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Background <p>To explore the value of intratumoral and peritumoral radiomics in preoperative prediction of anaplastic lymphoma kinase (ALK) mutation status and survival in patients with lung adenocarcinoma.</p> Methods <p>We retrospectively collected data from 505 eligible patients with lung adenocarcinoma from four hospitals (training and external validation sets 1–3). The CT-based radiomics features were extracted separately from the gross tumor volume (GTV) and GTV incorporating peritumoral 3-, 6-, 9-, 12-, and 15-mm regions (GPTV<sub>3</sub>, GPTV<sub>6</sub>, GPTV<sub>9</sub>, GPTV<sub>12</sub>, and GPTV<sub>15</sub>), and screened the most relevant features to construct radiomics models to predict ALK (+). The combined model incorporated radiomics scores (Rad-scores) of the best radiomics model and clinical predictors was constructed. Performance was evaluated using receiver operating characteristic (ROC) analysis. Progression-free survival (PFS) outcomes were examined using the Cox proportional hazards model.</p> Results <p>In the four sets, 21.19% (107/505) patients were ALK (+). The GPTV<sub>3</sub> radiomics model using a support vector machine algorithm achieved the best predictive performance, with the highest average AUC of 0.811 in the validation sets. Clinical TNM stage and pleural indentation were independent predictors. The combined model incorporating the GPTV<sub>3</sub>-Rad-score and clinical predictors achieved higher performance than the clinical model alone in predicting ALK (+) in three validation sets [AUC: 0.855 (95% CI: 0.766–0.919) vs. 0.648 (95% CI: 0.543–0.745), <i>P</i> = 0.001; 0.882 (95% CI: 0.801–0.962) vs. 0.634 (95% CI: 0.548–0.714), <i>P &lt;</i> 0.001; 0.810 (95% CI: 0.727–0.877) vs. 0.663 (95% CI: 0.570–0.748), <i>P</i> = 0.006]. The prediction score of the combined model could stratify PFS outcomes in patients receiving ALK-TKI therapy (HR: 0.37; 95% CI: 0.15–0.89; <i>P</i> = 0.026) and immunotherapy (HR: 2.49; 95% CI: 1.22–5.08; <i>P</i> = 0.012).</p> Conclusion <p>The presented combined model based on GPTV<sub>3</sub> effectively mined tumor features to predict ALK mutation status and stratify PFS outcomes in patients with lung adenocarcinoma.</p>

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Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月13日

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  • Weiyue Chen,
  • Guihan Lin,
  • Ye Feng,
  • Yongjun Chen,
  • Yanjun Li,
  • Jianbin Li,
  • Weibo Mao,
  • Yang Jing,
  • Chunli Kong,
  • Yumin Hu,
  • Minjiang Chen,
  • Shuiwei Xia,
  • Chenying Lu,
  • Jianfei Tu,
  • Jiansong Ji

摘要

Background

To explore the value of intratumoral and peritumoral radiomics in preoperative prediction of anaplastic lymphoma kinase (ALK) mutation status and survival in patients with lung adenocarcinoma.

Methods

We retrospectively collected data from 505 eligible patients with lung adenocarcinoma from four hospitals (training and external validation sets 1–3). The CT-based radiomics features were extracted separately from the gross tumor volume (GTV) and GTV incorporating peritumoral 3-, 6-, 9-, 12-, and 15-mm regions (GPTV3, GPTV6, GPTV9, GPTV12, and GPTV15), and screened the most relevant features to construct radiomics models to predict ALK (+). The combined model incorporated radiomics scores (Rad-scores) of the best radiomics model and clinical predictors was constructed. Performance was evaluated using receiver operating characteristic (ROC) analysis. Progression-free survival (PFS) outcomes were examined using the Cox proportional hazards model.

Results

In the four sets, 21.19% (107/505) patients were ALK (+). The GPTV3 radiomics model using a support vector machine algorithm achieved the best predictive performance, with the highest average AUC of 0.811 in the validation sets. Clinical TNM stage and pleural indentation were independent predictors. The combined model incorporating the GPTV3-Rad-score and clinical predictors achieved higher performance than the clinical model alone in predicting ALK (+) in three validation sets [AUC: 0.855 (95% CI: 0.766–0.919) vs. 0.648 (95% CI: 0.543–0.745), P = 0.001; 0.882 (95% CI: 0.801–0.962) vs. 0.634 (95% CI: 0.548–0.714), P < 0.001; 0.810 (95% CI: 0.727–0.877) vs. 0.663 (95% CI: 0.570–0.748), P = 0.006]. The prediction score of the combined model could stratify PFS outcomes in patients receiving ALK-TKI therapy (HR: 0.37; 95% CI: 0.15–0.89; P = 0.026) and immunotherapy (HR: 2.49; 95% CI: 1.22–5.08; P = 0.012).

Conclusion

The presented combined model based on GPTV3 effectively mined tumor features to predict ALK mutation status and stratify PFS outcomes in patients with lung adenocarcinoma.