Scholarlink

Purpose <p>Local control prediction for patients with pulmonary oligometastases underwent stereotactic body radiotherapy (SBRT) is crucial for optimizing therapeutic strategies. This study aims to develop and validate a predictive radiomics model integrating both tumor-intrinsic and peritumoral features along with clinical factors to enhance local control prediction using a multi-center dataset.</p> Materials and methods <p>We analyzed 223 tumors from 146 patients, which was divided into a training set (<i>n</i> = 165) and an external validation set (<i>n</i> = 58). Radiomic features from the gross tumor volume (GTV) and peritumoral regions (pGTV) representing the tumor microenvironment (TME) in CT images were extracted and combined with clinical factors to build a clinical outcome prediction model. Tumor response was classified into Favorable Response Group (FRG) and Unfavorable Response Group (URG) according to the 3-month and 1-year follow-up. Models were built using a Multilayer Perceptron (MLP) approach with SHAP analysis.</p> Results <p>Model-G (with GTV features) and Model-P (with pGTV features) achieved a validation area under curve (AUC) of 0.806 and 0.708, respectively. Meanwhile, Model-GP (with GTV and pGTV features) demonstrated an improved performance with a validation AUC of 0.851, reflecting the added value of peritumoral features. The Model-GPC, which incorporated GTV, pGTV, and clinical features, achieved a best validation AUC of 0.902, demonstrating the model’s ability to robustly integrate clinical and radiomic data for accurate local control prediction.</p> Conclusion <p>The Model-GPC, integrating clinical and radiomic features, accurately predicts post-SBRT local control in pulmonary oligometastases. Incorporating peritumoral features and SHAP analysis enhances prediction accuracy, offering insights to optimize SBRT strategies.</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

Integrating peritumor and tumor CT radiomics features in predicting local control after SBRT in patients with pulmonary oligometastases

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月14日

查看全文
  • Yao Lu,
  • Yiqi Wang,
  • Yuxi Ding,
  • Danni Chen,
  • Wenguang He,
  • Weixiang Zhong,
  • Jing Yang,
  • Senxiang Yan,
  • Ge Ren,
  • Feng Zhao

摘要

Purpose

Local control prediction for patients with pulmonary oligometastases underwent stereotactic body radiotherapy (SBRT) is crucial for optimizing therapeutic strategies. This study aims to develop and validate a predictive radiomics model integrating both tumor-intrinsic and peritumoral features along with clinical factors to enhance local control prediction using a multi-center dataset.

Materials and methods

We analyzed 223 tumors from 146 patients, which was divided into a training set (n = 165) and an external validation set (n = 58). Radiomic features from the gross tumor volume (GTV) and peritumoral regions (pGTV) representing the tumor microenvironment (TME) in CT images were extracted and combined with clinical factors to build a clinical outcome prediction model. Tumor response was classified into Favorable Response Group (FRG) and Unfavorable Response Group (URG) according to the 3-month and 1-year follow-up. Models were built using a Multilayer Perceptron (MLP) approach with SHAP analysis.

Results

Model-G (with GTV features) and Model-P (with pGTV features) achieved a validation area under curve (AUC) of 0.806 and 0.708, respectively. Meanwhile, Model-GP (with GTV and pGTV features) demonstrated an improved performance with a validation AUC of 0.851, reflecting the added value of peritumoral features. The Model-GPC, which incorporated GTV, pGTV, and clinical features, achieved a best validation AUC of 0.902, demonstrating the model’s ability to robustly integrate clinical and radiomic data for accurate local control prediction.

Conclusion

The Model-GPC, integrating clinical and radiomic features, accurately predicts post-SBRT local control in pulmonary oligometastases. Incorporating peritumoral features and SHAP analysis enhances prediction accuracy, offering insights to optimize SBRT strategies.