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Cited by in F6Publishing
For: Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell' Aversana F, Ottaiano A, Avallone A, Nasti G, Grassi F, Pilone V, Miele V, Brunese L, Izzo F, Petrillo A. Contrast MR-Based Radiomics and Machine Learning Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases: A Preliminary Study. Cancers (Basel) 2022;14:1110. [PMID: 35267418 DOI: 10.3390/cancers14051110] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Sun D, Dong J, Mu Y, Li F, Teekaraman Y. Texture Features of Computed Tomography Image under the Artificial Intelligence Algorithm and Its Predictive Value for Colorectal Liver Metastasis. Contrast Media & Molecular Imaging 2022;2022:1-8. [DOI: 10.1155/2022/2279018] [Reference Citation Analysis]
2 Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell'Aversana F, Grassi F, Belli A, Silvestro L, Ottaiano A, Nasti G, Avallone A, Flammia F, Miele V, Tatangelo F, Izzo F, Petrillo A. Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases. Radiol Med 2022. [PMID: 35653011 DOI: 10.1007/s11547-022-01501-9] [Reference Citation Analysis]
3 Granata V, Fusco R, De Muzio F, Cutolo C, Mattace Raso M, Gabelloni M, Avallone A, Ottaiano A, Tatangelo F, Brunese MC, Miele V, Izzo F, Petrillo A. Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern. Diagnostics 2022;12:1115. [DOI: 10.3390/diagnostics12051115] [Reference Citation Analysis]