潰瘍型與非潰瘍型間質性膀胱炎患者之膀胱壁厚度、尿液生物標記與治療預後之關聯性研究

劉民慶¹、李雨霜、楊家誠、林琮翊、黃子修、張天霖、游婉茹¹、江元宏¹、郭漢崇¹

花蓮慈濟醫院 泌尿部

The association of Bladder Wall Thickness and Urine Biomarkers and Treatment

Outcome in Patients with Hunner's IC and Non-Hunner's IC

Min-Ching Liu, Yu-Shuang Lee, Chia-Cheng Yang, Tsu-Hsiu Huang, Tien-Lin Chang, Wan-Ru Yu, Jia‑Fong Jhang, Yuan‑Hong Jiang, Hann‑Chorng Kuo

Department of Urology, Hualien Tzu Chi Hospital, Hualien, Taiwan

Purpose: This original study investigates the clinical synergy between computed tomography (CT)-derived bladder wall thickness (BWT) and urinary biomarkers, a combination that remains unexplored for predicting therapeutic outcomes. Interstitial cystitis/bladder pain syndrome (IC/BPS) is highly heterogeneous¹. We hypothesize that a novel two-step predictive model, integrating morphological BWT classification with specific urinary biomarkers, can effectively sub-phenotype IC/BPS and predict treatment outcomes (Global Response Assessment, GRA). This study aims to shift the focus from static diagnosis to dynamic prognostic prediction, utilizing receiver operating characteristic (ROC) analysis to establish high-accuracy prognostic models for both Hunner's IC (HIC) and non-Hunner's IC (NHIC) patients.

Methods: This retrospective study analyzed 149 patients with cystoscopy-proven IC/BPS (123 NHIC and 26 HIC). Before treatment, all patients underwent pelvic CT scans to be categorized into two morphological subgroups: smooth and non-smooth (focal or diffuse thickening)2. Baseline urine samples were analyzed for 14 biomarkers: inflammatory cytokines and chemokines (IL-2, IL-6, IL-8, CXCL10, MCP-1, MIP-1β, RANTES, TNF-α, Eotaxin), neurotrophic factors (NGF, BDNF), oxidative stress markers (8-OHdG, 8-isoprostane), and prostaglandin E2 (PGE2). Treatment outcomes were evaluated using the GRA scale, comparing favorable responders (GRA > 1) versus poor responders (GRA ≤ 1) within BWT subgroups. ROC curves were generated to establish optimal predictive cut-off values and evaluate diagnostic performance, specifically within the pure NHIC non-smooth cohort.

Results: Morphologically, smooth BWT was observed exclusively in NHIC patients (n=71/123, 100% of smooth group), whereas all HIC patients exhibited non-smooth BWT (n=26/26). Urinary biomarkers successfully predicted outcomes only when stratified by BWT. In the non-smooth group (n=63), favorable responders exhibited significantly higher baseline IL-8 (p=0.004), MCP-1 (p=0.017), and PGE2 (p=0.017). Conversely, in the smooth group, all biomarkers failed to differentiate treatment responses (all p > 0.3). In the pure NHIC non-smooth subgroup (excluding HIC, n=45), ROC analysis revealed that lower BDNF and higher PGE2 were significant predictors of success (GRA > 1). BDNF yielded an AUC of 0.683 (cut-off ≤ 0.530, sensitivity 76.5%, specificity 64.3%), while PGE2 had an AUC of 0.727 (cut-off ≥ 46.61, sensitivity 70.6%, specificity 71.4%). The combined model (BDNF+PGE2) demonstrated the highest predictive value with an AUC of 0.758 (cut-off ≥ -0.678, sensitivity 76.5%, specificity 75.0%).

Conclusion: We propose a novel, non-invasive two-step predictive model utilizing CT-derived BWT and urinary biomarker analysis. By integrating ROC-validated thresholds, this model demonstrates that non-smooth BWT patients with high PGE2 and low BDNF exhibit excellent responses to bladder-directed treatments. This provides a robust, quantitative framework for guiding personalized, phenotype-directed therapy in clinical practice.