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Cited by in F6Publishing
For: Interlenghi M, Salvatore C, Magni V, Caldara G, Schiavon E, Cozzi A, Schiaffino S, Carbonaro LA, Castiglioni I, Sardanelli F. A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses. Diagnostics 2022;12:187. [DOI: 10.3390/diagnostics12010187] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Tharmaseelan H, Hertel A, Rennebaum S, Nörenberg D, Haselmann V, Schoenberg SO, Froelich MF. The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization. Cancers (Basel) 2022;14:3349. [PMID: 35884409 DOI: 10.3390/cancers14143349] [Reference Citation Analysis]
2 Taghipour Zahir S, Aminpour S, Jafari-Nedooshan J, Rahmani K, SafiDahaj F. Comparative study of breast core needle biopsy (CNB) findings with ultrasound BI-RADS subtyping. Pol Przegl Chir 2022;95:1-6. [PMID: 36805305 DOI: 10.5604/01.3001.0015.8480] [Reference Citation Analysis]