Delta radiomic patterns on serial bi-parametric MRI are associated with pathologic upgrading in prostate cancer patients on active surveillance: preliminary findings

ObjectiveThe aim of this study was to quantify radiomic changes in prostate cancer (PCa) progression on serial MRI among patients on active surveillance (AS) and evaluate their association with pathologic progression on biopsy.MethodsThis retrospective study comprised N = 121 biopsy-proven PCa patients on AS at a single institution, of whom N = 50 at baseline conformed to the inclusion criteria. ISUP Gleason Grade Groups (GGG) were obtained from 12-core TRUS-guided systematic biopsies at baseline and follow-up. A biopsy upgrade (AS+) was defined as an increase in GGG (or in number of positive cores) and no upgrade (AS−) was defined when GGG remained the same during a median period of 18 months. Of N = 50 patients at baseline, N = 30 had MRI scans available at follow-up (median interval = 18 months) and were included for delta radiomic analysis. A total of 252 radiomic features were extracted from the PCa region of interest identified by board-certified radiologists on 3T bi-parametric MRI [T2-weighted (T2W) and apparent diffusion coefficient (ADC)]. Delta radiomic features were computed as the difference of radiomic feature between baseline and follow-up scans. The association of AS+ with age, prostate-specific antigen (PSA), Prostate Imaging Reporting and Data System (PIRADS v2.1) score, and tumor size was evaluated at baseline and follow-up. Various prediction models were built using random forest (RF) classifier within a threefold cross-validation framework leveraging baseline radiomics (Cbr), baseline radiomics + baseline clinical (Cbrbcl), delta radiomics (CΔr), delta radiomics + baseline clinical (CΔrbcl), and delta radiomics + delta clinical (CΔrΔcl).ResultsAn AUC of 0.64 ± 0.09 was obtained for Cbr, which increased to 0.70 ± 0.18 with the integration of clinical variables (Cbrbcl). CΔr yielded an AUC of 0.74 ± 0.15. Integrating delta radiomics with baseline clinical variables yielded an AUC of 0.77 ± 0.23. CΔrΔclresulted in the best AUC of 0.84 ± 0.20 (p < 0.05) among all combinations.ConclusionOur preliminary findings suggest that delta radiomics were more strongly associated with upgrade events compared to PIRADS and other clinical variables. Delta radiomics on serial MRI in combination with changes in clinical variables (PSA and tumor volume) between baseline and follow-up showed the strongest association with biopsy upgrade in PCa patients on AS. Further independent multi-site validation of these preliminary findings is warranted

Analysis of 2D singularities for mammographic mass classification

Masses are one of the prevalent early signs of breast cancer, visible in mammogram. However, its variation in shape, size, and appearance often creates hazards in proper diagnosis of mammographic masses. This study analyses the 2D singularities of masses and their surrounding regions with Ripplet‐II transform to classify them as benign and malignant. Since benign and malignant masses may change the orientation patterns of normal breast tissues differently, several textural features including Ripplet‐II coefficients and statistical co‐variates, derived from the Ripplet‐II transformed images, are extracted to quantify the texture information of mammographic regions. The important features are then selected using stepwise logistic regression technique and evaluated using linear discriminant analysis and support vector machine with a ten‐fold cross‐validation. The best performance in terms of the area under the receiver operating characteristic curve of 0.91 ± 0.01 and 0.83 ± 0.01 and accuracy of 87.28 ± 0.02 and 75.60 ± 0.01 are obtained with the proposed method while experimenting with 58 images from the mini‐MIAS and 200 images from the Digital Database for Screening Mammography database, respectively