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Cited by in F6Publishing
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] [Cited by in CrossRef: 11] [Cited by in F6Publishing: 11] [Article Influence: 3.7] [Reference Citation Analysis]
Number Citing Articles
1 Porrello G, Cannella R, Alvarez-Hornia Pérez E, Brancatelli G, Vernuccio F. The Neoplastic Side of the Abdominal Wall: A Comprehensive Pictorial Essay of Benign and Malignant Neoplasms. Diagnostics (Basel) 2023;13. [PMID: 36673126 DOI: 10.3390/diagnostics13020315] [Reference Citation Analysis]
2 Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022;15. [PMID: 36612061 DOI: 10.3390/cancers15010063] [Reference Citation Analysis]
3 Wong PK, Chan IN, Yan HM, Gao S, Wong CH, Yan T, Yao L, Hu Y, Wang ZR, Yu HH. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview. World J Gastroenterol 2022; 28(45): 6363-6379 [DOI: 10.3748/wjg.v28.i45.6363] [Reference Citation Analysis]
4 Wang Y, Wang Y, Ren J, Jia L, Ma L, Yin X, Yang F, Gao B. Malignancy risk of gastrointestinal stromal tumors evaluated with noninvasive radiomics: A multi-center study. Front Oncol 2022;12:966743. [DOI: 10.3389/fonc.2022.966743] [Reference Citation Analysis]
5 Inoue A, Ota S, Yamasaki M, Batsaikhan B, Furukawa A, Watanabe Y. Gastrointestinal stromal tumors: a comprehensive radiological review. Jpn J Radiol 2022. [PMID: 35809209 DOI: 10.1007/s11604-022-01305-x] [Reference Citation Analysis]
6 Canfora I, Cutaia G, Marcianò M, Calamia M, Faraone R, Cannella R, Benfante V, Comelli A, Guercio G, Giuseppe LR, Salvaggio G. A Predictive System to Classify Preoperative Grading of Rectal Cancer Using Radiomics Features. Lecture Notes in Computer Science 2022. [DOI: 10.1007/978-3-031-13321-3_38] [Reference Citation Analysis]
7 Cutaia G, Gargano R, Cannella R, Feo N, Greco A, Merennino G, Nicastro N, Comelli A, Benfante V, Salvaggio G, Casto AL. Radiomics Analyses of Schwannomas in the Head and Neck: A Preliminary Analysis. Lecture Notes in Computer Science 2022. [DOI: 10.1007/978-3-031-13321-3_28] [Reference Citation Analysis]
8 Shao M, Niu Z, He L, Fang Z, He J, Xie Z, Cheng G, Wang J. Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors. Front Oncol 2021;11:737302. [PMID: 34950578 DOI: 10.3389/fonc.2021.737302] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
9 Bartolotta TV, Taibbi A, Randazzo A, Gagliardo C. New frontiers in liver ultrasound: From mono to multi parametricity. World J Gastrointest Oncol 2021; 13(10): 1302-1316 [PMID: 34721768 DOI: 10.4251/wjgo.v13.i10.1302] [Cited by in CrossRef: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
10 Kang B, Yuan X, Wang H, Qin S, Song X, Yu X, Zhang S, Sun C, Zhou Q, Wei Y, Shi F, Yang S, Wang X. Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors. Front Oncol 2021;11:750875. [PMID: 34631589 DOI: 10.3389/fonc.2021.750875] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
11 Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E. A deep look into radiomics. Radiol Med 2021. [PMID: 34213702 DOI: 10.1007/s11547-021-01389-x] [Cited by in Crossref: 46] [Cited by in F6Publishing: 31] [Article Influence: 23.0] [Reference Citation Analysis]