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Cited by in CrossRef
For: Wang PP, Deng CL, Wu B. Magnetic resonance imaging-based artificial intelligence model in rectal cancer. World J Gastroenterol 2021; 27(18): 2122-2130 [PMID: 34025068 DOI: 10.3748/wjg.v27.i18.2122]
URL: https://www.wjgnet.com/1007-9327/full/v27/i18/2122.htm
Number Citing Articles
1
Francesca Maccioni, Ludovica Busato, Alessandra Valenti, Sara Cardaccio, Alessandro Longhi, Carlo Catalano. Magnetic Resonance Imaging of the Gastrointestinal Tract: Current Role, Recent Advancements and Future ProspectivesDiagnostics 2023; 13(14): 2410 doi: 10.3390/diagnostics13142410
2
Cristian-Constantin Volovat, Dragos-Viorel Scripcariu, Diana Boboc, Simona-Ruxandra Volovat, Ingrid-Andrada Vasilache, Corina Ursulescu-Lupascu, Liliana Gheorghe, Luiza-Maria Baean, Constantin Volovat, Viorel Scripcariu. Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological DataDiagnostics 2024; 14(6): 625 doi: 10.3390/diagnostics14060625
3
Long Wu, Huan Wu, Chen Li, Baofang Zhang, Xiaoyun Li, Yunhuan Zhen, Haiyang Li. Radiomics in colorectal canceriRADIOLOGY 2023; 1(3): 236 doi: 10.1002/ird3.29
4
Bhamini Vadhwana, Munir Tarazi, Vanash Patel. The Role of Artificial Intelligence in Prospective Real-Time Histological Prediction of Colorectal Lesions during Colonoscopy: A Systematic Review and Meta-AnalysisDiagnostics 2023; 13(20): 3267 doi: 10.3390/diagnostics13203267
5
Zhe Zhang, Xiawei Wei. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapySeminars in Cancer Biology 2023; 90: 57 doi: 10.1016/j.semcancer.2023.02.005
6
Markus von Wardenburg, Johannes Wessling. Die multiparametrische MRT zum Staging des Rektumkarzinoms – eine ÜbersichtRadiopraxis 2024; 17(01): 7 doi: 10.1055/a-2102-8157
7
Minsung Kim, Taeyong Park, Bo Young Oh, Min Jeong Kim, Bum-Joo Cho, Il Tae Son. Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic reviewAnnals of Coloproctology 2024; 40(1): 13 doi: 10.3393/ac.2023.00892.0127
8
Mengze Xu, Zhiyi Chen, Junxiao Zheng, Qi Zhao, Zhen Yuan. Artificial intelligence-aided optical imaging for cancer theranosticsSeminars in Cancer Biology 2023; 94: 62 doi: 10.1016/j.semcancer.2023.06.003
9
Stephanie Taha-Mehlitz, Silvio Däster, Laura Bach, Vincent Ochs, Markus von Flüe, Daniel Steinemann, Anas Taha. Modern Machine Learning Practices in Colorectal Surgery: A Scoping ReviewJournal of Clinical Medicine 2022; 11(9): 2431 doi: 10.3390/jcm11092431
10
Cristian-Constantin Volovat, Dragos-Viorel Scripcariu, Diana Boboc, Simona-Ruxandra Volovat, Ingrid-Andrada Vasilache, Corina Lupascu-Ursulescu, Liliana Gheorghe, Luiza-Maria Baean, Constantin Volovat, Viorel Scripcariu. Predicting the Feasibility of Curative Resection in Low Rectal Cancer: Insights from a Prospective Observational Study on Preoperative Magnetic Resonance Imaging AccuracyMedicina 2024; 60(2): 330 doi: 10.3390/medicina60020330
11
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