Validation of a Natural Language Processing System to Identify Metastatic Castration-Sensitive Prostate Cancer Patients in a Large Cohort of Patients.

Prostate cancer (PC) mortality in Latin America is reported to be twice as high as in developed countries. Combination therapies, such as androgen deprivation therapy (ADT) combined with new hormonal therapies (NHT), are known to improve survival, but their effectiveness in Latin American populations remains understudied. This knowledge limitation can be addressed using artificial intelligence (AI). In this study, an AI and Natural Language Processing (NLP) tool was developed by the Hospital Italiano de Buenos Aires's (HIBA) healthcare AI program in collaboration with the oncology department, to extract, encode, and analyze electronic health record (EHR) data. The data were collected from patients diagnosed with metastatic castration-sensitive prostate cancer (mCSPC) at HIBA, from 2010 to 2020, with a focus on first-line treatment patterns. Performance metrics, including precision, recall, and F1 score, were evaluated for several key variables. For "prostate cancer diagnosis," recall was 0.95, and for "metastasis development detection," recall was 0.91. Regarding "initiation of first-line treatment," precision, recall, and F1 score were 0.94, 0.97, and 0.95, respectively. However, metrics for more complex variables, such as "castration resistance detection," were lower (precision 0.87, recall 0.46, F1 score 0.60). The tool demonstrated adequate recall for metastatic disease diagnosis, although further refinements are needed to enhance performance.