BJU international · 2025
Artificial intelligence-driven streamlining of prostate cancer multidisciplinary team recommendations in a tertiary NHS centre in the UK.
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Khattak Ahmed, Ruta Danny, Patkar Vivek, Popert Rick, Makanjuola Jonathan, Martin Martha, Ledwaba-Chapman Lesedi, Dodgson Kate, Oldroyd Robert, Noel Jonathan, Cathcart Paul, Hughes Simon, Challacombe Ben, Josephs Deborah, Enting Deborah, Pintus Elias, McCarthy Ruth, Kazmi Majid
Аннотация
OBJECTIVES: To evaluate the effectiveness of a rules-based artificial intelligence (AI) clinical decision support system (CDSS) called the PROState AI Cancer-Decision Support (PROSAIC-DS) in streamlining the prostate cancer multidisciplinary team (MDT) pathway by identifying patients meeting standard of care (SoC) guidelines for reduced discussion in MDT meetings.
SUBJECTS/PATIENTS AND METHODS: This study consisted of two phases. Phase one involved a retrospective concordance analysis of 287 patients referred to the prostate MDT at King's College Hospital over a 2-year period. In phase two, a prospective analysis included 416 patients from Guy's Hospital over another 2-year period. Clinical treatment recommendations were independently reviewed by a panel of urologists and oncologists to establish a 'ground truth.' Concordance between the medical recommendations and those generated by the PROSAIC-DS was assessed.
RESULTS: In phase one, the overall concordance between the clinicians' recommendations and the PROSAIC-DS was 92% (95% confidence interval [CI] 88.1-94.7%), compared to just 53% (95% CI 47-59%) with historic MDT outputs (P < 0.01). In phase two, the PROSAIC-DS achieved an 85.6% concordance (95% CI 81.6-88.9%) with the MDT recommendations for 355 evaluable cases (P < 0.01). Notably, using a machine learning-derived decision tree enabled the identification of 93 patients for streamlined management, demonstrating a 97.8% concordance in this subgroup (P < 0.01).
CONCLUSION: The implementation of the PROSAIC-DS into the prostate cancer MDT pathway allowed 33.8% of patients to bypass MDT discussions with high treatment concordance. This study showcases the potential for AI-based solutions to improve clinical workflow and patient management in oncology, thus addressing the workload challenges faced by MDTs.
SUBJECTS/PATIENTS AND METHODS: This study consisted of two phases. Phase one involved a retrospective concordance analysis of 287 patients referred to the prostate MDT at King's College Hospital over a 2-year period. In phase two, a prospective analysis included 416 patients from Guy's Hospital over another 2-year period. Clinical treatment recommendations were independently reviewed by a panel of urologists and oncologists to establish a 'ground truth.' Concordance between the medical recommendations and those generated by the PROSAIC-DS was assessed.
RESULTS: In phase one, the overall concordance between the clinicians' recommendations and the PROSAIC-DS was 92% (95% confidence interval [CI] 88.1-94.7%), compared to just 53% (95% CI 47-59%) with historic MDT outputs (P < 0.01). In phase two, the PROSAIC-DS achieved an 85.6% concordance (95% CI 81.6-88.9%) with the MDT recommendations for 355 evaluable cases (P < 0.01). Notably, using a machine learning-derived decision tree enabled the identification of 93 patients for streamlined management, demonstrating a 97.8% concordance in this subgroup (P < 0.01).
CONCLUSION: The implementation of the PROSAIC-DS into the prostate cancer MDT pathway allowed 33.8% of patients to bypass MDT discussions with high treatment concordance. This study showcases the potential for AI-based solutions to improve clinical workflow and patient management in oncology, thus addressing the workload challenges faced by MDTs.