Optimization of first-line treatment selection in advanced pancreatic adenocarcinoma using artificial intelligence
摘要
Improved clinical outcomes are reported for patients with advanced pancreatic adenocarcinoma (PDAC) treated with first-line FOLFIRINOX/NALIRIFOX, but elderly patients with comorbidities are more often treated with gemcitabine/nab-paclitaxel (gem/nab-p). There is currently no comprehensive method to optimize first-line treatment selection between these regimens. We developed a propensity score matched, transcriptomic-based AI model using 2202 molecularly-profiled PDAC specimens to provide clinically relevant treatment recommendations and prognostic information. In a testing dataset of patients predicted to have superior outcomes on first-line FOLFIRINOX, time-to-next-treatment (TTNT) and overall survival (OS) were significantly longer for patients treated with FOLFIRINOX first (HRs = 0.55 and 0.48, respectively, p < 0.001). Patients recommended for gem/nab-p treatment had similar outcomes on either treatment, but a subset had improved outcomes on gem/nab-p. Approximately half of patients had received the opposite therapy from the model recommendation. Applied clinically, this model could improve treatment decision-making in advanced PDAC in the first-line setting.