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For: Hou M, Sun JH. Emerging applications of radiomics in rectal cancer: State of the art and future perspectives. World J Gastroenterol 2021; 27(25): 3802-3814 [PMID: PMC8291019 DOI: 10.3748/wjg.v27.i25.3802]
URL: https://www.wjgnet.com/1007-9327/full/v27/i25/3802.htm
Number Citing Articles
1
Yu-quan Wu, Rui-zhi Gao, Peng Lin, Rong Wen, Hai-yuan Li, Mei-yan Mou, Feng-huan Chen, Fen Huang, Wei-jie Zhou, Hong Yang, Yun He, Ji Wu. An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancerBMC Medical Imaging 2022; 22(1) doi: 10.1186/s12880-022-00813-6
2
Pengfei Tong, Danqi Sun, Guangqiang Chen, Jianming Ni, Yonggang Li. Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancerBMC Cancer 2023; 23(1) doi: 10.1186/s12885-023-10534-w
3
Ruobing Hu, Xiuling Li, Xiaomin Zhou, Songze Ding. Development and validation of a competitive risk model in patients with rectal cancer: based on SEER databaseEuropean Journal of Medical Research 2023; 28(1) doi: 10.1186/s40001-023-01357-3
4
Azadeh Tabari, Shin Mei Chan, Omar Mustafa Fathy Omar, Shams I. Iqbal, Michael S. Gee, Dania Daye. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal CancersCancers 2022; 15(1): 63 doi: 10.3390/cancers15010063
5
Yue Miao, Siyuan Tang, Zhuqiang Zhang, Jukun Song, Zhi Liu, Qiang Chen, Miao Zhang. Application of deep learning and XGBoost in predicting pathological staging of breast cancer MR imagesThe Journal of Supercomputing 2024; 80(7): 8933 doi: 10.1007/s11227-023-05797-w
6
Ilaria Canfora, Giuseppe Cutaia, Marco Marcianò, Mauro Calamia, Roberta Faraone, Roberto Cannella, Viviana Benfante, Albert Comelli, Giovanni Guercio, Lo Re Giuseppe, Giuseppe Salvaggio. Image Analysis and Processing. ICIAP 2022 WorkshopsLecture Notes in Computer Science 2022; 13373: 431 doi: 10.1007/978-3-031-13321-3_38
7
Rui Chen, Yan Fu, Xiaoping Yi, Qian Pei, Hongyan Zai, Bihong T. Chen, Xingrong Du. Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and ChallengesJournal of Oncology 2022; 2022: 1 doi: 10.1155/2022/1590620
8
Filippo Crimì, Carlo D’Alessandro, Chiara Zanon, Francesco Celotto, Christian Salvatore, Matteo Interlenghi, Isabella Castiglioni, Emilio Quaia, Salvatore Pucciarelli, Gaya Spolverato. A Machine Learning Model Based on MRI Radiomics to Predict Response to Chemoradiation Among Patients with Rectal CancerLife 2024; 14(12): 1530 doi: 10.3390/life14121530
9
Lei Wu, Jing-Jie Zhu, Xiao-Han Liang, He Tong, Yan Song. Predictive value of magnetic resonance imaging parameters combined with tumor markers for rectal cancer recurrence risk after surgeryWorld Journal of Gastrointestinal Surgery 2025; 17(2): 101897 doi: 10.4240/wjgs.v17.i2.101897
10
Thomas DeSilvio, Jacob T. Antunes, Kaustav Bera, Prathyush Chirra, Hoa Le, David Liska, Sharon L. Stein, Eric Marderstein, William Hall, Rajmohan Paspulati, Jayakrishna Gollamudi, Andrei S. Purysko, Satish E. Viswanath. Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader studyFrontiers in Medicine 2023; 10 doi: 10.3389/fmed.2023.1149056
11
如勇 秘. MDT Evaluation of Precision Therapy in Rectal CancerAdvances in Clinical Medicine 2023; 13(08): 12291 doi: 10.12677/ACM.2023.1381722
12
Lilang Lv, Bowen Xin, Yichao Hao, Ziyi Yang, Junyan Xu, Lisheng Wang, Xiuying Wang, Shaoli Song, Xiaomao Guo. Radiomic analysis for predicting prognosis of colorectal cancer from preoperative 18F-FDG PET/CTJournal of Translational Medicine 2022; 20(1) doi: 10.1186/s12967-022-03262-5
13
Pak Kin Wong, In Neng Chan, Hao-Ming Yan, Shan Gao, Chi Hong Wong, Tao Yan, Liang Yao, Ying Hu, Zhong-Ren Wang, Hon Ho Yu. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireviewWorld Journal of Gastroenterology 2022; 28(45): 6363-6379 doi: 10.3748/wjg.v28.i45.6363
14
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
15
Marina J. Corines, Abdalla Ibrahim, Akshay Baheti, Peter Gibbs, Shannon P. Sheedy, Sonia Lee, Stephanie Nougaret, Randy Ernst, Courtney C. Moreno, Elena Korngold, Michael Fox, Joao Miranda, Sergio Carlos Nahas, Iva Petkovska, Junting Zheng, Ramon E. Sosa, Natalie Gangai, Binsheng Zhao, Lawrence H. Schwartz, Natally Horvat, Marc J. Gollub. Can MRI radiomics distinguish residual adenocarcinoma from acellular mucin in treated rectal cancer?European Journal of Radiology 2025; 184: 111986 doi: 10.1016/j.ejrad.2025.111986
16
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 researchFrontiers in Oncology 2025; 14 doi: 10.3389/fonc.2024.1470824
17
Samira Abbaspour, Hamid Abdollahi, Hossein Arabalibeik, Maedeh Barahman, Amir Mohammad Arefpour, Pedram Fadavi, Mohammadreza Ay, Seied Rabi Mahdavi. Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learningAbdominal Radiology 2022; 47(11): 3645 doi: 10.1007/s00261-022-03625-y