Fibrosis of Periprostatic Adipose Tissue: A Potential Marker of Prostate Cancer Aggressiveness

TL;DR

This study shows that fibrosis in the fat tissue around the prostate could help predict how aggressive prostate cancer is, using MRI scans for non-invasive diagnosis.

Contribution

The first study to show a significant link between PPAT fibrosis and tumor location using MRI-based radiomics for non-invasive prostate cancer aggressiveness prediction.

Findings

PPAT fibrosis correlates with prostate cancer aggressiveness and tumor location in the peripheral zone.

MRI-based radiomic features of PPAT achieved an AUC of 0.86 in predicting cancer aggressiveness.

PPAT fibrosis is a better marker of aggressiveness than PPAT volume.

Abstract

This study investigated the feasibility of using periprostatic adipose tissue (PPAT) fibrosis as a potential marker of prostate cancer aggressiveness. A quantitative analysis of PPAT fibrosis was conducted on samples from 51 patients who underwent radical prostatectomy. The results revealed that more aggressive tumors were associated with increased complexity in the fibrous structure of PPAT, including an increased number of fibers, disorganized distribution and altered physical properties. This is the first study to report a significant correlation between the degree of PPAT fibrosis and the primary tumor location in prostate cancer. This study further validated the feasibility of evaluating PPAT fibrosis via MRI-based radiomic features, thus suggesting its potential as a noninvasive method to facilitate early diagnosis and personalized treatment of prostate cancer.

Background: Periprostatic adipose tissue (PPAT) fibrosis is a histological feature potentially linked to prostate cancer (PCa) aggressiveness, though its role is not fully understood. This study investigates the correlation between PPAT fibrosis and PCa aggressiveness and develops a radiomics model based on PPAT MRI features for non-invasive prediction. Methods: This retrospective study included 51 patients who underwent radical prostatectomy. PPAT samples were collected, stained with Sirius Red and quantitatively evaluated for fibrosis using 12 indices via 3D reconstruction with Imaris software. Patients were stratified by cancer aggressiveness based on Grade Groups. Radiomic features were extracted from T1-weighted MRIs of the PPAT. An XGBoost model was developed to predict aggressiveness using these features. Results: Significant correlations were found between multiple PPAT fibrosis indices and PCa aggressiveness (p < 0.05), with more aggressive tumors showing increased fiber complexity. PPAT fibrosis was also significantly associated with primary tumor location in the peripheral zone (p < 0.05). Conversely, PPAT volume showed no significant correlation with aggressiveness (p = 0.616). The radiomics model based on PPAT features achieved an AUC of 0.86 in predicting cancer aggressiveness. Conclusions: PPAT fibrosis is a promising marker of PCa aggressiveness, superior to PPAT volume. The significant link with tumor location provides new insights into the tumor microenvironment (TME). MRI-based radiomics of PPAT offers a potential non-invasive method for assessing fibrosis and aggressiveness, aiding in early diagnosis and personalized treatment.

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