Predicting Model of Biochemical Recurrence of Prostate Carcinoma (PCa-BCR) Using MR Perfusion-Weighted Imaging-Based Radiomics

Abstract

Objective To build a combined model that integrates clinical data, contrast-enhanced ultrasound, and magnetic resonance perfusion-weighted imaging-based radiomics for predicting the possibility of biochemical recurrence of prostate carcinoma and develop a nomogram tool. Method We retrospectively analyzed the clinical, ultrasound, and magnetic resonance imaging data of 206 patients pathologically confirmed with prostate carcinoma and receiving radical prostatectomy at Xiangyang No. 1 People’s Hospital from February 2015 to August 2021. Based on one to 7 years of follow-up (prostate specific antigen [PSA] level≥0.2 ng/mL, indicative of prostate carcinoma–biochemical recurrence), the patients were divided into biochemical recurrence group (n = 77) and normal group (n = 129). The training and testing sets were formed by dividing the patients at a 7:3 ratio. In training set, The magnetic resonance perfusion-weighted imaging–based radiomics radscore was generated using lasso regression. Several predictive models were built based on the patients’ clinical imaging data. The predictive efficacy (area under the curve) of these models was compared using the MedCalc software. The decision curve analysis was conducted using the R to compare the net benefit. Finally, an external validation was carried out on the testing set, and the nomogram tool was developed for predicting prostate carcinoma–biochemical recurrence. Result The univariate analysis confirmed that Tumor diameter, tumor node metastasis classification stage of tumor, lymph node metastasis or distance metastasis, Gleason grade, preoperative PSA, ultrasound (peak intensity, arrival time, and elastography grade), and magnetic resonance imaging-radscore1/2 were predictors of prostate carcinoma–biochemical recurrence. On the training set, the combined model based on the above factors had the highest predictive efficacy for prostate carcinoma–biochemical recurrence (area under the curve: 0.91; odds ratio 0.02, 95% confidence interval: 0.85-0.95). The predictive performance of the combined model was significantly higher than that of the model based on general clinical data (area under the curve: 0.74; odds ratio 0.04, 95% confidence interval: 0.67-0.81, P < .05), contrast-enhanced ultrasound (area under the curve: 0.61; odds ratio 0.05 95% confidence interval: 0.53-0.69, P < .05), and the magnetic resonance imaging–based radiomics model (area under the curve: 0.85; odds ratio 0.03, 95% confidence interval: 0.78-0.91, P = .01). The decision curve analysis also indicated the maximum net benefit derived from the combined model, which agreed with the validation results on the testing set. The nomogram tool developed based on the combined model achieved a good performance in clinical applications. Conclusion The magnetic resonance imaging texture parameters extracted by magnetic resonance perfusion-weighted imaging Lasso regression could help increase the accuracy of the predictive model. The combined model and the nomogram tool provide support for the clinical screening of the populations at a risk for biochemical recurrence.

Deep learning-based radiomics model from pretreatment ADC to predict biochemical recurrence in advanced prostate cancer

“…MR radiomics models have demonstrated diverse applications in the detection, classification and management of PCa ( 28 , 29 ). However, studies specifically focusing on radiomics models for predicting BCR remain limitation and exist inconsistency in the methodologies ( 13 – 18 ). A review of literature indicates that a variety of MR imaging sequences were used in BCR prediction models, such as MR perfusion ( 13 ), T2WI ( 15 , 17 ), ADC maps ( 14 ), a combination of T2WI and ADC maps ( 14 , 16 ) and combinations of T1WI with T2WI and DWI ( 14 , 16 ).…”

“…However, studies specifically focusing on radiomics models for predicting BCR remain limitation and exist inconsistency in the methodologies ( 13 – 18 ). A review of literature indicates that a variety of MR imaging sequences were used in BCR prediction models, such as MR perfusion ( 13 ), T2WI ( 15 , 17 ), ADC maps ( 14 ), a combination of T2WI and ADC maps ( 14 , 16 ) and combinations of T1WI with T2WI and DWI ( 14 , 16 ). In these studies, different types of ROI were annotated, encompassing the prostate gland ( 13 , 17 , 18 ), prostate with an expanded margin ( 15 ) and prostate tumor ( 14 – 16 ).…”

“…A review of literature indicates that a variety of MR imaging sequences were used in BCR prediction models, such as MR perfusion ( 13 ), T2WI ( 15 , 17 ), ADC maps ( 14 ), a combination of T2WI and ADC maps ( 14 , 16 ) and combinations of T1WI with T2WI and DWI ( 14 , 16 ). In these studies, different types of ROI were annotated, encompassing the prostate gland ( 13 , 17 , 18 ), prostate with an expanded margin ( 15 ) and prostate tumor ( 14 – 16 ). Notably, manual annotation methods were predominantly employed for ROI delineation ( 13 , 16 – 18 ) and a small number of studies utilized semi-automatic annotation techniques ( 14 , 15 ).…”

“…In these studies, different types of ROI were annotated, encompassing the prostate gland ( 13 , 17 , 18 ), prostate with an expanded margin ( 15 ) and prostate tumor ( 14 – 16 ). Notably, manual annotation methods were predominantly employed for ROI delineation ( 13 , 16 – 18 ) and a small number of studies utilized semi-automatic annotation techniques ( 14 , 15 ). These MR radiomics models demonstrated AUC values ranging from 0.63 to 0.85 for the prediction of BCR ( 13 – 18 ).…”

“…Notably, manual annotation methods were predominantly employed for ROI delineation ( 13 , 16 – 18 ) and a small number of studies utilized semi-automatic annotation techniques ( 14 , 15 ). These MR radiomics models demonstrated AUC values ranging from 0.63 to 0.85 for the prediction of BCR ( 13 – 18 ). Our study demonstrated that the three models had predictive performance (AUCs ranging from 0.717 to 0.954) comparable to or slightly better than previous studies.…”