Scholarlink

<p>Accurate preoperative prediction of occult lymph node metastasis (OLNM) in early-stage non-small cell lung cancer (NSCLC) is crucial for treatment planning. This study aimed to develop and validate a CT-based three-dimensional (3D) deep learning model to predict OLNM. In this retrospective, multicenter study, 900 patients from two hospitals were included and divided into a primary cohort (<i>n</i> = 500) for model development and an external test cohort (<i>n</i> = 400) for independent validation. We proposed a 3D EfficientNet model, and its diagnostic performance was compared against other benchmark deep learning architectures and four experienced radiologists. The proposed model achieved area under the receiver operating characteristic curve (AUC) of 0.8907 (95% CI 0.7878–0.9691) in the internal test set and 0.8721 (95% CI 0.8200–0.9170) in the external test cohort. This performance was superior to that of the other convolutional neural networks and the radiologists (all <i>P</i> &lt; 0.05). Furthermore, interpretability analysis using Grad-CAM indicated that the model’s predictions were based on distinct attention patterns. In conclusion, our 3D EfficientNet model demonstrates significant potential as a non-invasive and accurate tool for predicting OLNM in early-stage NSCLC. It can effectively support clinicians in making more precise staging and treatment decisions.</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

Interpretable Deep Learning with Multi-Scale CT for Predicting Occult Lymph Node Metastasis in Early-Stage NSCLC: A Multicenter Study

  • 期刊论文

  • 发表日期：

    2026年4月27日

查看全文
  • Zikang Yan,
  • Xiaojuan Deng,
  • Jun Dang,
  • Shangzhi Cai,
  • Wentao Zhang,
  • Shijiao Pan,
  • Tao Zhang,
  • Hao Wu,
  • Longke Ran

摘要

Accurate preoperative prediction of occult lymph node metastasis (OLNM) in early-stage non-small cell lung cancer (NSCLC) is crucial for treatment planning. This study aimed to develop and validate a CT-based three-dimensional (3D) deep learning model to predict OLNM. In this retrospective, multicenter study, 900 patients from two hospitals were included and divided into a primary cohort (n = 500) for model development and an external test cohort (n = 400) for independent validation. We proposed a 3D EfficientNet model, and its diagnostic performance was compared against other benchmark deep learning architectures and four experienced radiologists. The proposed model achieved area under the receiver operating characteristic curve (AUC) of 0.8907 (95% CI 0.7878–0.9691) in the internal test set and 0.8721 (95% CI 0.8200–0.9170) in the external test cohort. This performance was superior to that of the other convolutional neural networks and the radiologists (all P < 0.05). Furthermore, interpretability analysis using Grad-CAM indicated that the model’s predictions were based on distinct attention patterns. In conclusion, our 3D EfficientNet model demonstrates significant potential as a non-invasive and accurate tool for predicting OLNM in early-stage NSCLC. It can effectively support clinicians in making more precise staging and treatment decisions.