ArticleBMC cancer2024
Periprostatic fat magnetic resonance imaging based radiomics nomogram for predicting biochemical recurrence-free survival in patients with non-metastatic prostate cancer after radical prostatectomy.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiomics as a tool for predicting biochemical recurrence after total prostatectomy: a systematic review and meta-analysis.International urology and nephrology · 2026Pooled it
- MRI-based radiomics for prediction of biochemical recurrence in prostate cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- mpMRI-based clinic-radiomics-deep learning model integrating lesion and PPAT for predicting csPCa in PI-RADS category 3 lesions: a multicenter study.International urology and nephrology · 2026Article
- SARIFA and Lipid Metabolic Reprogramming in Prostate Cancer: Fundamental Mechanisms, Tumor Microenvironment, and Novel Biomarker Prospects.Life (Basel, Switzerland) · 2026Review
- Development and Validation of a Multimodal AI-Based Model for Predicting Post-Prostatectomy Treatment Outcomes from Baseline Biparametric Prostate MRI.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence Versus Conventional Methods for NCCN Risk Stratification in Localized Prostate Cancer (2020-2025): A Systematic Review.Prostate cancer · 2026Review
- CT-based radiomics model for noninvasive prediction of progression-free survival in high-grade serous ovarian carcinoma: a multicenter study incorporating preoperative and postoperative clinical factors.BMC medical imaging · 2025Article
- Radiologic Assessment of Periprostatic Fat as an Indicator of Prostate Cancer Risk on Multiparametric MRI.Bioengineering (Basel, Switzerland) · 2025Article
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Abstract
objectiveTo build and validate a periprostatic fat magnetic resonance imaging (MRI) based radiomics nomogram for prediction of biochemical recurrence-free survival (bRFS) of patients with non-metastatic prostate cancer (PCa) receiving radical prostatectomy (RP).
methodsA retrospective study was conducted on 356 patients with non-metastatic PCa who underwent preoperative mpMRI followed by RP treatment at our institution. Radiomic features were extracted from both intratumoral region and the periprostatic fat region, which were segmented on images obtained through T2-weighted imaging (T2WI) and apparent-diffusion coefficient (ADC) imaging. Three radiomics models were developed by applying the Least absolute shrinkage and selection operator (LASSO) Cox regression, followed by Cox risk regression to construct a combined radiomics-clinical model by integrating the optimal radiomics score and clinicopathological risk factors to draw a nomogram. The predictive performance was evaluated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis and calibration curves.
resultsOne hundred and twenty-one patients (33.98%) experienced biochemical recurrence. ROC analyses showed that the Area Under the Curve (AUC) of the periprostatic fat-intratumoral radiomics model demonstrated the highest AUC at 0.921 (95%CI, 0.857-0.981), 0.875 (95%CI, 0.763-0.950), 0.854 (95%CI, 0.706-0.923) for 1-year, 3-years and 5-years bRFS. Multivariate Cox regression analysis revealed that Pathological T stage, ISUP grading group and Positive surgical margin were independent prognostic factors for predicting bRFS. A radiomics-clinical nomogram based on these clinical predictors and periprostatic fat-intratumoral radiomics score was constructed. Kaplan-Meier analyses showed that radiomics-clinical nomogram was significantly related with survival of PCa (P < 0.001); and calibration curves revealed the predicted and observed survival probability of 1-year, 3-year and 5-year bRFS had high degree of consistency in the training and validation group. The radiomics-clinical nomogram showed a significant improvement than the clinical model for 1-year (AUC, 0.944; 95%CI, 0.912-0.990 vs. AUC, 0.839; 95%CI, 0.661-0.928; P = 0.009), 3-year (AUC, 0.864; 95%CI, 0.772-0.969 vs. AUC, 0.776; 95%CI, 0.602-0.872; P = 0.008), and 5-year bRFS (AUC, 0.907; 95%CI, 0.836-0.982 vs. AUC, 0.819; 95%CI, 0.687-0.915; P = 0.027).
conclusionsThis study developed and validated the radiomics-clinical nomogram for the prediction of bRFS in non-metastatic PCa patients underwent RP.
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