| For: | Li ZY, Wang XD, Li M, Liu XJ, Ye Z, Song B, Yuan F, Yuan Y, Xia CC, Zhang X, Li Q. Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer. World J Gastroenterol 2020; 26(19): 2388-2402 [PMID: 32476800 DOI: 10.3748/wjg.v26.i19.2388] |
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| URL: | https://www.wjgnet.com/1007-9327/full/v26/i19/2388.htm |
| Number | Citing Articles |
| 1 |
Shi Hui Tay, Xin Zhang, Melvin L. K. Chua. Radiomics in precision oncology: hype or ludum mutante. BMC Medicine 2023; 21(1) doi: 10.1186/s12916-023-03165-2
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| 2 |
I. Jurisica. Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging. 2022; doi: 10.1007/978-3-031-00119-2_13
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| 3 |
Xian Shao, Huanqing Xu, Lianqin Chen, Pufei Bai, Haizhen Sun, Qian Yang, Ruixuan Chen, Queran Lin, Lihua Wang, Ying Li, Yao Lin, Pei Yu. Multi‐modal models using fMRI, urine and serum biomarkers for classification and risk prognosis in diabetic kidney disease. Diabetes, Obesity and Metabolism 2025; 27(9) doi: 10.1111/dom.16572
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| 4 |
Wenjun Meng, Lu Pan, Li Huang, Qing Li, Yi Sun. Applications of image-guided locoregional transarterial chemotherapy in patients with inoperable colorectal cancer: a review. Frontiers in Oncology 2024; 14 doi: 10.3389/fonc.2024.1464242
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| 5 |
Gabriella Rossi, Luisa Altabella, Nicola Simoni, Giulio Benetti, Roberto Rossi, Martina Venezia, Salvatore Paiella, Giuseppe Malleo, Roberto Salvia, Stefania Guariglia, Claudio Bassi, Carlo Cavedon, Renzo Mazzarotto. Computed tomography-based radiomic to predict resectability in locally advanced pancreatic cancer treated with chemotherapy and radiotherapy. World Journal of Gastrointestinal Oncology 2022; 14(3): 703-715 doi: 10.4251/wjgo.v14.i3.703
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| 6 |
Ilaria Ambrosini, Roberto Francischello, Salvatore Claudio Fanni, Lorenzo Faggioni, Francesca Pia Caputo, Karolina Cwiklinska, Gayane Aghakhanyan, Emanuele Neri, Riccardo Lencioni, Dania Cioni. MRI-Based Radiomics to Predict Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer: A Retrospective Study. Journal of Personalized Medicine 2026; 16(6) doi: 10.3390/jpm16060282
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| 7 |
Femke C.R. Staal, Denise J. van der Reijd, Marjaneh Taghavi, Doenja M.J. Lambregts, Regina G.H. Beets-Tan, Monique Maas. Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review. Clinical Colorectal Cancer 2021; 20(1) doi: 10.1016/j.clcc.2020.11.001
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| 8 |
Apekshya Singh, Xiao-Fu Li, Sheng-Ming Shi, Han Liu, Yu-peng Wu, Sanjeev Nirala. Emerging MRI Biomarkers for Prognostication in Rectal Cancer. Current Cancer Therapy Reviews 2026; 22(1) doi: 10.2174/0115733947344693241018081807
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| 9 |
Camil Ciprian Mireștean, Roxana Irina Iancu, Dragoș Petru Teodor Iancu. Capecitabine—A “Permanent Mission” in Head and Neck Cancers “War Council”?. Journal of Clinical Medicine 2022; 11(19) doi: 10.3390/jcm11195582
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| 10 |
Yuting Shi, Qiuhan Huang, Jiali Lyu, Tianjie Dong, Jihong Sun. Progress of MRI‑based radiomics and deep learning for predicting the prognosis of locally advanced rectal cancer (Review). Oncology Letters 2025; 30(5) doi: 10.3892/ol.2025.15282
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| 11 |
Zongtai Zheng, Feijia Xu, Zhuoran Gu, Yang Yan, Tianyuan Xu, Shenghua Liu, Xudong Yao. Integrating multiparametric MRI radiomics features and the Vesical Imaging-Reporting and Data System (VI-RADS) for bladder cancer grading. Abdominal Radiology 2021; 46(9) doi: 10.1007/s00261-021-03108-6
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| 12 |
Bi-Yun Chen, Hui Xie, Yuan Li, Xin-Hua Jiang, Lang Xiong, Xiao-Feng Tang, Xiao-Feng Lin, Li Li, Pei-Qiang Cai. MRI-Based Radiomics Features to Predict Treatment Response to Neoadjuvant Chemotherapy in Locally Advanced Rectal Cancer: A Single Center, Prospective Study. Frontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.801743
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| 13 |
Qingfeng Chen. Association Analysis Techniques and Applications in Bioinformatics. 2024; doi: 10.1007/978-981-99-8251-6_10
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| 14 |
Haoyu Wang, Peihong Li. Shifted window-based Transformer with multimodal representation for the systematic staging of rectal cancer. Service Oriented Computing and Applications 2025; 19(3) doi: 10.1007/s11761-024-00400-3
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| 15 |
Fang Wang, Hong Yang, Wujie Chen, Lei Ruan, Tingting Jiang, Lei Cheng, Haitao Jiang, Min Fang. A combined model using pre-treatment CT radiomics and clinicopathological features of non-small cell lung cancer to predict major pathological responses after neoadjuvant chemoimmunotherapy. Current Problems in Cancer 2024; 50 doi: 10.1016/j.currproblcancer.2024.101098
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| 16 |
Xiaofang Guo, Yaoyao He, Zilong Yuan, Tingting Nie, Yulin Liu, Haibo Xu. Association Analysis Between Intratumoral and Peritumoral MRI Radiomics Features and Overall Survival of Neoadjuvant Therapy in Rectal Cancer. Journal of Magnetic Resonance Imaging 2025; 61(1) doi: 10.1002/jmri.29396
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| 17 |
Feng-ao Wang, Yixue Li, Tao Zeng. Deep Learning of radiology-genomics integration for computational oncology: A mini review. Computational and Structural Biotechnology Journal 2024; 23 doi: 10.1016/j.csbj.2024.06.019
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| 18 |
Zhiheng Li, Huizhen Huang, Chuchu Wang, Zhenhua Zhao, Weili Ma, Dandan Wang, Haijia Mao, Fang Liu, Ye Yang, Weihuo Pan, Zengxin Lu. DCE-MRI radiomics models predicting the expression of radioresistant-related factors of LRP-1 and survivin in locally advanced rectal cancer. Frontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.881341
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| 19 |
Qiaoling Chen, Qianwen Zhang, Zhihui Li, Shaoting Zhang, Yuwei Xia, Hao Wang, Yong Lu, Anqi Zheng, Chengwei Shao, Fu Shen. MRI-based habitat analysis for pathologic response prediction after neoadjuvant chemoradiotherapy in rectal cancer: a multicenter study. European Radiology 2025; 36(3) doi: 10.1007/s00330-025-11997-0
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| 20 |
Qiuyuan Yue, Mingwei Zhang, Wenying Jiang, Lanmei Gao, Rongping Ye, Jinsheng Hong, Yueming Li. Prognostic value of FDX1, the cuprotosis key gene, and its prediction models across imaging modalities and histology. BMC Cancer 2024; 24(1) doi: 10.1186/s12885-024-13149-x
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| 21 |
Weijing He, Qingguo Li, Xinxiang Li. Changing patterns of neoadjuvant therapy for locally advanced rectal cancer: A narrative review. Critical Reviews in Oncology/Hematology 2023; 181 doi: 10.1016/j.critrevonc.2022.103885
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| 22 |
Zhendong Luo, Jing Li, YuTing Liao, Wenxiao Huang, Yulin Li, Xinping Shen. Prediction of response to preoperative neoadjuvant chemotherapy in extremity high-grade osteosarcoma using X-ray and multiparametric MRI radiomics. Journal of X-Ray Science and Technology 2023; 31(3) doi: 10.3233/XST-221352
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| 23 |
Zuhir Bodalal, Nino Bogveradze, Leon C. ter Beek, Jose G. van den Berg, Joyce Sanders, Ingrid Hofland, Stefano Trebeschi, Kevin B. W. Groot Lipman, Koen Storck, Eun Kyoung Hong, Natalya Lebedyeva, Monique Maas, Regina G. H. Beets-Tan, Fernando M. Gomez, Ieva Kurilova. Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases. Insights into Imaging 2023; 14(1) doi: 10.1186/s13244-023-01474-x
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| 24 |
Nuo Yu, Yidong Wan, Lijing Zuo, Ying Cao, Dong Qu, Wenyang Liu, Lei Deng, Tao Zhang, Wenqing Wang, Jianyang Wang, Jima Lv, Zefen Xiao, Qinfu Feng, Zongmei Zhou, Nan Bi, Tianye Niu, Xin Wang. Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy. BMC Cancer 2025; 25(1) doi: 10.1186/s12885-025-13996-2
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| 25 |
Joao Miranda, Natally Horvat, Jose A. B. Araujo-Filho, Kamila S. Albuquerque, Charlotte Charbel, Bruno M. C. Trindade, Daniel L. Cardoso, Lucas de Padua Gomes de Farias, Jayasree Chakraborty, Cesar Higa Nomura. The Role of Radiomics in Rectal Cancer. Journal of Gastrointestinal Cancer 2023; 54(4) doi: 10.1007/s12029-022-00909-w
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| 26 |
Mohammad Mirza-Aghazadeh-Attari, Bharath Ambale Venkatesh, Mounes Aliyari Ghasabeh, Alireza Mohseni, Seyedeh Panid Madani, Ali Borhani, Haneyeh Shahbazian, Golnoosh Ansari, Ihab R. Kamel. The Additive Value of Radiomics Features Extracted from Baseline MR Images to the Barcelona Clinic Liver Cancer (BCLC) Staging System in Predicting Transplant-Free Survival in Patients with Hepatocellular Carcinoma: A Single-Center Retrospective Analysis. Diagnostics 2023; 13(3) doi: 10.3390/diagnostics13030552
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| 27 |
Yunsong Liu, Yi Wang, Xin Wang, Liyan Xue, Huan Zhang, Zeliang Ma, Heping Deng, Zhaoyang Yang, Xujie Sun, Yu Men, Feng Ye, Kuo Men, Jianjun Qin, Nan Bi, Qifeng Wang, Zhouguang Hui. MR radiomics predicts pathological complete response of esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy: a multicenter study. Cancer Imaging 2024; 24(1) doi: 10.1186/s40644-024-00659-x
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| 28 |
Sararas Khongwirotphan, Sornjarod Oonsiri, Sarin Kitpanit, Anussara Prayongrat, Danita Kannarunimit, Chakkapong Chakkabat, Chawalit Lertbutsayanukul, Sira Sriswasdi, Yothin Rakvongthai, Lorenzo Faggioni. Multimodality radiomics for tumor prognosis in nasopharyngeal carcinoma. PLOS ONE 2024; 19(2) doi: 10.1371/journal.pone.0298111
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| 29 |
Wen-Zhe Kang, Bing-Zhi Wang, Deng-Feng Li, Zhi-Chao Jiang, Jian-Ping Xiong, Yang Li, Peng Jin, Xin-Xin Shao, Hai-Tao Hu, Yan-Tao Tian, Alessandro Granito. Can Gastric Cancer Patients with High Mandard Score Benefit from Neoadjuvant Chemotherapy?. Canadian Journal of Gastroenterology and Hepatology 2022; 2022 doi: 10.1155/2022/8178184
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| 30 |
Zhou Chuanji, Wang Zheng, Lai Shaolv, Meng Linghou, Lu Yixin, Lu Xinhui, Lin Ling, Tang Yunjing, Zhang Shilai, Mo Shaozhou, Zhang Boyang. Comparative study of radiomics, tumor morphology, and clinicopathological factors in predicting overall survival of patients with rectal cancer before surgery. Translational Oncology 2022; 18 doi: 10.1016/j.tranon.2022.101352
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| 31 |
TingDan Hu, Jing Gong, YiQun Sun, MengLei Li, ChongPeng Cai, XinXiang Li, YanFen Cui, XiaoYan Zhang, Tong Tong. Magnetic resonance imaging‐based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study. MedComm 2024; 5(7) doi: 10.1002/mco2.609
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| 32 |
Zhen Zhao, Dongdong Xiao, Chuansheng Nie, Hao Zhang, Xiaobing Jiang, Ali Rajab Jecha, Pengfei Yan, Hongyang Zhao. Development of a Nomogram Based on Preoperative Bi-Parametric MRI and Blood Indices for the Differentiation Between Cystic-Solid Pituitary Adenoma and Craniopharyngioma. Frontiers in Oncology 2021; 11 doi: 10.3389/fonc.2021.709321
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| 33 |
Joao Miranda, Gary Xia Vern Tan, Maria Clara Fernandes, Onur Yildirim, John A. Sims, Jose de Arimateia Batista Araujo-Filho, Felipe Augusto de M. Machado, Antonildes N. Assuncao-Jr, Cesar Higa Nomura, Natally Horvat. Rectal MRI radiomics for predicting pathological complete response: Where we are. Clinical Imaging 2022; 82 doi: 10.1016/j.clinimag.2021.10.005
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| 34 |
Henry C. Kwok, Charlotte Charbel, Sofia Danilova, Joao Miranda, Natalie Gangai, Iva Petkovska, Jayasree Chakraborty, Natally Horvat. Rectal MRI radiomics inter- and intra-reader reliability: should we worry about that?. Abdominal Radiology 2022; 47(6) doi: 10.1007/s00261-022-03503-7
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| 35 |
Iram Shahzadi, Annika Lattermann, Annett Linge, Alexander Zwanenburg, Christian Baldus, Jan C. Peeken, Stephanie E. Combs, Michael Baumann, Mechthild Krause, Esther G. C. Troost, Steffen Löck. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Lecture Notes in Computer Science 2021; 12907 doi: 10.1007/978-3-030-87234-2_73
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| 36 |
William C. Sleeman, Rishabh Kapoor, Preetam Ghosh. Multimodal Classification: Current Landscape, Taxonomy and Future Directions. ACM Computing Surveys 2023; 55(7) doi: 10.1145/3543848
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| 37 |
Xueting Qu, Liang Zhang, Weina Ji, Jizheng Lin, Guohua Wang. Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics. Frontiers in Oncology 2023; 13 doi: 10.3389/fonc.2023.1267838
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| 38 |
Yu Gao, Jonathan Pham, Stephanie Yoon, Minsong Cao, Peng Hu, Yingli Yang. Recent Advances in Functional MRI to Predict Treatment Response for Locally Advanced Rectal Cancer. Current Colorectal Cancer Reports 2021; 17(6) doi: 10.1007/s11888-021-00470-x
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| 39 |
Guoquan Cao, Ji Zhang, Xiyao Lei, Bing Yu, Yao Ai, Zhenhua Zhang, Congying Xie, Xiance Jin, Liu Jinhui. Differentiating Primary Tumors for Brain Metastasis with Integrated Radiomics from Multiple Imaging Modalities. Disease Markers 2022; 2022 doi: 10.1155/2022/5147085
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| 40 |
Shuang Chen, Lin Liu, Guangwei Tian, Ruimei Chai. MRI‐based qualitative, quantitative, and radiomics/deep learning methods for assessing treatment response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer. Precision Radiation Oncology 2026; 10(1) doi: 10.1002/pro6.70055
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| 41 |
Yu Liu, Tingting Li, Zhenlong Wang, Jiuhong Guo, Yuanjun Wang. A novel clinical radiomics nomogram to predict disease activity in Crohn’s disease. British Journal of Radiology 2025; 98(1173) doi: 10.1093/bjr/tqaf141
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| 42 |
Jing Cheng, Xinying Wu. Developing a predictive model for the efficacy of neoadjuvant chemoradiotherapy in locally advanced rectal cancer using multiparametric magnetic resonance imaging: An innovative approach. Medicine 2025; 104(49) doi: 10.1097/MD.0000000000046209
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| 43 |
Pouria Yazdian Anari, Jovitha Nelson, Ashkan A. Malayeri. Machine Learning in MRI - From Methods to Clinical Translation. Advances in Magnetic Resonance Technology and Applications 2026; 13 doi: 10.1016/B978-0-443-14109-6.00021-3
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| 44 |
Bianca Petresc, Andrei Lebovici, Cosmin Caraiani, Diana Sorina Feier, Florin Graur, Mircea Marian Buruian. Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study. Cancers 2020; 12(7) doi: 10.3390/cancers12071894
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| 45 |
Andrea Delli Pizzi, Antonio Maria Chiarelli, Piero Chiacchiaretta, Martina d’Annibale, Pierpaolo Croce, Consuelo Rosa, Domenico Mastrodicasa, Stefano Trebeschi, Doenja Marina Johanna Lambregts, Daniele Caposiena, Francesco Lorenzo Serafini, Raffaella Basilico, Giulio Cocco, Pierluigi Di Sebastiano, Sebastiano Cinalli, Antonio Ferretti, Richard Geoffrey Wise, Domenico Genovesi, Regina G. H. Beets-Tan, Massimo Caulo. MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer. Scientific Reports 2021; 11(1) doi: 10.1038/s41598-021-84816-3
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| 46 |
Jiali Lyu, Zhenzhu Pang, Jihong Sun. Radiomics prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Radiology Science 2024; 3(1) doi: 10.15212/RADSCI-2023-0005
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| 47 |
Valerio Nardone, Luca Boldrini, Roberta Grassi, Davide Franceschini, Ilaria Morelli, Carlotta Becherini, Mauro Loi, Daniela Greto, Isacco Desideri. Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment. Cancers 2021; 13(14) doi: 10.3390/cancers13143590
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| 48 |
Iram Shahzadi, Alex Zwanenburg, Annika Lattermann, Annett Linge, Christian Baldus, Jan C. Peeken, Stephanie E. Combs, Markus Diefenhardt, Claus Rödel, Simon Kirste, Anca-Ligia Grosu, Michael Baumann, Mechthild Krause, Esther G. C. Troost, Steffen Löck. Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models. Scientific Reports 2022; 12(1) doi: 10.1038/s41598-022-13967-8
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| 49 |
Siyu Zhang, Mingrong Yu, Dan Chen, Peidong Li, Bin Tang, Jie Li. Role of MRI‑based radiomics in locally advanced rectal cancer (Review). Oncology Reports 2021; 47(2) doi: 10.3892/or.2021.8245
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| 50 |
Sin‐Hua Moi, Ming‐Yii Huang, Shu‐Han Yang, Chien‐Jang Chen, Ying‐Pei Jhong, Tun‐Wei Hsu, Chien‐Chih Ke. Integrating CT‐Based Radiomics Analysis With Serum CEA Level to Predict Neoadjuvant Chemoradiotherapy Response in Colorectal Cancer. The Kaohsiung Journal of Medical Sciences 2026; doi: 10.1002/kjm2.70241
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| 51 |
Lang Xiong, Xiaofeng Tang, Xinhua Jiang, Haolin Chen, Binyan Qian, Biyun Chen, Xiaofeng Lin, Jianhua Zhou, Li Li. Automatic segmentation-based multi-modal radiomics analysis of US and MRI for predicting disease-free survival of breast cancer: a multicenter study. Breast Cancer Research 2024; 26(1) doi: 10.1186/s13058-024-01909-3
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| 52 |
Lingyun Wang, Yong Chen, Jingwen Tan, Yingqian Ge, Zhihan Xu, Michael Wels, Zilai Pan. Efficacy and prognostic value of delta radiomics on dual-energy computed tomography for gastric cancer with neoadjuvant chemotherapy: a preliminary study. Acta Radiologica 2023; 64(4) doi: 10.1177/02841851221123971
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| 53 |
Junfeng Yan, Qiushuang Wang, Qiang Li, Jiatong Lu, Qiang Tong. Preoperative magnetic resonance imaging–based radiomics nomogram model for predicting postoperative anastomotic leakage in colorectal cancer. Annals of Coloproctology 2025; 41(6) doi: 10.3393/ac.2025.00689.0098
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| 54 |
Fang Wang, Wujie Chen, Fangmin Chen, Jinlan Lu, Yanjun Xu, Min Fang, Haitao Jiang. Risk stratification and overall survival prediction in extensive stage small cell lung cancer after chemotherapy with immunotherapy based on CT radiomics. Scientific Reports 2024; 14(1) doi: 10.1038/s41598-024-73331-w
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| 55 |
Yuan Cheng, Yahong Luo, Yue Hu, Zhaohe Zhang, Xingling Wang, Qing Yu, Guanyu Liu, Enuo Cui, Tao Yu, Xiran Jiang. Multiparametric MRI-based Radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Abdominal Radiology 2021; 46(11) doi: 10.1007/s00261-021-03219-0
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| 56 |
Arnaldo Stanzione, Francesco Verde, Valeria Romeo, Francesca Boccadifuoco, Pier Paolo Mainenti, Simone Maurea. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World Journal of Gastroenterology 2021; 27(32): 5306-5321 doi: 10.3748/wjg.v27.i32.5306
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| 57 |
Xuezhi Zhou, Yi Yu, Yanru Feng, Guojun Ding, Peng Liu, Luying Liu, Wenjie Ren, Yuan Zhu, Wuteng Cao. Attention mechanism based multi-sequence MRI fusion improves prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Radiation Oncology 2023; 18(1) doi: 10.1186/s13014-023-02352-y
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| 58 |
Wei-Qin Huang, Ruo-Xuan Lin, Xiao-Hui Ke, Xiao-Hong Deng, Shi-Xiong Ni, Lina Tang. Radiomics in rectal cancer: current status of use and advances in research. Frontiers in Oncology 2025; 14 doi: 10.3389/fonc.2024.1470824
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| 59 |
Harsha Veena Kanamathareddy, Aisha Lakhani, Antony Augustine, Sonia Thanikaivelu, Anu Eapen, Reetu John, Betty Simon, Dipti Masih, Inian Sarvarasan, Ashish Singh, Anuradha Chandramohan. Prevalence of CT-detected extramural vascular invasion in gastric adenocarcinoma and its correlation with other known prognostic factors. Japanese Journal of Radiology 2025; 43(1) doi: 10.1007/s11604-024-01644-x
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