ABSTRACT
Objective:To construct a clinical-radiomics model based on MRI, and to explore its predictive value for biochemical recurrence (BCR) after radical prostatectomy in prostate cancer patients.Methods:A total of 212 patients with prostate cancer who underwent radical prostatectomy in the First Affiliated Hospital of Soochow University from January 2015 to December 2018 and had complete follow-up data were retrospectively analyzed. The random toolkit of Python language was used to randomly sample the patients at a ratio of 7∶3 without replacement, and they were divided into a training set (149 cases) and a test set (63 cases). The endpoint of follow-up was BCR or at least 3 years. BCR occurred in 50 patients in the training group and 21 patients in the test group. The imaging features of the main lesion area in the preoperative T 2WI, diffusion-weighted imaging and apparent diffusion coefficient map of patients in the training set were extracted, and the unsupervised K means clustering algorithm was used to screen the features. The selected features were fitted by a multivariate Cox regression model, and the radiomics model was constructed. Univariate Cox regression analyses were used to screen the main clinical risk factors associated with BCR, and the clinical-radiomics model was constructed combined with RadScore. In the test set, the time-dependent receiver operating characteristic (ROC) curve was constructed, and the area under the curve (AUC) was calculated to evaluate the predictive efficacy of the radiomics model, clinical-radiomics model and prostate cancer risk assessment after radical resection (CAPRA-S) score for the occurrence of BCR. Harrell consistency index (C-index) was used to evaluate the model to predict BCR consistency. The calibration curve was used to evaluate the degree of variation of the model. The decision curve was used to evaluate the clinical application value of the prediction model. Results:A total of 26 radiomics features were screened to establish the radiomics model. The univariate Cox showed that the preoperative clinical features included preoperative prostate-specific antigen level (HR=1.006, 95%CI 1.002-1.009, P=0.001), Gleason score of biopsy (HR=1.422, 95%CI 1.153-1.753, P=0.001), clinical T stage (HR=1.501, 95%CI 1.238-1.822, P<0.001). The multivariate Cox showed that the RadScore was an independent predictor of BCR after radical prostatectomy (HR=51.214, 95%CI 18.226-143.908, P<0.001). The selected preoperative clinical features were combined with RadScore to construct a clinical-radiomics model. In the test set, the AUCs of the time (3 years)-dependent ROC curves of the radiomics model, the clinical-radiomics model, and the CAPRA-S score were 0.824 (95%CI 0.701-0.948), 0.841 (95%CI 0.714-0.968), and 0.662 (95%CI 0.518-0.806), respectively. The C-index of the radiomics model, clinical-radiomics model and CAPRA-S score were 0.784 (95%CI 0.660-0.891), 0.802 (95%CI 0.637-0.912) and 0.650 (95%CI 0.601-0.821), respectively. The calibration curve showed that the predicted probability and actual probability of BCR by radiomics model, clinical-radiomics model and CAPRA-S score were in good agreement (χ 2=7.64, 10.61, 6.37, P=0.465, 0.225, 0.498). The decision curve showed that the clinical net benefit of the clinical-radiomics model and the radiomics model was significantly higher than the CAPRA-S score. When the threshold probability was 0.20-0.30, 0.40-0.50, and >0.55, the clinical net benefit of the clinical radiomics model was higher than that of the radiomics model. Conclusions:The clinical-radiomics model can effectively predict the occurrence of BCR in patients with prostate cancer after radical prostate ctomy, and the prediction efficacy is better than the radiomics model and CAPRA-S score.
ABSTRACT
Objective:To evaluate the clinical feasibility and image quality of three-dimensional breath-hold gradient and spin-echo (3D BH-GRASE) sequence in magnetic resonance cholangiopancreatography (MRCP).Methods:In this prospective study, 59 patients with clinically suspected pancreaticobiliary duct disease performed MRCP with both 3D BH-GRASE and 3D respiration-triggered turbo spin-echo (3D RT-TSE) sequences on 3.0 T scanner in the Affiliated Zhangjiagang Hospital of Soochow University from November 2017 to December 2018. The overall image quality was scored independently by 3 experienced radiologists based on the visibility of different anatomical features of the pancreaticobiliary duct. For comparing the 2 sequences, the statistical difference in scan time was assessed with a paired t test; while subjective scores, signal-to-noise ratios (SNR), contrast ratios (CR) and contrast noise ratios (CNR) were compared with Wilcoxon signed rank test. Results:The scan time of 3D BH-GRASE sequence was 16.4 s while that of 3D RT-TSE was (258.6±42.2) s. Their difference was statistically significant ( t=44.073, P<0.001), with the scan time for 3D BH-GRASE shortened by 94%. The overall quality scores of 3D BH-GRASE images were better than those of 3D RT-TSE ( Z=-6.595, P<0.001). There was no statistical difference ( P>0.05) in the scores regarding the visibility of the upper, middle and lower parts of common bile duct and the first and second branches of left and right hepatic ducts. For visualizing the bottom, body, neck and duct of gallbladder, the 3D BH-GRASE sequence received a higher score than the 3D RT-TSE sequence ( P<0.001). For displaying the proximal, middle and distal segments of main pancreatic duct, the 3D RT-TSE sequence was scored higher than the 3D BH-GRASE sequence ( P<0.05). There was no significant difference of SNR between the two sequences ( Z=0.403, P=0.687), whereas CR and CNR of 3D RT-TSE MRCP were better than those of 3D BH-GRASE MRCP ( Z=6.215, P<0.001 and Z=3.046, P=0.002, respectively). Conclusion:Under the prerequisite of ensuring image quality, a proper use of 3D BH-GRASE sequence can significantly shorten the scan time and thus greatly improve the working efficiency of MRCP examination.