Genetic algorithm-optimized neural network outperforms TNM staging in predicting rapidly progressive nasopharyngeal carcinoma: Reassessing adjuvant chemotherapy benefit via propensity score matching.
Uloženo v:
| Název: | Genetic algorithm-optimized neural network outperforms TNM staging in predicting rapidly progressive nasopharyngeal carcinoma: Reassessing adjuvant chemotherapy benefit via propensity score matching. |
|---|---|
| Autoři: | Ling LT; Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China., Li WJ; Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China., Yao Y; Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China., Tan KQ; Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China., Zhu BL; Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China., Zhou LQ; Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China., Qu S; Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China., Li L; Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China., Guan Y; Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China., Zhu XD; Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China. Electronic address: zhuxdonggxmu@126.com., Pan LH; Guangxi Engineering Research Center for Tissue & Organ Injury and Repair Medicine, Nanning, China; Guangxi Key Laboratory for Basic Science and Prevention of Perioperative Organ Dysfunction, Nanning, China. Electronic address: panlinghui@gxmu.edu.cn., Liang ZG; Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China. Electronic address: liangzhongguo@gxmu.edu.cn. |
| Zdroj: | European journal of cancer (Oxford, England : 1990) [Eur J Cancer] 2025 Nov 17; Vol. 230, pp. 115787. Date of Electronic Publication: 2025 Sep 12. |
| Způsob vydávání: | Journal Article |
| Jazyk: | English |
| Informace o časopise: | Publisher: Elsevier Science Ltd Country of Publication: England NLM ID: 9005373 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0852 (Electronic) Linking ISSN: 09598049 NLM ISO Abbreviation: Eur J Cancer Subsets: MEDLINE |
| Imprint Name(s): | Publication: Oxford : Elsevier Science Ltd Original Publication: Oxford ; New York : Pergamon Press, c1990- |
| Výrazy ze slovníku MeSH: | Neural Networks, Computer* , Nasopharyngeal Carcinoma*/pathology , Nasopharyngeal Carcinoma*/therapy , Nasopharyngeal Carcinoma*/mortality , Nasopharyngeal Carcinoma*/genetics , Nasopharyngeal Neoplasms*/pathology , Nasopharyngeal Neoplasms*/therapy , Nasopharyngeal Neoplasms*/genetics , Nasopharyngeal Neoplasms*/mortality , Algorithms*, Humans ; Male ; Female ; Retrospective Studies ; Middle Aged ; Neoplasm Staging ; Chemotherapy, Adjuvant ; Disease Progression ; Propensity Score ; Adult ; Aged ; Machine Learning ; Chemoradiotherapy ; Young Adult ; Genetic Algorithms |
| Abstrakt: | Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Purpose: To establish machine learning-based predictive models for rapidly progressive nasopharyngeal carcinoma (RP-NPC), defined as disease progression within 24 months post-initial treatment, and to assess differential survival benefits of adjuvant chemotherapy (AC) following concurrent chemoradiotherapy (CCRT) in RP-NPC versus Non-RP-NPC subgroups. Methods: This retrospective cohort study analyzed 716 NPC patients (2007-2012). Five machine learning models were constructed using independent risk factors, including: Genetic algorithm-optimized neural network (GNN), Standard artificial neural networks (ANN and BPNN), eXtreme Gradient Boosting (XGBoost), and Logistic regression (LR). Predictive performance was rigorously evaluated using ROC curve analysis (quantified by area under the curve, AUC) for discrimination and calibration plots for reliability estimation, with both internal validation (bootstrap resampling) and external validation procedures. Stratified survival analysis was performed using Cox proportional hazards models to compare CCRT-AC versus CCRT alone in both machine learning-predicted and clinically defined RP-NPC and Non-RP-NPC subgroups. Results: Five independent predictors emerged for rapid disease progression (T/N stage, age, alkaline phosphatase, lactate dehydrogenase). The AUC value of the GNN, ANN, BPNN, XGBoost, LR model, and TNM stage in predicting RP-NPC was 0.777 vs 0.792 vs 0.774 vs 0.841 vs 0.735 vs 0.688 (training cohort), and 0.782 vs 0.734 vs 0.606 vs 0.698 vs 0.711 vs 0.687 (validation cohort), respectively. After propensity score matching, AC demonstrated no survival benefit for patients with RP-NPC, regardless of whether they were identified by the GNN model or clinically defined criteria. Conclusion: The GNN demonstrated superior predictive capability and enhanced generalizability over conventional TNM staging for identifying RP-NPC. Critically, RP-NPC patients derived no survival benefit from AC supplementation after CCRT. (Copyright © 2025 Elsevier Ltd. All rights reserved.) |
| Contributed Indexing: | Keywords: Adjuvant chemotherapy; Concurrent chemoradiotherapy; Machine learning; Prognostic model; Rapidly progressive nasopharyngeal carcinoma |
| Entry Date(s): | Date Created: 20250929 Date Completed: 20251102 Latest Revision: 20251102 |
| Update Code: | 20251103 |
| DOI: | 10.1016/j.ejca.2025.115787 |
| PMID: | 41022023 |
| Databáze: | MEDLINE |
Full Text Finder 
Nájsť tento článok vo Web of Science | Abstrakt: | Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />Purpose: To establish machine learning-based predictive models for rapidly progressive nasopharyngeal carcinoma (RP-NPC), defined as disease progression within 24 months post-initial treatment, and to assess differential survival benefits of adjuvant chemotherapy (AC) following concurrent chemoradiotherapy (CCRT) in RP-NPC versus Non-RP-NPC subgroups.<br />Methods: This retrospective cohort study analyzed 716 NPC patients (2007-2012). Five machine learning models were constructed using independent risk factors, including: Genetic algorithm-optimized neural network (GNN), Standard artificial neural networks (ANN and BPNN), eXtreme Gradient Boosting (XGBoost), and Logistic regression (LR). Predictive performance was rigorously evaluated using ROC curve analysis (quantified by area under the curve, AUC) for discrimination and calibration plots for reliability estimation, with both internal validation (bootstrap resampling) and external validation procedures. Stratified survival analysis was performed using Cox proportional hazards models to compare CCRT-AC versus CCRT alone in both machine learning-predicted and clinically defined RP-NPC and Non-RP-NPC subgroups.<br />Results: Five independent predictors emerged for rapid disease progression (T/N stage, age, alkaline phosphatase, lactate dehydrogenase). The AUC value of the GNN, ANN, BPNN, XGBoost, LR model, and TNM stage in predicting RP-NPC was 0.777 vs 0.792 vs 0.774 vs 0.841 vs 0.735 vs 0.688 (training cohort), and 0.782 vs 0.734 vs 0.606 vs 0.698 vs 0.711 vs 0.687 (validation cohort), respectively. After propensity score matching, AC demonstrated no survival benefit for patients with RP-NPC, regardless of whether they were identified by the GNN model or clinically defined criteria.<br />Conclusion: The GNN demonstrated superior predictive capability and enhanced generalizability over conventional TNM staging for identifying RP-NPC. Critically, RP-NPC patients derived no survival benefit from AC supplementation after CCRT.<br /> (Copyright © 2025 Elsevier Ltd. All rights reserved.) |
|---|---|
| ISSN: | 1879-0852 |
| DOI: | 10.1016/j.ejca.2025.115787 |