PURPOSE

Sarcopenia has already been widely investigated as a potential indicator of negative outcomes in oncology patients. Our aim was to evaluate the potential predictive role of sarcopenia assessed using an Artificial Intelligence-powered software in response to neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC).

MATERIALS AND METHODS

In this single-centre retrospective study, we enrolled patients diagnosed with non-metastatic MIBC who underwent NAC and had available pre-treatment mpMRI of the bladder and baseline CT scan.

The follow-up MRI assessment was performed using the NacVI-RADS algorithm to evaluate response to systematic therapy. AI-based software automatically calculated the skeletal muscle index (SMI) from CT images at the L3 vertebral level.

Multivariate logistic regression analysis was performed to assess independent predictors of response to NAC, and a receiver operating characteristic (ROC) analysis was subsequently conducted to provide an additional level of statistical validation.

RESULTS

Fifty-five patients were included (mean age: 67.2 years). Sarcopenia was identified in 36.4% of patients.

Multivariate logistic regression revealed sarcopenia (OR: 9.08; 95% CI 1.32-61.92; p = 0.024), comorbidities (OR: 14.63; 95% CI 2.12-100.71; p = 0.006), and high NacVI-RADS scores (4-5) (OR = 2.13 95% CI 1.03-4.42; p = 0.042) as independent predictors of poor response to NAC. Receiver operating characteristic (ROC) curve analysis confirmed the high discriminative ability of SMI for predicting treatment response (AUC = 0.952).

CONCLUSION

Sarcopenia, assessed by AI-powered analysis, was negatively associated with tumor response following NAC in patients with MIBC.

These findings support the integration of AI-driven sarcopenia evaluation into clinical staging workflows, enabling tailored nutritional interventions and improved patient stratification. Moreover, our study reinforces the prognostic value of the NacVI-RADS scoring system in predicting NAC outcomes.