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

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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Journal
La radiologia medica
Published
2026-03-31
DOI
https://doi.org/10.1007/s11547-026-02203-2
Primary Topic
Nutrition and Health in Aging
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Ludovica Laschena, Antonella Borrelli, Ailin Dehghanpour, Emanuele Messina et al.
La radiologia medica
Nutrition and Health in Aging
article

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

Ludovica Laschena, Antonella Borrelli, Ailin Dehghanpour, Emanuele Messina, Simone Novelli, V. Panebianco, Nicholas Landini, M. Muscaritoli, Francesca Mezzapesa, Giuseppe Simone
article en

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.

La radiologia medica
Policlinico Umberto I (IT), The Royal Free Hospital (GB), Istituti di Ricovero e Cura a Carattere Scientifico (IT), University College London (GB), Sapienza University of Rome (IT)
Ministero dell'Università e della Ricerca
Good health and well-being
Openalex Percentile: Top 37%
Nutrition and Health in Aging
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