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Abstract #1910

Genetic Algorithm-Based Individual Treatment Effect Model for Optimizing Induction Chemotherapy in Nasopharyngeal Carcinoma

Chao Luo1, Shuchao Chen2, Haojiang Li1, Hongbo Chen2, Kan Deng3, Peng Wu4, and Lizhi Liu1
1Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China, 2School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China, 3Bayer Healthcare, Radiology, Guangzhou, China, 4Philips Healthcare, Shanghai, China

Synopsis

Keywords: Cancer, Head & Neck/ENT

Motivation: Improve decision-making by focusing on treatment effects rather than risk.

Goal(s): Develop an individual treatment effect (ITE) model, based on structured MR reporting variables, for predicting induction chemotherapy (IC) efficacy in nasopharyngeal carcinoma (NPC).

Approach: In this retrospective 2-center study (N=1207), an ITE model was developed based on real-number coding genetic algorithm.

Results: The ITE model successfully classify patients into three categories. In IC-beneficial subgroup, IC treatment decreased the mortality risk by 48% (adjusted P=0·029) in testing sets. In IC-detrimental group, mortality risks increased by 2·11 (adjusted P=0·023, testing set) after IC treatment. Overall survival was not significantly different in IC-ambiguous group.

Impact: By precisely stratifying the outcomes of IC treatment, ITE model has the potential to enhance individualized treatment and optimize the management of patients with NPC. Additionally, the ITE model offers a viable strategy for personalized treatment decision-making in other tumors.

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