ArticleNPJ precision oncology2026
Optimization of first-line treatment selection in advanced pancreatic adenocarcinoma using artificial intelligence.
Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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16 authors.
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Abstract
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.
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