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Radiomics of Periprostatic Fat and Tumor Lesion Based on MRI Predicts the Pathological Upgrading of Prostate Cancer
Wen-Qi Liu1, Yong Wei1, Zhi-Bin Ke1
1Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 35005, China (W-Q.L., Y.W., Z-B.K., B.L., X-H.W., X-Y.H., Z-J.C., J-Y.C., S-H.C., Y-T.X., F.L., D-N.C., Q-S.Z., X-Y.X., N.X.); Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China (W-Q.L., Y.W., Z-B.K., B.L., X-H.W., X-Y.H., Z-J.C., J-Y.C., S-H.C., Y-T.X., F.L., D-N.C., Q-S.Z., X-Y.X., N.X.).
Rationale And Objectives:
To assess the predictive value of MRI-based radiomics of periprostatic fat (PPF) and tumor lesions for predicting Gleason score (GS) upgrading from biopsy to radical prostatectomy (RP) in prostate cancer (PCa).
Methods:
A total of 314 patients with pathologically confirmed prostate cancer (PCa) after radical prostatectomy (RP) were included in the study. The patients were randomly assigned to the training cohort (n = 157) and the validating cohort (n = 157) in a 1:1 ratio. All had pre-surgery MRI followed by transrectal ultrasound-guided prostate biopsy. Radiological features were extracted from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences for PPF and tumors. Univariate and multivariate logistic regression identified independent clinical risk factors, and a combined model was established by integrating radiomic features of PPF and PCa. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis.
Results:
The combined model, incorporating radiomic features of PPF, PCa, and clinical data, predicted GS upgrading from biopsy to RP excellently (AUC=0.925, 95%CI0.872-0.979) in the training cohort. The Hosmer-Lemeshow test confirmed model fit (χ2 = 9.316, P = 0.316). The nomogram was validated in the validating cohort; it showed good accuracy (AUC= 0.937, 95% CI, 0.891-0.983) and was well calibrated (χ2 = 12.871, P = 0.116). Decision curve analysis indicated good clinical utility of the radiomic nomogram.
Conclusion:
The combined model incorporating PPF, PCa, and clinical data showed excellent performance in predicting GS upgrading from biopsy to RP in PCa patients. This offers a novel and reliable noninvasive tool for GS upgrading risk stratification.
Insights
This study developed a novel MRI radiomics model using periprostatic fat and prostate cancer features to accurately predict Gleason score upgrading. The model offers a reliable, noninvasive tool for risk stratification in prostate cancer patients.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Gleason score (GS) upgrading from biopsy to radical prostatectomy (RP) is common in prostate cancer (PCa).
- Accurate prediction of GS upgrading is crucial for treatment planning and patient management.
- Current methods rely on invasive procedures or lack predictive power.
Purpose of the Study:
- To evaluate the predictive capability of MRI-based radiomics of periprostatic fat (PPF) and tumor lesions for GS upgrading.
- To develop and validate a combined model integrating radiomic and clinical data for predicting GS upgrading.
Main Methods:
- A cohort of 314 PCa patients undergoing RP was analyzed.
- Radiomic features were extracted from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences.
- A combined logistic regression model incorporating radiomic and clinical data was developed and validated.
Main Results:
- The combined model demonstrated excellent performance in predicting GS upgrading (AUC=0.925 in training, 0.937 in validation).
- The model showed good calibration and clinical utility as assessed by decision curve analysis.
- The developed radiomic nomogram proved to be accurate and reliable.
Conclusions:
- MRI-based radiomics of PPF and PCa, combined with clinical data, effectively predicts GS upgrading.
- This approach provides a novel, noninvasive tool for risk stratification in PCa patients.
- The findings support the use of radiomics for improved pre-operative assessment in prostate cancer.
