Abstract
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%.
Highlights
Breast calcifications are a diagnostic challenge in mammography interpretation and frequently prompt a needle biopsy [1], being a possible sign of breast cancer (BC) [2].The introduction of quasi-three-dimensional (3D) acquisition with digital breast tomosynthesis (DBT) has brought considerable advantages in BC detection rates and in some studies, lowered the false positive (FP) rate and the recall rate of patients [3]
Digital breast tomosynthesis (DBT) studies were introduced as a successful help for the detection of calcification, which can be a primary sign of cancer
Impressions from initial studies on DBT in the 1990s supposed lower accuracy compared to standard digital mammography (DM), mainly because a cluster of microcalcifications may be visible on different two-dimensional (2D) images, with poor resolution in out-of-focus images and lack of comprehensive cluster visualization [5]
Summary
Breast calcifications are a diagnostic challenge in mammography interpretation and frequently prompt a needle biopsy [1], being a possible sign of breast cancer (BC) [2].The introduction of quasi-three-dimensional (3D) acquisition with digital breast tomosynthesis (DBT) has brought considerable advantages in BC detection rates and in some studies, lowered the false positive (FP) rate and the recall (i.e., assessment) rate of patients [3]. Impressions from initial studies on DBT in the 1990s supposed lower accuracy compared to standard digital mammography (DM), mainly because a cluster of microcalcifications may be visible on different two-dimensional (2D) images, with poor resolution in out-of-focus images and lack of comprehensive cluster visualization [5]. These drawbacks have been overcome using DBT image series and/or DBT-derived synthetic two-dimensional (2D) views, offering a visualization of calcifications similar or even highlighted when compared to standard DM. Notwithstanding these improvements, the malignancy rate of the calcifications addressed to needle biopsy on the basis of DBT remained relatively low, as it was with 2D mammography, with a not-negligible amount of FP, as reported by Lang et al [6]
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