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Cited by in CrossRef
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]
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 mutanteBMC Medicine 2023; 21(1) doi: 10.1186/s12916-023-03165-2
2
I. Jurisica. Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging2022; : 171 doi: 10.1007/978-3-031-00119-2_13
3
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 radiotherapyWorld Journal of Gastrointestinal Oncology 2022; 14(3): 703-715 doi: 10.4251/wjgo.v14.i3.703
4
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 2021Lecture Notes in Computer Science 2021; 12907: 775 doi: 10.1007/978-3-030-87234-2_73
5
William C. Sleeman, Rishabh Kapoor, Preetam Ghosh. Multimodal Classification: Current Landscape, Taxonomy and Future DirectionsACM Computing Surveys 2023; 55(7): 1 doi: 10.1145/3543848
6
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 radiomicsFrontiers in Oncology 2023; 13 doi: 10.3389/fonc.2023.1267838
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 ReviewClinical Colorectal Cancer 2021; 20(1): 52 doi: 10.1016/j.clcc.2020.11.001
8
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 CancerCurrent Colorectal Cancer Reports 2021; 17(6): 77 doi: 10.1007/s11888-021-00470-x
9
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 ModalitiesDisease Markers 2022; 2022: 1 doi: 10.1155/2022/5147085
10
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): 5582 doi: 10.3390/jcm11195582
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 gradingAbdominal Radiology 2021; 46(9): 4311 doi: 10.1007/s00261-021-03108-6
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 StudyFrontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.801743
13
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 StudyCancers 2020; 12(7): 1894 doi: 10.3390/cancers12071894
14
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 cancerScientific Reports 2021; 11(1) doi: 10.1038/s41598-021-84816-3
15
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 cancerFrontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.881341
16
Jiali Lyu, Zhenzhu Pang, Jihong Sun. Radiomics prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancerRadiology Science 2024; 3(1) doi: 10.15212/RADSCI-2023-0005
17
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 TreatmentCancers 2021; 13(14): 3590 doi: 10.3390/cancers13143590
18
Weijing He, Qingguo Li, Xinxiang Li. Changing patterns of neoadjuvant therapy for locally advanced rectal cancer: A narrative reviewCritical Reviews in Oncology/Hematology 2023; 181: 103885 doi: 10.1016/j.critrevonc.2022.103885
19
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 radiomicsJournal of X-Ray Science and Technology 2023; 31(3): 611 doi: 10.3233/XST-221352
20
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 metastasesInsights into Imaging 2023; 14(1) doi: 10.1186/s13244-023-01474-x
21
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 modelsScientific Reports 2022; 12(1) doi: 10.1038/s41598-022-13967-8
22
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
23
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 CancerJournal of Gastrointestinal Cancer 2023; 54(4): 1158 doi: 10.1007/s12029-022-00909-w
24
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 AnalysisDiagnostics 2023; 13(3): 552 doi: 10.3390/diagnostics13030552
25
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 studyActa Radiologica 2023; 64(4): 1311 doi: 10.1177/02841851221123971
26
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 studyCancer Imaging 2024; 24(1) doi: 10.1186/s40644-024-00659-x
27
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 carcinomaPLOS ONE 2024; 19(2): e0298111 doi: 10.1371/journal.pone.0298111
28
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: 1 doi: 10.1155/2022/8178184
29
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 surgeryTranslational Oncology 2022; 18: 101352 doi: 10.1016/j.tranon.2022.101352
30
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 CraniopharyngiomaFrontiers in Oncology 2021; 11 doi: 10.3389/fonc.2021.709321
31
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 cancerAbdominal Radiology 2021; 46(11): 5072 doi: 10.1007/s00261-021-03219-0
32
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 perspectivesWorld Journal of Gastroenterology 2021; 27(32): 5306-5321 doi: 10.3748/wjg.v27.i32.5306
33
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 cancerRadiation Oncology 2023; 18(1) doi: 10.1186/s13014-023-02352-y
34
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 areClinical Imaging 2022; 82: 141 doi: 10.1016/j.clinimag.2021.10.005
35
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): 2004 doi: 10.1007/s00261-022-03503-7