No abstract

Background Biopsy Gleason score (GS) is crucial for prostate cancer (PCa) treatment decision‐making. Upgrading in GS from biopsy to radical prostatectomy (RP) puts a proportion of patients at risk of undertreatment. Purpose To develop and validate a radiomics model based on multiparametric magnetic resonance imaging (mp‐MRI) to predict PCa upgrading. Study Type Retrospective, radiomics. Population A total of 166 RP‐confirmed PCa patients (training cohort, n = 116; validation cohort, n = 50) were included. Field Strength/Sequence 3.0T/T2‐weighted (T2W), apparent diffusion coefficient (ADC), and dynamic contrast enhancement (DCE) sequences. Assessment PI‐RADSv2 score for each tumor was recorded. Radiomic features were extracted from T2W, ADC, and DCE sequences and Mutual Information Maximization criterion was used to identify the optimal features on each sequence. Multivariate logistic regression analysis was used to develop predictive models and a radiomics nomogram and their performance was evaluated. Statistical Tests Student's t or chi‐square were used to assess the differences in clinicopathologic data between the training and validation cohorts. Receiver operating characteristic (ROC) curve analysis was performed and the area under the curve (AUC) was calculated. Results In PI‐RADSv2 assessment, 67 lesions scored 5, 70 lesions scored 4, and 29 lesions scored 3. For each sequence, 4404 features were extracted and the top 20 best features were selected. The radiomics model incorporating signatures from the three sequences achieved better performance than any single sequence (AUC: radiomics model 0.868, T2W 0.700, ADC 0.759, DCE 0.726). The combined mode incorporating radiomics signature, clinical stage, and time from biopsy to RP outperformed the clinical model and radiomics model (AUC: combined model 0.910, clinical model 0.646, radiomics model 0.868). The nomogram showed good performance (AUC 0.910) and calibration (P‐values: training cohort 0.624, validation cohort 0.294). Data Conclusion Radiomics based on mp‐MRI has potential to predict upgrading of PCa from biopsy to RP. Level of Evidence 3 Technical Efficacy Stage 5 J. Magn. Reson. Imaging 2020;52:1239–1248.

Purpose: To develop a model to select appropriate candidates for irradiation stent placement among patients with unresectable pancreatic cancer with malignant biliary obstruction (UPC-MBO).Methods: This retrospective study included 106 patients treated with an irradiation stent for UPC-MBO. These patients were randomly divided into a training group (74 patients) and a validation group (32 patients). A clinical model for predicting restenosis-free survival (RFS) was developed with clinical predictors selected by univariate and multivariate analyses. After integrating the radiomics signature, a combined model was constructed to predict RFS. The predictive performance was evaluated with the concordance index (C-index) in both the training and validation groups. The median risk score of progression in the training group was used to divide patients into high- and low-risk subgroups.Results: Radiomics features were integrated with clinical predictors to develop a combined model. The predictive performance was better in the combined model (C-index, 0.791 and 0.779 in the training and validation groups, respectively) than in the clinical model (C-index, 0.673 and 0.667 in the training and validation groups, respectively). According to the median risk score of 1.264, the RFS was significantly different between the high- and low-risk groups (p < 0.001 for the training group, and p = 0.016 for the validation group).Conclusions: The radiomics-based model had good performance for RFS prediction in patients with UPC-MBO who received an irradiation stent. Patients with slow progression should consider undergoing irradiation stent placement for a longer RFS.