Search

The aim of the study was to analyse the effects of dynamic contrast enhanced (DCE)-MRI on transitional-zone prostate cancer (tzPCa) and clinically significant transitional-zone prostate cancer (cs-tzPCa) in Prostate Imaging Reporting and Data System (PI-RADS) Version 2.1.

Objective To investigate the feasibility of the CT-based radiomics model to predict the recurrence of bladder cancer in one year postoperatively. Methods Patients with bladder cancer that received surgical treatment in Peking Union Medical College Hospital from May 2014 to July 2018 were retrospectively enrolled and followed up the recurrence of the disease. Nephrographic phase images of preoperative CT urography(CTU) performed in our hospital were collected. The images were filtered before radiomic feature extraction, and JMIM was used to identify the best radiomic features related to recurrence of bladder cancer. Random forest, AdaBoost, gradient boosting, and their combined model were used to build the model for predicting recurrence of bladder cancer after resection in one year. We applied 10-fold cross validation to validate each model and performed receiver operator characteristic curves to analyze the performance of each model. Results A total of 228 cases were included in this study according to inclusion and exclusion criteria. Fifty-one patients had recurrence and the rest 177 patients had no recurrence in one year during postoperative follow-up. In the cross validation, the random forest model, AdaBoost model, gradient boosting model and combined model predicted the recurrence of bladder cancer with AUC of 0.729(95% CI: 0.649-0.809), 0.710(95% CI: 0.627-0.793), 0.709(95% CI: 0.624-0.793)and 0.732(95% CI: 0.651-0.812), accuracy of 76.8%(95% CI: 70.6%-82.0%), 73.7%(95% CI: 67.4%-79.2%), 61.8%(95% CI: 54.7%-67.7%)and 75.0%(95% CI: 68.8%-80.4%), sensitivity of 52.9%(95% CI: 38.6%-66.8%), 62.7%(95% CI: 48.1%-75.5%), 80.4%(95% CI: 64.3%-88.2%)and 58.8%(95% CI: 44.2%-72.1%), specificity of 83.6%(95% CI: 77.1%-88.6%), 76.8%(95% CI: 69.8%-82.7%), 56.5%(95% CI: 48.9%-63.9%)and 79.7%(95% CI: 72.8%-85.2%), respectively. Conclusion Integration of CT-based radiomics prediction models can predict the recurrence risk of bladder cancer in one year postoperatively.

Background: Whether men with a prostate-specific antigen (PSA) level of 4-10 ng/mL should be recommended for a biopsy is clinically challenging.Purpose: To develop and validate a radiomics model based on multiparametric MRI (mp-MRI) in patients with PSA levels of 4-10 ng/mL to predict prostate cancer (PCa) preoperatively and reduce unnecessary biopsies.Study Type: Retrospective.Subjects: In all, 199 patients with PSA levels of 4-10 ng/mL.Field Strength/sequence: 3T, T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI.Assessment: Lesion regions of interest (ROIs) from T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI were annotated by two radiologists. A total of 2104 radiomic features were extracted from the ROI of each patient. A random forest classifier was used to build the radiomics model for PCa in the primary cohort. A combined model was constructed using multivariate logistic regression by incorporating the radiomics signature and clinical-radiological risk factors.Statistical Tests: For continuous variables, variance equality was assessed by Levene's test and Student's t-test, and Welch's t-test was used to assess between-group differences. For categorical variables, Pearson's chi-square test, Fisher's exact test, or the approximate chi-square test was used to assess between-group differences. P < 0.05 was considered statistically significant.Results: The combined model incorporating the multi-imaging fusion model, age, PSA density (PSAD), and the PI-RADS v2 score yielded area under the curve (AUC) values of 0.956 and 0.933 on the primary (n = 133) and validation (n = 66) cohorts, respectively. Compared with the clinical-radiological model, the combined model performed better on both the primary and validation cohorts (P < 0.05). Furthermore, the use of the combined model to predict PCa could identify more negative PCa patients than the use of the clinical-radiological model by 18.4%.Data Conclusion: The combined model was developed and validated to provide potential preoperative prediction of PCa in men with PSA levels of 4-10 ng/mL and might aid in treatment decision-making and reduce unnecessary biopsies.Level Of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1890-1899. [ABSTRACT FROM AUTHOR]

Objective To investigate the feasibility of using low-dose prostate CT perfusion (pCTP)on a third-generation dual-source CT. Methods Nine patients with elevated prostate-specific antigen underwent pCTP before having prostate biopsy. We measured the blood flow (BF),blood volume (BV),mean transit time (MTT),permeability surface (PS),and time to peak(TTP)of both lesions and normal prostate tissue. The effective dose (ED)was calculated. Results Of the 9 cases,6 were prostate cancers and 3 were prostate hyperplasia with chronic inflammation. The average ED of the 9 pCTPs was (3.5±0.3)mSv. The BF (t=4.64,P<0.001),BV (t=3.27,P<0.001),and PS (t=3.58,P=0.004)of prostate cancer were significantly higher than those of normal prostate tissue and TTP (t=-1.26,P<0.001)of prostate cancer was significantly lower than that of normal prostate tissue. BF (t=3.96,P=0.001)and PS (t=2.91,P=0.021)of prostate hyperplasia with chronic inflammation were also significantly higher and TTP (t=-1.19,P<0.001)was significantly lower than those of normal prostate tissue. TTP of prostate cancer was significantly lower than that of prostate hyperplasia with chronic inflammation (t=-2.56,P=0.049). Conclusion sLow-dose pCTP is feasible on third-generation dual-source CT. The BF,PS,and TTP differ among prostate cancer,prostate hyperplasia with chronic inflammation,and normal prostate tissue.