JCO clinical cancer informatics · 2026
Analysis of Large Language Model Decision Making in Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Early Breast Cancer.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Buonaiuto Roberto, Caltavituro Aldo, Di Rienzo Rossana, Grieco Angela, Mangiacotti Federica P, Longobardi Alessandra, Cantile Vincenza, Molinaro Vittoria, Pagliuca Martina, Buono Giuseppe, De Placido Pietro, Pietroluongo Erica, Forestieri Valeria, Martinelli Claudia, di Lauro Vincenzo, Leo Luigi, D'Aiuto Massimiliano, Bianchini Giampaolo, Criscitiello Carmen, Bianco Roberto, Del Mastro Lucia, De Laurentiis Michelino, Arpino Grazia, De Angelis Carmine, Giuliano Mario
Аннотация
PURPOSE: To assess the ability of GPT-4o in adjuvant treatment decision making in hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) early breast cancer by comparing its recommendations with those of clinicians including Oncotype DX data, and to explore its potential as a decision-support tool in routine clinical practice.
METHODS: We compared clinician and GPT-4o recommendations in patients tested with Oncotype DX in routine practice at the University of Naples Federico II (n = 607, cohort 1 [C1]) and within the prospective, multicenter PRO BONO study (n = 237, cohort 2 [C2]). Pre- and post-Oncotype DX treatment recommendations were categorized as chemotherapy (CT) + endocrine therapy (ET) or ET alone. Concordance between clinician and GPT-4o recommendations was assessed using agreement rates and Cohen's kappa. The accuracy of Oncotype DX results was evaluated using the AUC metric.
RESULTS: The agreement between clinicians and GPT-4o in pretest recommendations was 68% (kappa, 0.381 [95% CI, 0.31 to 0.45], P < .001) in C1 and 70% (0.401 [95% CI, 0.29 to 0.52], P < .001) in C2. Before Oncotype DX, clinicians recommended CT more frequently than GPT-4o for C1 (58% v 38%) and C2 (53% v 43%). Post-test agreement increased to 93% (0.814 [95% CI, 0.76 to 0.87], P < .001) in C1 and 90% (0.741 [95% CI, 0.64 to 0.84], P < .001) in C2. The agreement between pre- and post-Oncotype DX treatment recommendations for clinicians was 56% and 63% versus 68% and 60% for GPT-4o in C1 and C2, respectively. GPT-4o showed higher accuracy in predicting low than high genomic risk in postmenopausal patients (87% v 43% in C1; 85% v 45% in C2, P < .001) and low versus intermediate and high risk in premenopausal patients in both cohorts (P < .001).
CONCLUSION: The agreement between clinicians and GPT-4o in pretest recommendations was modest but improved post-test, highlighting the importance of multigene testing and the potential of large language models in clinical decision making.
METHODS: We compared clinician and GPT-4o recommendations in patients tested with Oncotype DX in routine practice at the University of Naples Federico II (n = 607, cohort 1 [C1]) and within the prospective, multicenter PRO BONO study (n = 237, cohort 2 [C2]). Pre- and post-Oncotype DX treatment recommendations were categorized as chemotherapy (CT) + endocrine therapy (ET) or ET alone. Concordance between clinician and GPT-4o recommendations was assessed using agreement rates and Cohen's kappa. The accuracy of Oncotype DX results was evaluated using the AUC metric.
RESULTS: The agreement between clinicians and GPT-4o in pretest recommendations was 68% (kappa, 0.381 [95% CI, 0.31 to 0.45], P < .001) in C1 and 70% (0.401 [95% CI, 0.29 to 0.52], P < .001) in C2. Before Oncotype DX, clinicians recommended CT more frequently than GPT-4o for C1 (58% v 38%) and C2 (53% v 43%). Post-test agreement increased to 93% (0.814 [95% CI, 0.76 to 0.87], P < .001) in C1 and 90% (0.741 [95% CI, 0.64 to 0.84], P < .001) in C2. The agreement between pre- and post-Oncotype DX treatment recommendations for clinicians was 56% and 63% versus 68% and 60% for GPT-4o in C1 and C2, respectively. GPT-4o showed higher accuracy in predicting low than high genomic risk in postmenopausal patients (87% v 43% in C1; 85% v 45% in C2, P < .001) and low versus intermediate and high risk in premenopausal patients in both cohorts (P < .001).
CONCLUSION: The agreement between clinicians and GPT-4o in pretest recommendations was modest but improved post-test, highlighting the importance of multigene testing and the potential of large language models in clinical decision making.