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Cited by in CrossRef
For: Cannella R, La Grutta L, Midiri M, Bartolotta TV. New advances in radiomics of gastrointestinal stromal tumors. World J Gastroenterol 2020; 26(32): 4729-4738 [PMID: 32921953 DOI: 10.3748/wjg.v26.i32.4729]
URL: https://www.wjgnet.com/2307-8960/full/v26/i32/4729.htm
Number Citing Articles
1
Giorgia Porrello, Roberto Cannella, Eduardo Alvarez-Hornia Pérez, Giuseppe Brancatelli, Federica Vernuccio. The Neoplastic Side of the Abdominal Wall: A Comprehensive Pictorial Essay of Benign and Malignant NeoplasmsDiagnostics 2023; 13(2): 315 doi: 10.3390/diagnostics13020315
2
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
3
Akitoshi Inoue, Shinichi Ota, Michio Yamasaki, Bolorkhand Batsaikhan, Akira Furukawa, Yoshiyuki Watanabe. Gastrointestinal stromal tumors: a comprehensive radiological reviewJapanese Journal of Radiology 2022; 40(11): 1105 doi: 10.1007/s11604-022-01305-x
4
Yun Wang, Yurui Wang, Jialiang Ren, Linyi Jia, Luyao Ma, Xiaoping Yin, Fei Yang, Bu-Lang Gao. Malignancy risk of gastrointestinal stromal tumors evaluated with noninvasive radiomics: A multi-center studyFrontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.966743
5
Bing Kang, Xianshun Yuan, Hexiang Wang, Songnan Qin, Xuelin Song, Xinxin Yu, Shuai Zhang, Cong Sun, Qing Zhou, Ying Wei, Feng Shi, Shifeng Yang, Ximing Wang. Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal TumorsFrontiers in Oncology 2021; 11 doi: 10.3389/fonc.2021.750875
6
Giuseppe Cutaia, Rosalia Gargano, Roberto Cannella, Nicoletta Feo, Antonio Greco, Giuseppe Merennino, Nicola Nicastro, Albert Comelli, Viviana Benfante, Giuseppe Salvaggio, Antonio Lo Casto. Image Analysis and Processing. ICIAP 2022 WorkshopsLecture Notes in Computer Science 2022; 13373: 317 doi: 10.1007/978-3-031-13321-3_28
7
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
8
Meihua Shao, Zhongfeng Niu, Linyang He, Zhaoxing Fang, Jie He, Zongyu Xie, Guohua Cheng, Jian Wang. Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal TumorsFrontiers in Oncology 2021; 11 doi: 10.3389/fonc.2021.737302
9
Camilla Scapicchio, Michela Gabelloni, Andrea Barucci, Dania Cioni, Luca Saba, Emanuele Neri. A deep look into radiomicsLa radiologia medica 2021; 126(10): 1296 doi: 10.1007/s11547-021-01389-x
10
Cesare Gagliardo, Angelo Randazzo, Adele Taibbi, Tommaso Vincenzo Bartolotta. New frontiers in liver ultrasound: From mono to multi parametricityWorld Journal of Gastrointestinal Oncology 2021; 13(10): 1302-1316 doi: 10.4251/wjgo.v13.i10.1302
11
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