Radiol 2022; 32:7278-7294. [PMID: 35882634 DOI: 10.1007/s00330-022-08996-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/25/2022] [Accepted: 06/26/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE The number of radiomics studies in gastroenteropancreatic neuroendocrine tumours (GEP-NETs) is rapidly increasing. This systematic review aims to provide an overview of the available evidence of radiomics for clinical outcome measures in GEP-NETs, to understand which applications hold the most promise and which areas lack evidence. METHODS PubMed, Embase, and Wiley/Cochrane Library databases were searched and a forward and backward reference check of the identified studies was executed. Inclusion criteria were (1) patients with GEP-NETs and (2) radiomics analysis on CT, MRI or PET. Two reviewers independently agreed on eligibility and assessed methodological quality with the radiomics quality score (RQS) and extracted outcome data. RESULTS In total, 1364 unique studies were identified and 45 were included for analysis. Most studies focused on GEP-NET grade and differential diagnosis of GEP-NETs from other neoplasms, while only a minority analysed treatment response or long-term outcomes. Several studies were able to predict tumour grade or to differentiate GEP-NETs from other lesions with a good performance (AUCs 0.74-0.96 and AUCs 0.80-0.99, respectively). Only one study developed a model to predict recurrence in pancreas NETs (AUC 0.77). The included studies reached a mean RQS of 18%. CONCLUSION Although radiomics for GEP-NETs is still a relatively new area, some promising models have been developed. Future research should focus on developing robust models for clinically relevant aims such as prediction of response or long-term outcome in GEP-NET, since evidence for these aims is still scarce. KEY POINTS • The majority of radiomics studies in gastroenteropancreatic neuroendocrine tumours is of low quality. • Most evidence for radiomics is available for the identification of tumour grade or differentiation of gastroenteropancreatic neuroendocrine tumours from other neoplasms. • Radiomics for the prediction of response or long-term outcome in gastroenteropancreatic neuroendocrine tumours warrants further research.
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Affiliation(s)
- Femke C R Staal
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands
| | - Else A Aalbersberg
- The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands.,Department of Nuclear Medicine, The Netherlands Cancer Institute Amsterdam, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Daphne van der Velden
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Erica A Wilthagen
- Scientific Information Service, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Margot E T Tesselaar
- The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands.,Department of Medical Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,Faculty of Health Sciences, University of Southern Denmark, J. B. Winsløws Vej 19, 3, 5000, Odense, Denmark
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
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19
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Ramachandran A, Madhusudhan KS. Advances in the imaging of gastroenteropancreatic neuroendocrine neoplasms. World J Gastroenterol 2022; 28:3008-3026. [PMID: 36051339 PMCID: PMC9331531 DOI: 10.3748/wjg.v28.i26.3008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/30/2021] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
Gastroenteropancreatic neuroendocrine neoplasms comprise a heterogeneous group of tumors that differ in their pathogenesis, hormonal syndromes produced, biological behavior and consequently, in their requirement for and/or response to specific chemotherapeutic agents and molecular targeted therapies. Various imaging techniques are available for functional and morphological evaluation of these neoplasms and the selection of investigations performed in each patient should be customized to the clinical question. Also, with the increased availability of cross sectional imaging, these neoplasms are increasingly being detected incidentally in routine radiology practice. This article is a review of the various imaging modalities currently used in the evaluation of neuroendocrine neoplasms, along with a discussion of the role of advanced imaging techniques and a glimpse into the newer imaging horizons, mostly in the research stage.
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Affiliation(s)
- Anupama Ramachandran
- Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Kumble Seetharama Madhusudhan
- Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi 110029, India
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20
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Liu C, Bian Y, Meng Y, Liu F, Cao K, Zhang H, Fang X, Li J, Yu J, Feng X, Ma C, Lu J, Xu J, Shao C. Preoperative Prediction of G1 and G2/3 Grades in Patients With Nonfunctional Pancreatic Neuroendocrine Tumors Using Multimodality Imaging. Acad Radiol 2022; 29:e49-e60. [PMID: 34175209 DOI: 10.1016/j.acra.2021.05.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/05/2021] [Accepted: 05/13/2021] [Indexed: 12/13/2022]
Abstract
OBJECTIVES We aimed to develop and validate a multimodality radiomics model for the preoperative prediction of nonfunctional pancreatic neuroendocrine tumor (NF-pNET) grade (G). METHODS This retrospective study assessed 123 patients with surgically resected, pathologically confirmed NF-pNETs who underwent multidetector computed tomography and MRI scans between December 2012 and May 2020. Radiomic features were extracted from multidetector computed tomography and MRI. Wilcoxon rank-sum test and Max-Relevance and Min-Redundancy tests were used to select the features. The linear discriminative analysis (LDA) was used to construct the four models including a clinical model, MRI radiomics model, computed tomography radiomics model, and mixed radiomics model. The performance of the models was assessed using a training cohort (82 patients) and a validation cohort (41 patients), and decision curve analysis was applied for clinical use. RESULTS We successfully constructed 4 models to predict the tumor grade of NF- pNETs. Model 4 combined 6 features of T2-weighted imaging radiomics features and 1 arterial-phase computed tomography radiomics feature, and showed better discrimination in the training cohort (AUC = 0.92) and validation cohort (AUC = 0.85) relative to the other models. In the decision curves, if the threshold probability was 0.07-0.87, the use of the radiomics score to distinguish NF-pNET G1 and G2/3 offered more benefit than did the use of a "treat all patients" or a "treat none" scheme in the training cohort of the MRI radiomics model. CONCLUSION The LDA classifier combining multimodality images may be a valuable noninvasive tool for distinguishing NF-pNET grades and avoid unnecessary surgery.
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21
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Pantelis AG, Panagopoulou PA, Lapatsanis DP. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms-A Scoping Review. Diagnostics (Basel) 2022; 12:874. [PMID: 35453922 PMCID: PMC9027316 DOI: 10.3390/diagnostics12040874] [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: 02/27/2022] [Revised: 03/27/2022] [Accepted: 03/29/2022] [Indexed: 12/21/2022] Open
Abstract
Neuroendocrine neoplasms (NENs) and tumors (NETs) are rare neoplasms that may affect any part of the gastrointestinal system. In this scoping review, we attempt to map existing evidence on the role of artificial intelligence, machine learning and deep learning in the diagnosis and management of NENs of the gastrointestinal system. After implementation of inclusion and exclusion criteria, we retrieved 44 studies with 53 outcome analyses. We then classified the papers according to the type of studied NET (26 Pan-NETs, 59.1%; 3 metastatic liver NETs (6.8%), 2 small intestinal NETs, 4.5%; colorectal, rectal, non-specified gastroenteropancreatic and non-specified gastrointestinal NETs had from 1 study each, 2.3%). The most frequently used AI algorithms were Supporting Vector Classification/Machine (14 analyses, 29.8%), Convolutional Neural Network and Random Forest (10 analyses each, 21.3%), Random Forest (9 analyses, 19.1%), Logistic Regression (8 analyses, 17.0%), and Decision Tree (6 analyses, 12.8%). There was high heterogeneity on the description of the prediction model, structure of datasets, and performance metrics, whereas the majority of studies did not report any external validation set. Future studies should aim at incorporating a uniform structure in accordance with existing guidelines for purposes of reproducibility and research quality, which are prerequisites for integration into clinical practice.
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Affiliation(s)
- Athanasios G. Pantelis
- 4th Department of Surgery, Evaggelismos General Hospital of Athens, 10676 Athens, Greece;
| | | | - Dimitris P. Lapatsanis
- 4th Department of Surgery, Evaggelismos General Hospital of Athens, 10676 Athens, Greece;
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22
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Preuss K, Thach N, Liang X, Baine M, Chen J, Zhang C, Du H, Yu H, Lin C, Hollingsworth MA, Zheng D. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Cancers (Basel) 2022; 14:cancers14071654. [PMID: 35406426 PMCID: PMC8997008 DOI: 10.3390/cancers14071654] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary With a five-year survival rate of only 3% for the majority of patients, pancreatic cancer is a global healthcare challenge. Radiomics and deep learning, two novel quantitative imaging methods that treat medical images as minable data instead of just pictures, have shown promise in advancing personalized management of pancreatic cancer through diagnosing precursor diseases, early detection, accurate diagnosis, and treatment personalization. Radiomics and deep learning methods aim to collect hidden information in medical images that is missed by conventional radiology practices through expanding the data search and comparing information across different patients. Both methods have been studied and applied in pancreatic cancer. In this review, we focus on the current progress of these two methods in pancreatic cancer and provide a comprehensive narrative review on the topic. With better regulation, enhanced workflow, and larger prospective patient datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through personalized precision medicine. Abstract As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.
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Affiliation(s)
- Kiersten Preuss
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Nutrition and Health Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA
| | - Nate Thach
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Justin Chen
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Naperville North High School, Naperville, IL 60563, USA
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Huijing Du
- Department of Mathematics, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Hongfeng Yu
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Chi Lin
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Michael A. Hollingsworth
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Dandan Zheng
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Radiation Oncology, University of Rochester, Rochester, NY 14626, USA
- Correspondence: ; Tel.: +1-(585)-276-3255
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23
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Abstract
Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.
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Affiliation(s)
| | - Ihab R Kamel
- Department of Imaging & Imaging Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Harrison X Bai
- Department of Imaging & Imaging Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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24
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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25
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Abstract
ABSTRACT Artificial intelligence is poised to revolutionize medical image. It takes advantage of the high-dimensional quantitative features present in medical images that may not be fully appreciated by humans. Artificial intelligence has the potential to facilitate automatic organ segmentation, disease detection and characterization, and prediction of disease recurrence. This article reviews the current status of artificial intelligence in liver imaging and reviews the opportunities and challenges in clinical implementation.
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26
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Han X, Yang J, Luo J, Chen P, Zhang Z, Alu A, Xiao Y, Ma X. Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods. Front Oncol 2021; 11:606677. [PMID: 34367940 PMCID: PMC8339967 DOI: 10.3389/fonc.2021.606677] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 07/05/2021] [Indexed: 02/05/2023] Open
Abstract
Objectives The purpose of this study aimed at investigating the reliability of radiomics features extracted from contrast-enhanced CT in differentiating pancreatic cystadenomas from pancreatic neuroendocrine tumors (PNETs) using machine-learning methods. Methods In this study, a total number of 120 patients, including 66 pancreatic cystadenomas patients and 54 PNETs patients were enrolled. Forty-eight radiomic features were extracted from contrast-enhanced CT images using LIFEx software. Five feature selection methods were adopted to determine the appropriate features for classifiers. Then, nine machine learning classifiers were employed to build predictive models. The performance of the forty-five models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score in the testing group. Results The predictive models exhibited reliable ability of differentiating pancreatic cystadenomas from PNETs when combined with suitable selection methods. A combination of DC as the selection method and RF as the classifier, as well as Xgboost+RF, demonstrated the best discriminative ability, with the highest AUC of 0.997 in the testing group. Conclusions Radiomics-based machine learning methods might be a noninvasive tool to assist in differentiating pancreatic cystadenomas and PNETs.
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Affiliation(s)
- Xuejiao Han
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Melanoma and Sarcoma Medical Oncology Unit, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jingwen Luo
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Pengan Chen
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Zilong Zhang
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Aqu Alu
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yinan Xiao
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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27
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Song C, Wang M, Luo Y, Chen J, Peng Z, Wang Y, Zhang H, Li ZP, Shen J, Huang B, Feng ST. Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:833. [PMID: 34164467 PMCID: PMC8184461 DOI: 10.21037/atm-21-25] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 03/21/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND To establish and validate a prediction model for pancreatic neuroendocrine neoplasms (pNENs) recurrence after radical surgery with preoperative computed tomography (CT) images. METHODS We retrospectively collected data from 74 patients with pathologically confirmed pNENs (internal group: 56 patients, Hospital I; external validation group: 18 patients, Hospital II). Using the internal group, models were trained with CT findings evaluated by radiologists, radiomics, and deep learning radiomics (DLR) to predict 5-year pNEN recurrence. Radiomics and DLR models were established for arterial (A), venous (V), and arterial and venous (A&V) contrast phases. The model with the optimal performance was further combined with clinical information, and all patients were divided into high- and low-risk groups to analyze survival with the Kaplan-Meier method. RESULTS In the internal group, the areas under the curves (AUCs) of DLR-A, DLR-V, and DLR-A&V models were 0.80, 0.58, and 0.72, respectively. The corresponding radiomics AUCs were 0.74, 0.68, and 0.70. The AUC of the CT findings model was 0.53. The DLR-A model represented the optimum; added clinical information improved the AUC from 0.80 to 0.83. In the validation group, the AUCs of DLR-A, DLR-V, and DLR-A&V models were 0.77, 0.48, and 0.64, respectively, and those of radiomics-A, radiomics-V, and radiomics-A&V models were 0.56, 0.52, and 0.56, respectively. The AUC of the CT findings model was 0.52. In the validation group, the comparison between the DLR-A and the random models showed a trend of significant difference (P=0.058). Recurrence-free survival differed significantly between high- and low-risk groups (P=0.003). CONCLUSIONS Using DLR, we successfully established a preoperative recurrence prediction model for pNEN patients after radical surgery. This allows a risk evaluation of pNEN recurrence, optimizing clinical decision-making.
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Affiliation(s)
- Chenyu Song
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Mingyu Wang
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yanji Luo
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jie Chen
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhenpeng Peng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yangdi Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Hongyuan Zhang
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Zi-Ping Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jingxian Shen
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
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28
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Laoveeravat P, Abhyankar PR, Brenner AR, Gabr MM, Habr FG, Atsawarungruangkit A. Artificial intelligence for pancreatic cancer detection: Recent development and future direction. Artif Intell Gastroenterol 2021; 2:56-68. [DOI: 10.35712/aig.v2.i2.56] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/31/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) has been increasingly utilized in medical applications, especially in the field of gastroenterology. AI can assist gastroenterologists in imaging-based testing and prediction of clinical diagnosis, for examples, detecting polyps during colonoscopy, identifying small bowel lesions using capsule endoscopy images, and predicting liver diseases based on clinical parameters. With its high mortality rate, pancreatic cancer can highly benefit from AI since the early detection of small lesion is difficult with conventional imaging techniques and current biomarkers. Endoscopic ultrasound (EUS) is a main diagnostic tool with high sensitivity for pancreatic adenocarcinoma and pancreatic cystic lesion. The standard tumor markers have not been effective for diagnosis. There have been recent research studies in AI application in EUS and novel biomarkers to early detect and differentiate malignant pancreatic lesions. The findings are impressive compared to the available traditional methods. Herein, we aim to explore the utility of AI in EUS and novel serum and cyst fluid biomarkers for pancreatic cancer detection.
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Affiliation(s)
- Passisd Laoveeravat
- Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Priya R Abhyankar
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Aaron R Brenner
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Moamen M Gabr
- Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Fadlallah G Habr
- Division of Gastroenterology, Warren Alpert Medical School of Brown University, Providence, RI 02903, United States
| | - Amporn Atsawarungruangkit
- Division of Gastroenterology, Warren Alpert Medical School of Brown University, Providence, RI 02903, United States
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29
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Bezzi C, Mapelli P, Presotto L, Neri I, Scifo P, Savi A, Bettinardi V, Partelli S, Gianolli L, Falconi M, Picchio M. Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance. Eur J Nucl Med Mol Imaging 2021; 48:4002-4015. [PMID: 33835220 DOI: 10.1007/s00259-021-05338-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/24/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided. METHODS Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction. RESULTS Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7-0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response. CONCLUSIONS Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients.
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Affiliation(s)
- C Bezzi
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy
| | - P Mapelli
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.,Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - L Presotto
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - I Neri
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - P Scifo
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - A Savi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - V Bettinardi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - S Partelli
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.,Pancreatic Surgery Unit, Pancreas Translational & Clinical Research Centre, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
| | - L Gianolli
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - M Falconi
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.,Pancreatic Surgery Unit, Pancreas Translational & Clinical Research Centre, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
| | - M Picchio
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy. .,Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.
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30
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Bian Y, Li J, Cao K, Fang X, Jiang H, Ma C, Jin G, Lu J, Wang L. Magnetic resonance imaging radiomic analysis can preoperatively predict G1 and G2/3 grades in patients with NF-pNETs. Abdom Radiol (NY) 2021; 46:667-680. [PMID: 32808056 DOI: 10.1007/s00261-020-02706-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 08/02/2020] [Accepted: 08/08/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE We aimed to explore the relationship between the magnetic resonance imaging (MRI) radiomic score (rad-score) and the grades of non-functioning pancreatic neuroendocrine tumors (NF-pNETs) and evaluate the potential of the calculated MRI rad-score to differentiate grade 1 from grade 2/3 NF-pNETs. METHODS This retrospective study assessed 157 patients with surgically resected, pathologically confirmed NF-pNETs who underwent magnetic resonance scans from November 2012 to December 2019. Radiomic features were extracted from arterial and portal venous MRI. The least absolute shrinkage and selection operator method were used to select the features. Multivariate logistic regression models were used to analyze the association between the MRI rad-score and NF-pNET grades. The MRI rad-score performance was assessed based on its discriminative ability and clinical usefulness. RESULTS The MRI rad-score, which consisted of seven selected features, was significantly associated with the NF-pNET grades. Every 1-point increase in the rad-score was associated with a 35% increased risk of grade 2/3 disease. The score also showed high accuracy (area under the curve = 0.775). The best cut-off point for maximal sensitivity and specificity was at 0.41. In the decision curves, when the threshold probability was higher than 0.3, the rad-score used in this study to distinguish grades 1 and 2/3 NF-pNETs offered more benefits than the use of a treat-all-patients or a treat-none scheme. CONCLUSIONS The MRI rad-score showed a significant association with the grades of NF-pNETs. Thus, it may be used as a valuable non-invasive tool for differential NF-pNET grading.
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Affiliation(s)
- Yun Bian
- Department of Radiology, Changhai Hospital, The Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, The Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Kai Cao
- Department of Radiology, Changhai Hospital, The Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, The Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Hui Jiang
- Department of Pathology, Changhai Hospital, The Navy Military Medical University, Shanghai, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital, The Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Gang Jin
- Department of Pancreatic Surgery, Changhai Hospital, The Navy Military Medical University, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, The Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Li Wang
- Department of Pathology, Changhai Hospital, The Navy Military Medical University, Shanghai, China.
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31
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Gorris M, Hoogenboom SA, Wallace MB, van Hooft JE. Artificial intelligence for the management of pancreatic diseases. Dig Endosc 2021; 33:231-241. [PMID: 33065754 PMCID: PMC7898901 DOI: 10.1111/den.13875] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/29/2020] [Accepted: 10/11/2020] [Indexed: 12/16/2022]
Abstract
Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine-learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.
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Affiliation(s)
- Myrte Gorris
- Department of Gastroenterology and HepatologyAmsterdam Gastroenterology Endocrinology MetabolismAmsterdam University Medical CentersUniversity of AmsterdamAmsterdamThe Netherlands
| | - Sanne A. Hoogenboom
- Department of Gastroenterology and HepatologyAmsterdam Gastroenterology Endocrinology MetabolismAmsterdam University Medical CentersUniversity of AmsterdamAmsterdamThe Netherlands
| | - Michael B. Wallace
- Department of Gastroenterology and HepatologyMayo Clinic JacksonvilleJacksonvilleUSA
| | - Jeanin E. van Hooft
- Department of Gastroenterology and HepatologyAmsterdam Gastroenterology Endocrinology MetabolismAmsterdam University Medical CentersUniversity of AmsterdamAmsterdamThe Netherlands
- Department of Gastroenterology and HepatologyLeiden University Medical CenterLeidenThe Netherlands
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32
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Mpilla GB, Philip PA, El-Rayes B, Azmi AS. Pancreatic neuroendocrine tumors: Therapeutic challenges and research limitations. World J Gastroenterol 2020; 26:4036-4054. [PMID: 32821069 PMCID: PMC7403797 DOI: 10.3748/wjg.v26.i28.4036] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 06/10/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023] Open
Abstract
Pancreatic neuroendocrine tumors (PNETs) are known to be the second most common epithelial malignancy of the pancreas. PNETs can be listed among the slowest growing as well as the fastest growing human cancers. The prevalence of PNETs is deceptively low; however, its incidence has significantly increased over the past decades. According to the American Cancer Society's estimate, about 4032 (> 7% of all pancreatic malignancies) individuals will be diagnosed with PNETs in 2020. PNETs often cause severe morbidity due to excessive secretion of hormones (such as serotonin) and/or overall tumor mass. Patients can live for many years (except for those patients with poorly differentiated G3 neuroendocrine tumors); thus, the prevalence of the tumors that is the number of patients actually dealing with the disease at any given time is fairly high because the survival is much longer than pancreatic ductal adenocarcinoma. Due to significant heterogeneity, the management of PNETs is very complex and remains an unmet clinical challenge. In terms of research studies, modest improvements have been made over the past decades in the identification of potential oncogenic drivers in order to enhance the quality of life and increase survival for this growing population of patients. Unfortunately, the majority of systematic therapies approved for the management of advanced stage PNETs lack objective response or at most result in modest benefits in survival. In this review, we aim to discuss the broad challenges associated with the management and the study of PNETs.
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Affiliation(s)
- Gabriel Benyomo Mpilla
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI 48201, United States
| | - Philip Agop Philip
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI 48201, United States
| | - Bassel El-Rayes
- Department of Hematology Oncology, Emory Winship Institute, Atlanta, GA 30322, United States
| | - Asfar Sohail Azmi
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI 48201, United States
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33
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Bian Y, Zhao Z, Jiang H, Fang X, Li J, Cao K, Ma C, Guo S, Wang L, Jin G, Lu J, Xu J. Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors. J Magn Reson Imaging 2020; 52:1124-1136. [PMID: 32343872 DOI: 10.1002/jmri.27176] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 04/05/2020] [Accepted: 04/06/2020] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Endoscopic ultrasound-guided fine-needle aspiration is associated with the accurate determination of tumor grade. However, because it is an invasive procedure there is a need to explore alternative noninvasive procedures. PURPOSE To develop and validate a noncontrast radiomics model for the preoperative prediction of nonfunctional pancreatic neuroendocrine tumor (NF-pNET) grade (G). STUDY TYPE Retrospective, single-center study. SUBJECTS Patients with pathologically confirmed PNETs (139) were included. FIELD STRENGTH/SEQUENCE 3T/breath-hold single-shot fast-spin echo T2 -weighted sequence and unenhanced and dynamic contrast-enhanced T1 -weighted fat-suppressed sequences. ASSESSMENT Tumor features on contrast MR images were evaluated by three board-certified abdominal radiologists. STATISTICAL TESTS Multivariable logistic regression analysis was used to develop the clinical model. The least absolute shrinkage and selection operator method and linear discriminative analysis (LDA) were used to select the features and to construct a radiomics model. The performance of the models was assessed using the training cohort (97 patients) and the validation cohort (42 patients), and decision curve analysis (DCA) was applied for clinical use. RESULTS The clinical model included 14 imaging features, and the corresponding area under the curve (AUC) was 0.769 (95% confidence interval [CI], 0.675-0.863) in the training cohort and 0.729 (95% CI, 0.568-0.890) in the validation cohort. The LDA included 14 selected radiomics features that showed good discrimination-in the training cohort (AUC, 0.851; 95% CI, 0.758-0.916) and the validation cohort (AUC, 0.736; 95% CI, 0.518-0.874). In the decision curves, if the threshold probability was 0.17-0.84, using the radiomics score to distinguish NF-pNET G1 and G2/3, offered more benefit than did the use of a treat-all-patients or treat-none scheme. DATA CONCLUSION The developed radiomics model using noncontrast MRI could help differentiate G1 and G2/3 tumors, to make the clinical decision, and screen pNETs grade. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1124-1136.
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Affiliation(s)
- Yun Bian
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Zengrui Zhao
- Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing University of Information Science and Technology, Nanjing, China
| | - Hui Jiang
- Department of Pathology, Changhai Hospital, Shanghai, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Kai Cao
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Shiwei Guo
- Department of Pancreatic Surgery, Changhai Hospital, Shanghai, China
| | - Li Wang
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Gang Jin
- Department of Pancreatic Surgery, Changhai Hospital, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Jun Xu
- Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing University of Information Science and Technology, Nanjing, China
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