Microenvironment at a Distance: Multi-Endocrine-Organ Radiomics to Identify Systemic Signatures in PSMA-Negative Prostate Cancer.

Background/

Introduction

Prostate cancer (PCa) is the most commonly diagnosed malignancy among men and remains a major cause of cancer-related mortality worldwide. We aimed to evaluate whether radiomic features extracted from normal endocrine organs, combined with clinical variables, could predict clinical progression in patients with PSMA-negative prostate cancer. Materials and

Methods

In this retrospective study, 101 men with biochemically recurrent prostate cancer and negative [18F]DCFPyL PET/CT scans were included. Radiomic features were extracted from the adrenal glands, thyroid, the hypothalamus-pituitary complex, and testes. Post-imaging variables were excluded to prevent temporal data leakage. Models were developed using a stratified train/test split framework with preprocessing and feature selection performed exclusively within the training subset prior to evaluation on the held-out test set. Performance was evaluated using AUC, accuracy, sensitivity, specificity, and Brier score, while bootstrap confidence intervals and DeLong analysis were used for statistical assessment.

Results

Multimodal fusion models integrating CT radiomics, PET radiomics, and clinical variables demonstrated the strongest predictive performance. The highest-performing model combined TESTIS_CT and TESTIS_PET radiomics with clinical variables, achieving an AUC of 0.758 (95% CI: 0.653-0.849). Clinical-only models remained highly competitive, with the best configuration achieving an AUC of 0.727 (95% CI: 0.618-0.833). PET + clinical and CT + clinical models achieved AUC values of up to 0.733 and 0.729, respectively, while imaging-only models demonstrated substantially lower discrimination. Although endocrine organ radiomics numerically improved predictive performance and specificity, DeLong analysis demonstrated no statistically significant improvement beyond clinical variables alone. Discussion: These findings suggest that endocrine organ radiomics may provide complementary system-level imaging biomarkers reflecting tumor-host interactions in PSMA-negative prostate cancer. However, their incremental clinical value remains modest.

Conclusions

Endocrine organ radiomics combined with clinical variables demonstrated promising predictive performance in PSMA-negative prostate cancer, particularly in multimodal fusion models. Nevertheless, the added value beyond clinical variables alone was not statistically significant and requires validation in larger independent cohorts.