Scholarlink

Objectives <p>To develop and validate an interpretable and generalized machine learning model using MRI for the individualized prediction of induction chemotherapy (ICT) response and survival in locoregionally advanced nasopharyngeal carcinoma (LANPC).</p> Methods <p>A total of 1368 patients who underwent MRI examinations before ICT from three hospitals were retrospectively enrolled and divided into training, internal validation, external validation, and cross-field strength validation cohorts. Significant radiomics and clinical features were selected from coarse to fine. An interpretable genetic algorithm-enhanced artificial neural network (GNN) was applied for models’ development and validation. The performance of junior and senior doctors in predicting ICT response with and without model aid was evaluated.</p> Results <p>The interpretable GNN model achieved good generalization performance in predicting ICT response, with areas under the curve (AUCs) ranging from 0.808 to 0.864 across all cohorts. Survival analysis demonstrated that low-risk patients defined by GNN-radiomics signature and clinical factors had better progression-free survival than high-risk patients in all cohorts (hazard ratio ranging from 3.231 to 12.787, <i>p</i> &lt; 0.05). The predictive performance of junior and senior doctors for ICT response significantly improved with model assistance (AUCs: 0.686 vs. 0.785 and 0.736 vs. 0.836, <i>p</i> &lt; 0.05).</p> Conclusion <p>An interpretable, applicable, and generalized GNN model based on multi-center databases achieved superior performance in predicting ICT response and survival in LANPC patients, which may contribute to the personalized treatment of LANPC.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Currently, there is a lack of accurate methods for predicting and evaluating the efficacy and prognosis of nasopharyngeal carcinoma (NPC)</i>.</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>Genetic algorithm-enhanced artificial neural network model excels in predicting induction chemotherapy response and survival outcome of NPC, providing valuable assistance to doctors in clinical practice</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>This model can identify patients likely to benefit from induction chemotherapy, promoting individualized treatment and optimizing clinical management</i>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An interpretable machine learning model assists in predicting induction chemotherapy response and survival for locoregionally advanced nasopharyngeal carcinoma using MRI: a multicenter study

  • 期刊论文

  • 发表日期：

    2025年2月10日

查看全文
  • Hai Liao,
  • Yang Zhao,
  • Wei Pei,
  • Xia Huang,
  • Shiting Huang,
  • Wei Wei,
  • Penghao Lai,
  • Weifeng Jin,
  • Huayan Bao,
  • Xueli Liang,
  • Lei Xiao,
  • Zhenyu Chen,
  • Shaolu Lu,
  • Danke Su,
  • Bingfeng Lu,
  • Linghui Pan

摘要

Objectives

To develop and validate an interpretable and generalized machine learning model using MRI for the individualized prediction of induction chemotherapy (ICT) response and survival in locoregionally advanced nasopharyngeal carcinoma (LANPC).

Methods

A total of 1368 patients who underwent MRI examinations before ICT from three hospitals were retrospectively enrolled and divided into training, internal validation, external validation, and cross-field strength validation cohorts. Significant radiomics and clinical features were selected from coarse to fine. An interpretable genetic algorithm-enhanced artificial neural network (GNN) was applied for models’ development and validation. The performance of junior and senior doctors in predicting ICT response with and without model aid was evaluated.

Results

The interpretable GNN model achieved good generalization performance in predicting ICT response, with areas under the curve (AUCs) ranging from 0.808 to 0.864 across all cohorts. Survival analysis demonstrated that low-risk patients defined by GNN-radiomics signature and clinical factors had better progression-free survival than high-risk patients in all cohorts (hazard ratio ranging from 3.231 to 12.787, p < 0.05). The predictive performance of junior and senior doctors for ICT response significantly improved with model assistance (AUCs: 0.686 vs. 0.785 and 0.736 vs. 0.836, p < 0.05).

Conclusion

An interpretable, applicable, and generalized GNN model based on multi-center databases achieved superior performance in predicting ICT response and survival in LANPC patients, which may contribute to the personalized treatment of LANPC.

Key Points

Question Currently, there is a lack of accurate methods for predicting and evaluating the efficacy and prognosis of nasopharyngeal carcinoma (NPC).

Findings Genetic algorithm-enhanced artificial neural network model excels in predicting induction chemotherapy response and survival outcome of NPC, providing valuable assistance to doctors in clinical practice.

Clinical relevance This model can identify patients likely to benefit from induction chemotherapy, promoting individualized treatment and optimizing clinical management.