MRI radiomics predicts progression-free survival in prostate cancer

Abstract

ObjectiveTo assess the predictive value of magnetic resonance imaging (MRI) radiomics for progression-free survival (PFS) in patients with prostate cancer (PCa).Methods191 patients with prostate cancer confirmed by puncture biopsy or surgical pathology were included in this retrospective study, including 133 in the training group and 58 in the validation group. All patients underwent T2WI and DWI serial scans. Three radiomics models were constructed using univariate logistic regression and Gradient Boosting Decision Tree(GBDT) for feature screening, followed by Cox risk regression to construct a mixed model combining radiomics features and clinicopathological risk factors and to draw a nomogram. The performance of the models was evaluated by receiver operating characteristic curve (ROC), calibration curve and decision curve analysis. The Kaplan-Meier method was applied for survival analysis.ResultsCompared with the radiomics model, the hybrid model consisting of a combination of radiomics features and clinical data performed the best in predicting PFS in PCa patients, with AUCs of 0.926 and 0.917 in the training and validation groups, respectively. Decision curve analysis showed that the radiomics nomogram had good clinical application and the calibration curve proved to have good stability. Survival curves showed that PFS was shorter in the high-risk group than in the low-risk group.ConclusionThe hybrid model constructed from radiomics and clinical data showed excellent performance in predicting PFS in prostate cancer patients. The nomogram provides a non-invasive diagnostic tool for risk stratification of clinical patients.

Amide Proton Transfer-Weighted Magnetic Resonance Imaging for Application in Renal Fibrosis: A Radiological-Pathological-Based Analysis

“…More importantly, the APT value combined with the ADC value significantly improved the performance of fMRI in diagnosing RF, with an AUC of 0.93. This finding suggests that the combination of multiple fMRI sequence will further improve the diagnostic sensitivity of moderate-to-severe fibrosis [40‒42], with different MRI sequences perhaps reflecting different microscopic pathological changes. The combination of multiple MR parameters provides more comprehensive information on renal morphology, function, and tissue microstructure.…”

Research on texture images and radiomics in urology: a review of urological MR imaging applications

“…In addition, in the study of Yan et al a DL-based radiomics signature was developed and externally validated, displaying C-index of 0.802 in both primary and validating cohorts and outperformed psCAPRA score (0.677) [37]. Interestingly, Jia et al reported that their hybrid model (radiomics features /clinical data) performed the best in predicting PFS (AUCs: 0.926 and 0.917 for training and validation groups) [38]. More recently, An et al built a combined model (clinical data, contrast-enhanced ultrasound-CEUS-, MRI radiomics) showing the highest predictive value (AUC: 0.91 and 0.93 in external validation) compared to clinical or CEUS and MRI radiomics and a nomogram was constructed [39 ▪▪ ].…”

“…In addition, in the study of Yan et al a DL-based radiomics signature was developed and externally validated, displaying C-index of 0.802 in both primary and validating cohorts and outperformed psCAPRA score (0.677) [37]. Interestingly, Jia et al reported that their hybrid model (radiomics features /clinical data) performed the best in predicting PFS (AUCs: 0.926 and 0.917 for training and validation groups) [38]. More recently, An et al & ]) published between 2017 and 2020.…”

Robustness of magnetic resonance imaging and positron emission tomography radiomic features in prostate cancer: Impact on recurrence prediction after radiation therapy

“…Radiomics and artificial intelligence (AI) are emerging tools for implementing precision medicine in prostate cancer [1] , [2] , [3] . Previous studies have demonstrated the potential of pre-treatment MRI or PET-based radiomic features to predict biochemical recurrence (BCR) [4] , [5] , [6] , [7] , [8] , which can affect up to 50 % of patients within ten years of radiation therapy (RT) [3] . Radiomic features also hold promise to assess early post-RT treatment response accurately, complementing the standard blood-based prostate-specific-antigen (PSA) test [1] , [3] .…”