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For: Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol 2020;196:879-87. [PMID: 32367456 DOI: 10.1007/s00066-020-01625-9] [Cited by in Crossref: 8] [Cited by in F6Publishing: 11] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Zhang R, Huo X, Wang Q, Zhang J, Duan S, Zhang Q, Zhang S. Prediction of TTF-1 expression in non-small-cell lung cancer using machine learning-based radiomics. J Cancer Res Clin Oncol 2022. [PMID: 36151427 DOI: 10.1007/s00432-022-04357-8] [Reference Citation Analysis]
2 Yu X, Zhang S, Xu J, Huang Y, Luo H, Huang C, Nie P, Deng Y, Mao N, Zhang R, Gao L, Li S, Kang B, Wang X. Nomogram Using CT Radiomics Features for Differentiation of Pneumonia-Type Invasive Mucinous Adenocarcinoma and Pneumonia: Multicenter Development and External Validation Study. American Journal of Roentgenology. [DOI: 10.2214/ajr.22.28139] [Reference Citation Analysis]
3 Shi W, Yang Z, Zhu M, Zou C, Li J, Liang Z, Wang M, Yu H, Yang B, Wang Y, Li C, Wang Z, Zhao W, Chen L. Correlation between PD-L1 expression and radiomic features in early-stage lung adenocarcinomas manifesting as ground-glass nodules. Front Oncol 2022;12:986579. [DOI: 10.3389/fonc.2022.986579] [Reference Citation Analysis]
4 Wang K, Chen P, Feng B, Tu J, Hu Z, Zhang M, Yang J, Zhan Y, Yao J, Xu D. Machine learning prediction of prostate cancer from transrectal ultrasound video clips. Front Oncol 2022;12:948662. [DOI: 10.3389/fonc.2022.948662] [Reference Citation Analysis]
5 Tang ZP, Ma Z, He Y, Liu RC, Jin BB, Wen DY, Wen R, Yin HH, Qiu CC, Gao RZ, Ma Y, Yang H. Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery. BMC Med Imaging 2022;22:147. [PMID: 35996097 DOI: 10.1186/s12880-022-00879-2] [Reference Citation Analysis]
6 Ito Y, Nakajima T, Inage T, Otsuka T, Sata Y, Tanaka K, Sakairi Y, Suzuki H, Yoshino I. Prediction of Nodal Metastasis in Lung Cancer Using Deep Learning of Endobronchial Ultrasound Images. Cancers 2022;14:3334. [DOI: 10.3390/cancers14143334] [Reference Citation Analysis]
7 Salihoğlu YS, Uslu Erdemir R, Aydur Püren B, Özdemir S, Uyulan Ç, Ergüzel TT, Tekin HO. Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules. Mol Imaging Radionucl Ther 2022;31:82-8. [PMID: 35770958 DOI: 10.4274/mirt.galenos.2021.43760] [Reference Citation Analysis]
8 Xie F, Zheng K, Liu L, Jin X, Fu L, Zhu Z. A Pilot Study of Radiomics Models Combining Multi-Probe and Multi-Modality Images of 68Ga-NOTA-PRGD2 and 18F-FDG PET/CT for Differentiating Benign and Malignant Pulmonary Space-Occupying Lesions. Front Oncol 2022;12:877501. [PMID: 35720018 DOI: 10.3389/fonc.2022.877501] [Reference Citation Analysis]
9 Bourbonne V, Geier M, Schick U, Lucia F. Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review. Biomedicines 2022;10:1237. [DOI: 10.3390/biomedicines10061237] [Reference Citation Analysis]
10 Zhao H, Su Y, Wang M, Lyu Z, Xu P, Jiao Y, Zhang L, Han W, Tian L, Fu P. The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer. Front Oncol 2022;12:875761. [DOI: 10.3389/fonc.2022.875761] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
11 Du T, Zhao H, Li C. Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast Cancer. BioMed Research International 2022;2022:1-15. [DOI: 10.1155/2022/1376659] [Reference Citation Analysis]
12 Ma X, Shu L, Jia X, Zhou H, Liu T, Liang J, Ding Y, He M, Shu Q. Machine Learning-Based CT Radiomics Method for Identifying the Stage of Wilms Tumor in Children. Front Pediatr 2022;10:873035. [DOI: 10.3389/fped.2022.873035] [Reference Citation Analysis]
13 Chang C, Ruan M, Lei B, Yu H, Zhao W, Ge Y, Duan S, Teng W, Wu Q, Qian X, Wang L, Yan H, Liu C, Liu L, Feng J, Xie W. Development of a PET/CT molecular radiomics-clinical model to predict thoracic lymph node metastasis of invasive lung adenocarcinoma ≤ 3 cm in diameter. EJNMMI Res 2022;12:23. [PMID: 35445899 DOI: 10.1186/s13550-022-00895-x] [Reference Citation Analysis]
14 Gui S, Lan M, Wang C, Nie S, Fan B. Application Value of Radiomic Nomogram in the Differential Diagnosis of Prostate Cancer and Hyperplasia. Front Oncol 2022;12:859625. [DOI: 10.3389/fonc.2022.859625] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 van der Bijl P, Stassen J, Bax JJ. Application of a deep learning algorithm to calcium scoring in myocardial perfusion imaging. J Nucl Cardiol . [DOI: 10.1007/s12350-022-02941-6] [Reference Citation Analysis]
16 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;14:1654. [PMID: 35406426 DOI: 10.3390/cancers14071654] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
17 Kroschke J, von Stackelberg O, Heußel CP, Wielpütz MO, Kauczor HU. Imaging Biomarkers in Thoracic Oncology: Current Advances in the Use of Radiomics in Lung Cancer Patients and its Potential Use for Therapy Response Prediction and Monitoring. Rofo 2022. [PMID: 35211928 DOI: 10.1055/a-1729-1516] [Reference Citation Analysis]
18 Xie Z, Zhang H, Singh D. Analysis of the Diagnosis Model of Peripheral Non-Small-Cell Lung Cancer under Computed Tomography Images. Journal of Healthcare Engineering 2022;2022:1-13. [DOI: 10.1155/2022/3107965] [Reference Citation Analysis]
19 Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022;12:773840. [DOI: 10.3389/fonc.2022.773840] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
20 Yan S, Huang Q, Yu S, Liu Z, Ramirez G. Computed Tomography Images under Deep Learning Algorithm in the Diagnosis of Perioperative Rehabilitation Nursing for Patients with Lung Cancer. Scientific Programming 2022;2022:1-10. [DOI: 10.1155/2022/8685604] [Reference Citation Analysis]
21 Zhang YM, Gong GZ, Qiu QT, Han YW, Lu HM, Yin Y. Radiomics for Diagnosis and Radiotherapy of Nasopharyngeal Carcinoma. Front Oncol 2021;11:767134. [PMID: 35070971 DOI: 10.3389/fonc.2021.767134] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Niu R, Gao J, Shao X, Wang J, Jiang Z, Shi Y, Zhang F, Wang Y, Shao X. Maximum Standardized Uptake Value of 18F-deoxyglucose PET Imaging Increases the Effectiveness of CT Radiomics in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules. Front Oncol 2021;11:727094. [PMID: 34976790 DOI: 10.3389/fonc.2021.727094] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Shi Z, Zhang Z, Liu Z, Zhao L, Ye Z, Dekker A, Wee L. Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy. Eur J Nucl Med Mol Imaging 2021. [PMID: 34939174 DOI: 10.1007/s00259-021-05658-9] [Reference Citation Analysis]
24 Zhang Z, He K, Wang Z, Zhang Y, Wu D, Zeng L, Zeng J, Ye Y, Gu T, Xiao X. Multiparametric MRI Radiomics for the Early Prediction of Response to Chemoradiotherapy in Patients With Postoperative Residual Gliomas: An Initial Study. Front Oncol 2021;11:779202. [PMID: 34869030 DOI: 10.3389/fonc.2021.779202] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
25 Jiang T, Jiang W, Chang S, Wang H, Niu S, Yue Z, Yang H, Wang X, Zhao N, Fang S, Luo Y, Jiang X. Intratumoral analysis of digital breast tomosynthesis for predicting the Ki-67 level in breast cancer: A multi-center radiomics study. Med Phys 2021. [PMID: 34861045 DOI: 10.1002/mp.15392] [Reference Citation Analysis]
26 Liu B, Li C, Sun X, Zhou W, Sun J, Liu H, Li S, Jia H, Xing L, Dong X. Assessment and Prognostic Value of Immediate Changes in Post-Ablation Intratumor Density Heterogeneity of Pulmonary Tumors via Radiomics-Based Computed Tomography Features. Front Oncol 2021;11:615174. [PMID: 34804908 DOI: 10.3389/fonc.2021.615174] [Reference Citation Analysis]
27 Kendrick J, Francis R, Hassan GM, Rowshanfarzad P, Jeraj R, Kasisi C, Rusanov B, Ebert M. Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies. Front Oncol 2021;11:771787. [PMID: 34790581 DOI: 10.3389/fonc.2021.771787] [Cited by in F6Publishing: 8] [Reference Citation Analysis]
28 Rocha ALG, da Conceição MAM, da Cunha Sequeira Mano FXP, Martins HC, Costa GMLM, Dos Santos Oliveiros Paiva BCB, Lapa PAA. Metabolic active tumour volume quantified on [18F]FDG PET/CT further stratifies TNM stage IV non-small cell lung cancer patients. J Cancer Res Clin Oncol 2021;147:3601-11. [PMID: 34570257 DOI: 10.1007/s00432-021-03799-w] [Reference Citation Analysis]
29 Avanzo M, Gagliardi V, Stancanello J, Blanck O, Pirrone G, El Naqa I, Revelant A, Sartor G. Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy. Med Phys 2021;48:6257-69. [PMID: 34415574 DOI: 10.1002/mp.15178] [Cited by in F6Publishing: 7] [Reference Citation Analysis]
30 Placidi L, Gioscio E, Garibaldi C, Rancati T, Fanizzi A, Maestri D, Massafra R, Menghi E, Mirandola A, Reggiori G, Sghedoni R, Tamborra P, Comi S, Lenkowicz J, Boldrini L, Avanzo M. A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity. Cancers (Basel) 2021;13:3835. [PMID: 34359737 DOI: 10.3390/cancers13153835] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
31 Liu X, Li KW, Yang R, Geng LS. Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy. Front Oncol 2021;11:717039. [PMID: 34336704 DOI: 10.3389/fonc.2021.717039] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
32 Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E. A deep look into radiomics. Radiol Med 2021. [PMID: 34213702 DOI: 10.1007/s11547-021-01389-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
33 Bianconi F, Fravolini ML, Pizzoli S, Palumbo I, Minestrini M, Rondini M, Nuvoli S, Spanu A, Palumbo B. Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT. Quant Imaging Med Surg 2021;11:3286-305. [PMID: 34249654 DOI: 10.21037/qims-20-1356] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
34 Shang S, Sun J, Yue Z, Wang Y, Wang X, Luo Y, Zhao D, Yu T, Jiang X. Multi-parametric MRI based radiomics with tumor subregion partitioning for differentiating benign and malignant soft-tissue tumors. Biomedical Signal Processing and Control 2021;67:102522. [DOI: 10.1016/j.bspc.2021.102522] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
35 Taralli S, Scolozzi V, Boldrini L, Lenkowicz J, Pelliccioni A, Lorusso M, Attieh O, Ricciardi S, Carleo F, Cardillo G, Calcagni ML. Application of Artificial Neural Network to Preoperative 18F-FDG PET/CT for Predicting Pathological Nodal Involvement in Non-small-cell Lung Cancer Patients. Front Med (Lausanne) 2021;8:664529. [PMID: 33968968 DOI: 10.3389/fmed.2021.664529] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
36 Yan M, Wang W. A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy. Sci Prog 2021;104:36850421997295. [PMID: 33687294 DOI: 10.1177/0036850421997295] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
37 Avanzo M, Trianni A, Botta F, Talamonti C, Stasi M, Iori M. Artificial Intelligence and the Medical Physicist: Welcome to the Machine. Applied Sciences 2021;11:1691. [DOI: 10.3390/app11041691] [Cited by in Crossref: 8] [Cited by in F6Publishing: 11] [Article Influence: 8.0] [Reference Citation Analysis]
38 Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening. Transl Lung Cancer Res 2021;10:1186-99. [PMID: 33718055 DOI: 10.21037/tlcr-20-708] [Cited by in Crossref: 1] [Cited by in F6Publishing: 12] [Article Influence: 1.0] [Reference Citation Analysis]
39 Padole A, Singh R, Zhang EW, Mendoza DP, Dagogo-Jack I, Kalra MK, Digumarthy SR. Radiomic features of primary tumor by lung cancer stage: analysis in BRAF mutated non-small cell lung cancer. Transl Lung Cancer Res 2020;9:1441-51. [PMID: 32953516 DOI: 10.21037/tlcr-20-347] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]