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Purpose <p>To develop and internally validate a PSMA PET/CT–centric deep-learning framework based on using a foundational model for noninvasive preoperative risk stratification of prostate cancer at the ISUP Grade Group (GG) 1–2 vs. 3–5 boundary, without using biopsy-derived inputs at inference.</p> Methods <p>In this single-center retrospective cohort, 494 men underwent [<sup>18</sup>F]PSMA-1007 PET/CT within one month before radical prostatectomy. Intraprostatic ROIs were manually delineated. We developed a dual-path hybrid model that fuses global semantic features from a frozen BiomedCLIP foundation model with task-specific 3D PET/CT features, and used the fused representation for GG 1–2 vs. 3–5 stratification. Five-fold, patient-level cross-validation was used; prostatectomy pathology served as the reference standard. Benchmarks included conventional radiomics and deep-learning baselines (BiomedCLIP-only, ResNet-only, MedSAM, XSurv). The primary metric was AUC; precision, recall, F1, PRC-AUC, and decision-curve analysis (DCA) assessed complementary performance and clinical utility.</p> Results <p>The proposed model achieved AUC 0.800 with precision 0.854, recall 0.888, and F1 0.870, outperforming baselines (BiomedCLIP 0.764; MedSAM 0.759; XSurv 0.756; ResNet 0.745; radiomics/random forest 0.676). Mean PRC-AUC was 0.928 ± 0.020 (across folds), and DCA showed higher net benefit across wide thresholds. SUVmax alone was modest (AUC 0.699 for ≥GG3; 0.625 for ≥GG4).</p> Conclusion <p>The framework demonstrated noninvasive discrimination of GG 1–2 vs. 3–5 in a single-center cohort, suggesting a candidate decision-support role for biopsy-sparing pathways; its generalizability and deployability require multicenter external validation and end-to-end automation.</p>

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Noninvasive preoperative risk stratification of prostate cancer via a foundational model based deep learning with PSMA PET/CT

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月9日

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  • Linjie Bian,
  • Fanxuan Liu,
  • Xuan Huang,
  • Yige Peng,
  • Yichong Fang,
  • Lei Bi,
  • Shaoli Song

摘要

Purpose

To develop and internally validate a PSMA PET/CT–centric deep-learning framework based on using a foundational model for noninvasive preoperative risk stratification of prostate cancer at the ISUP Grade Group (GG) 1–2 vs. 3–5 boundary, without using biopsy-derived inputs at inference.

Methods

In this single-center retrospective cohort, 494 men underwent [18F]PSMA-1007 PET/CT within one month before radical prostatectomy. Intraprostatic ROIs were manually delineated. We developed a dual-path hybrid model that fuses global semantic features from a frozen BiomedCLIP foundation model with task-specific 3D PET/CT features, and used the fused representation for GG 1–2 vs. 3–5 stratification. Five-fold, patient-level cross-validation was used; prostatectomy pathology served as the reference standard. Benchmarks included conventional radiomics and deep-learning baselines (BiomedCLIP-only, ResNet-only, MedSAM, XSurv). The primary metric was AUC; precision, recall, F1, PRC-AUC, and decision-curve analysis (DCA) assessed complementary performance and clinical utility.

Results

The proposed model achieved AUC 0.800 with precision 0.854, recall 0.888, and F1 0.870, outperforming baselines (BiomedCLIP 0.764; MedSAM 0.759; XSurv 0.756; ResNet 0.745; radiomics/random forest 0.676). Mean PRC-AUC was 0.928 ± 0.020 (across folds), and DCA showed higher net benefit across wide thresholds. SUVmax alone was modest (AUC 0.699 for ≥GG3; 0.625 for ≥GG4).

Conclusion

The framework demonstrated noninvasive discrimination of GG 1–2 vs. 3–5 in a single-center cohort, suggesting a candidate decision-support role for biopsy-sparing pathways; its generalizability and deployability require multicenter external validation and end-to-end automation.