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<p>Current survival prediction model for extra-nodal natural killer/T-cell lymphoma (ENKTL) have poor accuracy. We developed and validated a machine-learning (ML) algorithm using clinical and pathological co-variates. Data from 977 subjects with ENKTL from 4 cohorts were analyzed. Model performance was evaluated using Harrell’s c-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCAs). The Gradient Boosting Machine (GBM) outperformed the other 15 ML algorithms tested, leading to the development of the ENKTL-ML score. C-indexes of the evaluation and external validation cohorts were 0.82 (95% Confidence Interval [CI], 0.76, 0.87), 0.84 (0.81, 0.88) and 0.83 (0.72, 0.94). The ENKTL-ML score effectively stratified subjects into 3 groups with distinct survival outcomes. Our model was more accurate than the IPI, KPI, PINK-E, and NRI (all <i>P</i> &lt; 0.001) models. An online calculator is available at <a href="https://highcloud.shinyapps.io/ENKTL_ML_Scores/">https://highcloud.shinyapps.io/ENKTL_ML_Scores/</a>. The ENKTL-ML score should help physicians predict survival of people with ENKTL.</p>

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Development and validation of a predictive model for extranodal natural killer/T-cell lymphoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月6日

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  • Shuo Li,
  • Li-Min Gao,
  • Huang-Ming Hong,
  • Yu-Jia Zhong,
  • Qing-Qing Cai,
  • Hui-Qiang Huang,
  • Zhi-Ming Li,
  • He Huang,
  • Yin-Li Zheng,
  • Xuan-Kai Zeng,
  • Yan-Fen Feng,
  • Ying-Chun Zhang,
  • Sheng-Bing Zang,
  • Yan Li,
  • Jing-Ping Yun,
  • Yu-Hua Huang

摘要

Current survival prediction model for extra-nodal natural killer/T-cell lymphoma (ENKTL) have poor accuracy. We developed and validated a machine-learning (ML) algorithm using clinical and pathological co-variates. Data from 977 subjects with ENKTL from 4 cohorts were analyzed. Model performance was evaluated using Harrell’s c-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCAs). The Gradient Boosting Machine (GBM) outperformed the other 15 ML algorithms tested, leading to the development of the ENKTL-ML score. C-indexes of the evaluation and external validation cohorts were 0.82 (95% Confidence Interval [CI], 0.76, 0.87), 0.84 (0.81, 0.88) and 0.83 (0.72, 0.94). The ENKTL-ML score effectively stratified subjects into 3 groups with distinct survival outcomes. Our model was more accurate than the IPI, KPI, PINK-E, and NRI (all P < 0.001) models. An online calculator is available at https://highcloud.shinyapps.io/ENKTL_ML_Scores/. The ENKTL-ML score should help physicians predict survival of people with ENKTL.