No abstract

The present study aimed to evaluate the efficacy of using the prostate imaging reporting and data system (PI-RADS) for the detection of prostate cancer (PCa) in the transitional zone (TZ) by 3T multiparametric magnetic resonance imaging (mpMRI), and to compare the diagnostic performance of PI-RADS V1 to V2 for the detection of PCa in the TZ. A total of 77 patients with suspicious lesions in the prostate TZ (83 cores) identified from mpMRI images acquired at 3T were scored per the PI-RADS system (V1 and V2) criteria. Magnetic resonance/transrectal ultrasound fusion-guided biopsy was performed in patients with at least one lesion classified as category ≥3 in the PI-RADS V1 assessment. The diagnostic performance of PI-RADS V1 for the detection of PCa in the TZ was compared with PI-RADS V2 by assessing the sensitivity, specificity and receiver operating characteristics. A total of 31 cases of PCa in the TZ and 46 cases of benign prostatic hyperplasia were confirmed by pathology, including 23 cases classified as Gleason score ≥7 and 54 cases of negative results and Gleason score 6. PI-RADS V2 exhibited a higher area under the curve (AUC, 0.888) compared with V1 (AUC, 0.869). The sensitivity of V2 (75.0%) was higher compared with that of V1 (68.8%), whereas the specificity of V2 (90.2%) was lower compared with that of V1 (96.1%) at PI-RADS scores of 11 and 4, respectively. The ESUR PI-RADS system may indicate the likelihood of PCa in suspicious lesions in the TZ on mpMRI. These results suggest that PI-RADS V2 performs better compared with V1 for the assessment of PCa in the TZ.

ObjectivesTo develop and validate a nomogram model based on radiomics features for preoperative prediction of visceral pleural invasion (VPI) in patients with lung adenocarcinoma.MethodsA total of 659 patients with surgically pathologically confirmed lung adenocarcinoma underwent CT examination. All cases were divided into a training cohort (n = 466) and a validation cohort (n = 193). CT features were analyzed by two chest radiologists. CT radiomics features were extracted from CT images. LASSO regression analysis was applied to determine the most useful radiomics features and construct radiomics score (radscore). A nomogram model was developed by combining the optimal clinical and CT features and the radscore. The model performance was evaluated using ROC analysis, calibration curve and decision curve analysis (DCA).ResultsA total of 1316 radiomics features were extracted. A radiomics signature model with a selection of the six optimal features was developed to identify patients with or without VPI. There was a significant difference in the radscore between the two groups of patients. Five clinical features were retained and contributed as clinical feature models. The nomogram combining clinical features and radiomics features showed improved accuracy, specificity, positive predictive value, and AUC for predicting VPI, compared to the radiomics model alone (specificity: training cohort: 0.89, validation cohort: 0.88, accuracy: training cohort: 0.84, validation cohort: 0.83, AUC: training cohort: 0.89, validation cohort: 0.89). The calibration curve and decision curve analyses suggested that the nomogram with clinical features is beyond the traditional clinical and radiomics features.ConclusionA nomogram model combining radiomics and clinical features is effective in non-invasively prediction of VPI in patients with lung adenocarcinoma.