Predictive Function of Tumor Burden-incorporated Machine-learning Algorithms for Overall Survival and Their Value in Guiding Management Decisions in Patients with Locally Advanced Nasopharyngeal Carcinoma

Overview

Journal J Natl Cancer Cent

Specialty Oncology

Date 2024 Jul 22

PMID 39036668

Authors

Yang Liu

Shiran Sun

Ye Zhang

Xiaodong Huang

Kai Wang

Yuan Qu

Xuesong Chen

Runye Wu

Jianghu Zhang

Jingwei Luo

Yexiong Li

Jingbo Wang

Junlin Yi

Affiliations

Soon will be listed here.

Abstract

Objective: Accurate prognostic predictions and personalized decision-making on induction chemotherapy (IC) for individuals with locally advanced nasopharyngeal carcinoma (LA-NPC) remain challenging. This research examined the predictive function of tumor burden-incorporated machine-learning algorithms for overall survival (OS) and their value in guiding treatment in patients with LA-NPC.

Methods: Individuals with LA-NPC were reviewed retrospectively. Tumor burden signature-based OS prediction models were established using a nomogram and two machine-learning methods, the interpretable eXtreme Gradient Boosting (XGBoost) risk prediction model, and DeepHit time-to-event neural network. The models' prediction performances were compared using the concordance index (C-index) and the area under the curve (AUC). The patients were divided into two cohorts based on the risk predictions of the most successful model. The efficacy of IC combined with concurrent chemoradiotherapy was compared to that of chemoradiotherapy alone.

Results: The 1 221 eligible individuals, assigned to the training ( = 813) or validation ( = 408) set, showed significant respective differences in the C-indices of the XGBoost, DeepHit, and nomogram models (0.849 and 0.768, 0.811 and 0.767, 0.730 and 0.705). The training and validation sets had larger AUCs in the XGBoost and DeepHit models than the nomogram model in predicting OS (0.881 and 0.760, 0.845 and 0.776, and 0.764 and 0.729, < 0.001). IC presented survival benefits in the XGBoost-derived high-risk but not low-risk group.

Conclusion: This research used machine-learning algorithms to create and verify a comprehensive model integrating tumor burden with clinical variables to predict OS and determine which patients will most likely gain from IC. This model could be valuable for delivering patient counseling and conducting clinical evaluations.

Citing Articles

Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.

Wang C, Wang T, Lu C, Wu Y, Hua M Diagnostics (Basel). 2024; 14(9).

PMID: 38732337 PMC: 11082984. DOI: 10.3390/diagnostics14090924.

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