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Objective <p>The aim of this study was to develop a machine learning model that can predict spread through air space (STAS) of lung adenocarcinoma preoperatively. STAS is associated with poor prognosis in invasive lung adenocarcinoma. Therefore non-invasive and accurate pre-surgical prediction of STAS in patients with lung adenocarcinoma is essential for individualised patient management.</p> Methods <p>We included 138 patients with invasive lung adenocarcinoma who underwent lobectomy, collected their preoperative imaging data and clinical features, built a model for predicting STAS using machine learning and deep learning methods, and validated the efficacy of the model. Finally a nomogram was created based on logistic regression (LR).</p> Results <p>Imaging histology features showed good model efficacy in both the training set (LR AUC = 0.764) and the test set (LR AUC = 0.776), and we combined the imaging histology and clinical features to jointly build a nomogram graph (AUC = 0.878), extracted the deep learning features, and built a machine learning model based on the ResNET50 algorithm, where the LR AUC = 0.918.</p> Conclusions <p>This presented radiomics model can be served as a non-invasive for predicting STAS in Infiltrating lung adenocarcinoma.</p>

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Predicting spread through air space of lung adenocarcinoma based on deep learning and machine learning models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月14日

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  • Zengming Wang,
  • Lingxin Kong,
  • Bin Li,
  • Qingtao Zhao,
  • Xiaopeng Zhang,
  • Huanfen Zhao,
  • Wenfei Xue,
  • Wei Li,
  • Shun Xu,
  • Guochen Duan

摘要

Objective

The aim of this study was to develop a machine learning model that can predict spread through air space (STAS) of lung adenocarcinoma preoperatively. STAS is associated with poor prognosis in invasive lung adenocarcinoma. Therefore non-invasive and accurate pre-surgical prediction of STAS in patients with lung adenocarcinoma is essential for individualised patient management.

Methods

We included 138 patients with invasive lung adenocarcinoma who underwent lobectomy, collected their preoperative imaging data and clinical features, built a model for predicting STAS using machine learning and deep learning methods, and validated the efficacy of the model. Finally a nomogram was created based on logistic regression (LR).

Results

Imaging histology features showed good model efficacy in both the training set (LR AUC = 0.764) and the test set (LR AUC = 0.776), and we combined the imaging histology and clinical features to jointly build a nomogram graph (AUC = 0.878), extracted the deep learning features, and built a machine learning model based on the ResNET50 algorithm, where the LR AUC = 0.918.

Conclusions

This presented radiomics model can be served as a non-invasive for predicting STAS in Infiltrating lung adenocarcinoma.