Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis.

Autor:	Murtas F; Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy.; Department of Biomedicine and Prevention, University of Rome 'Tor Vergata', Rome, Italy., Landoni V; Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy., Ordòñez P; Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy., Greco L; Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy., Ferranti FR; Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy., Russo A; Pathology Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy., Perracchio L; Pathology Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy., Vidiri A; Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy.
Jazyk:	angličtina
Zdroj:	Frontiers in oncology [Front Oncol] 2023 May 12; Vol. 13, pp. 1152158. Date of Electronic Publication: 2023 May 12 (Print Publication: 2023).
DOI:	10.3389/fonc.2023.1152158
Abstrakt:	Objective: This study aimed to develop a clinical-radiomic model based on radiomic features extracted from digital breast tomosynthesis (DBT) images and clinical factors that may help to discriminate between benign and malignant breast lesions. Materials and Methods: A total of 150 patients were included in this study. DBT images acquired in the setting of a screening protocol were used. Lesions were delineated by two expert radiologists. Malignity was always confirmed by histopathological data. The data were randomly divided into training and validation set with an 80:20 ratio. A total of 58 radiomic features were extracted from each lesion using the LIFEx Software. Three different key methods of feature selection were implemented in Python: (1) K best (KB), (2) sequential (S), and (3) Random Forrest (RF). A model was therefore produced for each subset of seven variables using a machine-learning algorithm, which exploits the RF classification based on the Gini index. Results: All three clinical-radiomic models show significant differences (p < 0.05) between malignant and benign tumors. The area under the curve (AUC) values of the models obtained with three different feature selection methods were 0.72 [0.64,0.80], 0.72 [0.64,0.80] and 0.74 [0.66,0.82] for KB, SFS, and RF, respectively. Conclusion: The clinical-radiomic models developed by using radiomic features from DBT images showed a good discriminating power and hence may help radiologists in breast cancer tumor diagnoses already at the first screening. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Murtas, Landoni, Ordòñez, Greco, Ferranti, Russo, Perracchio and Vidiri.)
Databáze:	MEDLINE
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