A Decision Support System Based on BI-RADS and Radiomic Classifiers to Reduce False Positive Breast Calcifications at Digital Breast Tomosynthesis: A Preliminary Study

Applied Sciences

Matteo Interlenghi¹  Christian Salvatore¹  Marco Alì²  Marina Maniglio²  Deborah Fazzini²  NataschaClaudia D’Amico²  Sergio Papa²  Isabella Castiglioni³  Simone Schiaffino⁴ 

[1] DeepTrace Technologies S.R.L., Via Conservatorio 17, 20122 Milan, Italy;Department of Diagnostic Imaging and Sterotactic Radiosurgery, Centro Diagnostico Italiano S.p.A., Via S. Saint Bon 20, 20147 Milan, Italy;Department of Physics “G. Occhialini”, University of Milan, Bicocca, Piazza della Scienza 3, 20126 Milan, Italy;Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy;

关键词: radiomic;    digital breast tomosynthesis;    calcifications;    diagnostic imaging;   

DOI  :  10.3390/app11062503

来源: DOAJ

【 摘 要 】

Digital breast tomosynthesis (DBT) studies were introduced as a successful help for the detection of calcification, which can be a primary sign of cancer. Expert radiologists are able to detect suspicious calcifications in DBT, but a high number of calcifications with non-malignant diagnosis at biopsy have been reported (false positives, FP). In this study, a radiomic approach was developed and applied on DBT images with the aim to reduce the number of benign calcifications addressed to biopsy and to give the radiologists a helpful decision support system during their diagnostic activity. This allows personalizing patient management on the basis of personalized risk. For this purpose, 49 patients showing microcalcifications on DBT images were retrospectively included, classified by BI-RADS (Breast Imaging-Reporting and Data System) and analyzed. After segmentation of microcalcifications from DBT images, radiomic features were extracted. Features were then selected with respect to their stability within different segmentations and their repeatability in test–retest studies. Stable radiomic features were used to train, validate and test (nested 10-fold cross-validation) a preliminary machine learning radiomic classifier that, combined with BI-RADS classification, allowed a reduction in FP of a factor of 2 and an improvement in positive predictive value of 50%.

【 授权许可】

Unknown