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Borrelli, Antonella; Pecoraro, Martina; Del Giudice, Francesco; Cristofani, Leonardo; Messina, Emanuele; Dehghanpour, Ailin; Landini, Nicholas; Roberto, Michela; Perotti, Stefano; Muscaritoli, Maurizio; Santini, Daniele; Catalano, Carlo; Panebianco, Valeria

Standardization of Body Composition Status in Patients with Advanced Urothelial Tumors: The Role of a CT-Based AI-Powered Software for the Assessment of Sarcopenia and Patient Outcome Correlation

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  • Medientyp: E-Artikel
  • Titel: Standardization of Body Composition Status in Patients with Advanced Urothelial Tumors: The Role of a CT-Based AI-Powered Software for the Assessment of Sarcopenia and Patient Outcome Correlation
  • Beteiligte: Borrelli, Antonella; Pecoraro, Martina; Del Giudice, Francesco; Cristofani, Leonardo; Messina, Emanuele; Dehghanpour, Ailin; Landini, Nicholas; Roberto, Michela; Perotti, Stefano; Muscaritoli, Maurizio; Santini, Daniele; Catalano, Carlo; Panebianco, Valeria
  • Erschienen: MDPI AG, 2023
  • Erschienen in: Cancers, 15 (2023) 11, Seite 2968
  • Sprache: Englisch
  • DOI: 10.3390/cancers15112968
  • ISSN: 2072-6694
  • Schlagwörter: Cancer Research ; Oncology
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: Background: Sarcopenia is a well know prognostic factor in oncology, influencing patients’ quality of life and survival. We aimed to investigate the role of sarcopenia, assessed by a Computed Tomography (CT)-based artificial intelligence (AI)-powered-software, as a predictor of objective clinical benefit in advanced urothelial tumors and its correlations with oncological outcomes. Methods: We retrospectively searched patients with advanced urothelial tumors, treated with systemic platinum-based chemotherapy and an available total body CT, performed before and after therapy. An AI-powered software was applied to CT to obtain the Skeletal Muscle Index (SMI-L3), derived from the area of the psoas, long spine, and abdominal muscles, at the level of L3 on CT axial images. Logistic and Cox-regression modeling was implemented to explore the association of sarcopenic status and anthropometric features to the clinical benefit rate and survival endpoints. Results: 97 patients were included, 66 with bladder cancer and 31 with upper-tract urothelial carcinoma. Clinical benefit outcomes showed a linear positive association with all the observed body composition variables variations. The chances of not experiencing disease progression were positively associated with ∆_SMI-L3, ∆_psoas, and ∆_long spine muscle when they ranged from ~10–20% up to ~45–55%. Greater survival chances were matched by patients achieving a wider ∆_SMI-L3, ∆_abdominal and ∆_long spine muscle. Conclusions: A CT-based AI-powered software body composition and sarcopenia analysis provide prognostic assessments for objective clinical benefits and oncological outcomes.
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