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

Federica Murtas; Valeria Landoni; Pedro Ordòñez; Laura Greco; Francesca Romana Ferranti; Andrea Russo; Letizia Perracchio; Antonello Vidiri

doi:10.3389/fonc.2023.1152158

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

Front Oncol. 2023 May 12:13:1152158. doi: 10.3389/fonc.2023.1152158. eCollection 2023.

Authors

Federica Murtas^{1

2}, Valeria Landoni¹, Pedro Ordòñez¹, Laura Greco³, Francesca Romana Ferranti³, Andrea Russo⁴, Letizia Perracchio⁴, Antonello Vidiri³

Affiliations

¹ Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy.
² Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy.
³ Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy.
⁴ Pathology Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy.

Abstract

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.

Keywords: AI; breast cancer; predictive model; radiomic; tomosynthesis (DBT).