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Microenvironment at a Distance: Multi-Endocrine-Organ Radiomics to Identify Systemic Signatures in PSMA-Negative Prostate Cancer
Journal article   Open access   Peer reviewed

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

Hamid Abdollahi, Sara Harsini, Fereshteh Yousefirizi, Bahareh Hatami, François Bénard, Ahmad Shariftabrizi, Ian Alberts and Arman Rahmim
Cancers, Vol.18(11), 1767
05/28/2026
DOI: 10.3390/cancers18111767
PMCID: PMC13255653
PMID: 42279350
url
https://doi.org/10.3390/cancers18111767View
Published (Version of record) Open Access

Abstract

This study evaluated endocrine organ radiomics from [18F]DCFPyL PET/CT for predicting clinical progression in PSMA-negative prostate cancer. Multimodal models integrating CT, PET, and clinical variables achieved the best predictive performance, although clinical-only models remained highly competitive. Endocrine organ radiomics demonstrated complementary but modest predictive value beyond conventional clinical variables, supporting their exploratory role as potential system-level biomarkers of tumor–host interaction. 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_(C)T and TESTIS_(P)ET 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.
 PSMA-negative prostate cancer radiomics PET/CT endocrine system clinical progression

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