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

Medical Imaging & Diagnostics

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

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 character-istic (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. Multivari-ate 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 pre-dictors 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.

Executive Impact Summary

This study highlights the significant role of AI-assessed sarcopenia as an independent predictor of response to neoadjuvant chemotherapy (NAC) in muscle-invasive bladder cancer (MIBC) patients. Integrating AI-driven sarcopenia assessment into pretreatment workflows can significantly refine therapeutic decision-making, improve patient outcomes, and enable tailored nutritional interventions. The high AUC of 0.952 for SMI in predicting pathological response underscores its robust prognostic value.

Sarcopenia as predictor of poor NAC response
SMI's discriminative ability (AUC)
Patients with sarcopenia

Deep Analysis & Enterprise Applications

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9.08 Odds Ratio for Poor NAC Response

AI-assessed sarcopenia was identified as an independent predictor of poor response to neoadjuvant chemotherapy (NAC) with an adjusted odds ratio of 9.08 (95% CI 1.32–61.92; p=0.024), highlighting its significant prognostic value.

0.952 AUC for SMI in Predicting Pathological Response

The Receiver Operating Characteristic (ROC) curve analysis confirmed the strong discriminative ability of SMI in predicting pathological response (TRG 1–2 vs. TRG 3) with an AUC of 0.952 (95% CI 0.89–0.99).

NACVI-RADS Classification Impact

Good Response (NacVI-RADS 1-3) Poor Response (NacVI-RADS 4-5)
  • Strong association with treatment outcome (OR=2.13, p=0.042)
  • Category 3 behaves more similarly to non-muscle-invasive group
  • Enhances clinical utility and improves risk stratification
  • Higher NacVI-RADS categories (4-5) strongly associated with non-response (OR=36.6, p<0.001)
  • Indicates persistent or worsening disease
  • Independent predictor of poor treatment outcome

Enterprise Process Flow

Baseline CT Scan for Body Composition
AI-Powered Software for SMI Calculation
Sarcopenia Identification
Integration into Clinical Staging
Tailored Nutritional Interventions
Improved Patient Stratification & Outcomes

Transforming Patient Management with AI

Scenario: A 67-year-old MIBC patient undergoing NAC presents with sarcopenia, as assessed by AI. Traditional methods are slow and subjective. With AI, sarcopenia is quickly identified, indicating a higher risk of poor treatment response. Clinicians can then implement early nutritional support and adjust treatment plans accordingly, potentially improving overall prognosis and treatment tolerance.

Outcome: The integration of AI-driven sarcopenia assessment leads to proactive interventions, personalized treatment strategies, and a more precise understanding of the patient's physical condition, ultimately enhancing the likelihood of a positive treatment outcome and overall survival.

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