What question did this study set out to answer?

The research aimed to determine if AI-assessed sarcopenia can independently predict outcomes of neoadjuvant chemotherapy in muscle-invasive bladder cancer patients.

April 1, 2026Open Access

AI-assessed sarcopenia as an independent predictor of neoadjuvant chemotherapy outcomes in muscle-invasive bladder cancer

Key Points

The research aimed to determine if AI-assessed sarcopenia can independently predict outcomes of neoadjuvant chemotherapy in muscle-invasive bladder cancer patients.
Conducted a single-centre retrospective analysis of patients with non-metastatic muscle-invasive bladder cancer who underwent neoadjuvant chemotherapy.
Used AI software to calculate the skeletal muscle index from CT images at the L3 vertebral level.
Performed multivariate logistic regression to identify predictors for chemotherapy response.
Utilized receiver operating characteristic analysis to validate predictive performance.
Sarcopenia was identified in 36.4% of patients enrolled in the study.
Logistic regression indicated that sarcopenia (OR: 9.08), comorbidities (OR: 14.63), and high NacVI-RADS scores (OR: 2.13) were independent predictors of poor chemotherapy response.
Receiver operating characteristic analysis resulted in an AUC of 0.952 for skeletal muscle index, demonstrating high accuracy in predicting treatment response.

Abstract

Abstract 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.

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Cite This Study

Borrelli et al. (Tue,) studied this question.

synapsesocial.com/papers/69cd7aa45652765b073a7fb8 https://doi.org/https://doi.org/10.1007/s11547-026-02203-2

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