Artificial intelligence versus multidisciplinary teams in high-risk prostate cancer with positive surgical margins: a pilot proof-of-concept study

Authors

  • Renata Duarte-Valdés Secretaría der Salud, Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”, Ciudad de México, México.
  • Horst Emanuel Lagos-Beitz Secretaría der Salud, Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”, Ciudad de México, México. https://orcid.org/0009-0001-4345-9183
  • Guillermo H. Martínez-Delgado Secretaría der Salud, Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”, Ciudad de México, México. https://orcid.org/0000-0003-0421-0550
  • Ricardo Alonso Castillejos-Molina Secretaría der Salud, Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”, Ciudad de México, México. https://orcid.org/0000-0001-7889-2318

DOI:

https://doi.org/10.48193/vfsvwx11

Keywords:

Artificial intelligence, prostate cáncer, positive surgical margins, multidisciplinary teams, decision-making, radical prostatectomy

Abstract

Objective: to compare treatment recommendations from multidisciplinary teams (MDTs) and artificial intelligence (AI) in high- and very high-risk prostate cancer (PCa) with positive surgical margins (PSM) after radical prostatectomy (RP).

Design, methodology or approach: we conducted a retrospective proof-of-concept study at a tertiary referral center. Among 700 men who underwent RP between 2010–2024, 76 had PSM, and 14 had documented preoperative MDT discussions. Five patients fulfilled inclusion criteria: high-/very high-risk PCa (per NCCN), confirmed PSM, and both pre- and postoperative MDT evaluations. Clinical and pathological data were entered into ChatGPT-4.0 (OpenAI), primed with NCCN, AUA, and EAU guidelines. The AI was queried in a zero-shot manner for treatment recommendations, and concordance was evaluated using Cohen’s kappa (κ).

Results: five patients were included in the final analysis. AI recommendations aligned with MDT decisions in 2 of 5 cases (40 %) both preoperatively and postoperatively. Agreement was minimal for initial therapy (κ = 0.00) and slight for postoperative management (κ = 0.12).

Limitations or implications: this pilot study is limited by its small sample size and retrospective design, restricting generalizability.

Originality or value: this is the first study to directly compare AI-driven and MDT recommendations in high-risk PCa with PSM in Mexico.

Findings or conclusions: AI generated rapid, guideline-based recommendations, but showed limited concordance with MDT decisions in this pilot cohort. AI may support efficiency and guideline adherence as an adjunctive tool; however, this study did not evaluate whether either recommendation strategy leads to superior clinical outcomes.

Author Biography

  • Ricardo Alonso Castillejos-Molina, Secretaría der Salud, Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”, Ciudad de México, México.

    Especialidad: Urología Oncológica, Medicina Sexual y Andrología
    País: México

    Educación superior y especialidad:
    • Urología, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Ciudad de México
    • Urología Oncológica, Medicina Sexual y Andrología, IRCCS Ospedale San Raffaele di Milano
    Experiencia laboral:
    • Urología, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Ciudad de México desde 2007 a la fecha
    • Profesor Titular Posgrado de la Especialidad en Urología, UNAM
    • Profesor Titular Pregrado Urología, UNAM
    Asociaciones:
    • Sociedad Mexicana de Urología
    • AUA
    • EAU
    • SMSNA
    Distinciones y publicaciones:
    • Profesor Titular Posgrado de la Especialidad en Urología, UNAM
    • Vicepresidente de la Sociedad Mexicana de Urología
    • Presidente de la Fundación de la Sociedad Mexicana de Urología
    • 135 publicaciones, 715 citas

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Published

2026-06-16

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Original articles