Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer

A comprehensive systematic review and meta-analysis

Authors

  • Caio Vinícius Suartz Universidade de São Paulo https://orcid.org/0000-0002-1364-5508
  • Lucas Motta Martinez Division of Urology, Institute of Cancer of São Paulo, University of São Paulo, São Paulo, Brazil
  • Maurício Dener Cordeiro Division of Urology, Institute of Cancer of São Paulo, University of São Paulo, São Paulo, Brazil
  • Hunter Ausley Flores Department of Urology, University of Colorado, Anschutz Medical Campus, United States of America
  • Sarah Kodama Virginia Commonwealth University School of Medicine, United States of America
  • Leonardo Cardili Division of Urology, Institute of Cancer of São Paulo, University of São Paulo, São Paulo, Brazil
  • José Maurício Mota Division of Oncology, Institute of Cancer of São Paulo, University of São Paulo, São Paulo, Brazil
  • Fernando Morbeck Almeida Coelho Department of Radiology, University of São Paulo, São Paulo, Brazil
  • José de Bessa Junior State University of Feira de Santana, Feira de Santana, Bahia, Brazil
  • Cristina Pires Camargo Microsurgery and Plastic Surgery Laboratory, School of Medicine, Universidade de São Paulo, São Paulo, Brazil.
  • Jeremy Yuen-Chun Teoh S.H.Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
  • Shahrokh F. Shariat Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria; Department of Urology, Weill Cornell Medical College, New York, NY; Department of Urology, University of Texas Southwestern, Dallas, TX; Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan.
  • Paul Toren CHU de Québec-Université Laval, Quebec City, Quebec, Canada.
  • William Carlos Nahas Division of Urology, Institute of Cancer of São Paulo, University of São Paulo, São Paulo, Brazil
  • Leopoldo Alves Ribeiro-Filho Division of Urology, Institute of Cancer of São Paulo, University of São Paulo, São Paulo, Brazil

DOI:

https://doi.org/10.5489/cuaj.8681

Keywords:

Bladder cancer, Radical cystectomy, Prognosis, Neoadjuvant, Artificial Intelligence

Abstract

INTRODUCTION: Neoadjuvant cisplatin-based combination chemotherapy (NAC) followed by radical cystectomy is the standard of care for cisplatin-fit patients harboring muscleinvasive bladder cancer (MIBC). Prediction of response to NAC is essential for clinical decision-making regarding alternatives in case of non-response, and bladder-sparing in case of complete response. This research aimed to assess the performance of machine learning in predicting therapeutic response following NAC treatment in patients with MIBC.

METHODS: A systematic review adhering to the PRISMA guidelines was conducted until July 2023. The study integrated articles relating to artificial intelligence and NAC response in MIBC from various databases. The quality of articles was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies 2 (QUADAS-2). A meta-analysis was subsequently performed on selected studies to determine the sensitivity and specificity of machine learning algorithms in predicting NAC response.

RESULTS: Of 655 articles identified, 12 studies comprising 1523 patients were included, and four studies were eligible for meta-analysis. The sensitivity and specificity of the studies were 0.62 (95% confidence interval [CI] 0.50–0.72) and 0.82 (95% CI 0.72–0.89), respectively, with a heterogeneity score (I2) of 38.5%. The machine learning algorithms used computed tomography, genetic, and anatomopathologic data as input and exhibited promising potential for predicting NAC response.

CONCLUSIONS: Machine-learning algorithms, especially those using computed tomography, genetic, and pathologic data, demonstrate significant potential for predicting NAC response in MIBC. Standardization of methodologic data analysis and response criteria are needed as validation studies.

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Published

2024-05-21

How to Cite

Suartz, C. V., Martinez, L. M., Cordeiro, M. D. ., Flores, H. A., Kodama, S., Cardili, L., … Ribeiro-Filho, L. A. . (2024). Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer: A comprehensive systematic review and meta-analysis. Canadian Urological Association Journal, 18(9), E276–84. https://doi.org/10.5489/cuaj.8681

Issue

Vol. 18 No. 9 (2024): CUAJ September

Section

Review

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