Coupling Habitat Radiomic Analysis with the Diversification of the Tumor ecosystem: Illuminating New Strategy in the Assessment of Postoperative Recurrence of Non-Muscle Invasive Bladder Cancer.

RATIONALE AND OBJECTIVES

Non-muscle-invasive bladder cancer (NMIBC) is highly recurrent, with each recurrence potentially progressing to muscle-invasive cancer, affecting patient prognosis. Intratumoral heterogeneity plays a crucial role in NMIBC recurrence. This study investigated a novel habitat-based radiomic analysis for stratifying NMIBC recurrence risk.

MATERIALS AND METHODS

A retrospective collection of 382 NMIBC patients between 2015 and 2021 from two medical institutions was carried out. Patients' CT images were collected across three phases, with tumor sites delineated within the bladder. Intratumoral habitats were identified using K-means clustering on 19 texture features of the tumor sites, followed by the extraction of 107 radiomic features per habitat with PyRadiomics. These features were integrated into machine learning algorithms to develop a habitat-based model (HBM) for predicting two-year recurrence of NMIBC patients. The clinical and multiphase radiomic models were also constructed for comparison, with the Delong test comparing their diagnostic efficiency. The impact of HMB on patients' recurrence-free survival and the correlation between HBM and tumor-stroma ratio were further analyzed.

RESULTS

Three distinct habitats were identified within NMIBC. The HBM showed an AUC of 0.932 (95% CI: 0.906 - 0.958) in the training cohort and 0.782 (95% CI: 0.674 - 0.890) in the validation cohort for predicting two-year recurrence. With comparison between different models, The HBM is demonstrated to possess superior diagnostic efficacy to the clinical model (p < 0.001) in the training cohort. However, no significant difference was noted between the multiphase and clinical models (p = 0.130) in the training cohort. The HBM score effectively distinguished the recurrence-free survival of NIMBC patients and demonstrated a significant correlation with the tumor-stroma ratio.

CONCLUSIONS

Habitat-based radiomics, coupled with machine learning, efficiently predicts NMIBC recurrence. Further research on habitat-based radiomics offers potential improvement in clinical management of NMIBC.