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Purpose <p>This study evaluates a three-dimensional (3D) deep learning (DL) model based on fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for predicting the preoperative status of spread through air spaces (STAS) in patients with clinical stage I lung adenocarcinoma (LUAD).</p> Methods <p>A retrospective analysis of 162 patients with stage I LUAD was conducted, splitting data into training and test sets (4:1). Six 3D DL models were developed, and the top-performing PET and CT models (ResNet50) were fused for optimal prediction. The model’s clinical utility was assessed through a two-stage reader study.</p> Results <p>The fused PET/CT model achieved an area under the curve (AUC) of 0.956 (95% CI 0.9230–0.9881) in the training set and 0.889 (95% CI 0.7624–1.0000) in the test set. Compared to three physicians, the model demonstrated superior sensitivity and specificity. After the artificial intelligence (AI) assistance's participation, the diagnostic accuracy of the physicians improved during their subsequent reading session.</p> Conclusion <p>Our DL model demonstrates potential as a resource to aid physicians in predicting STAS status and preoperative treatment planning for stage I LUAD, though prospective validation is required.</p>

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A PET/CT-based 3D deep learning model for predicting spread through air spaces in stage I lung adenocarcinoma

  • 期刊论文

  • 发表日期：

    2025年2月24日

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  • Cheng Zheng,
  • Yujie Cai,
  • Jiangfeng Miao,
  • BingShu Zheng,
  • Yan Gao,
  • Chen Shen,
  • ShanLei Bao,
  • ZhongHua Tan,
  • ChunFeng Sun

摘要

Purpose

This study evaluates a three-dimensional (3D) deep learning (DL) model based on fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for predicting the preoperative status of spread through air spaces (STAS) in patients with clinical stage I lung adenocarcinoma (LUAD).

Methods

A retrospective analysis of 162 patients with stage I LUAD was conducted, splitting data into training and test sets (4:1). Six 3D DL models were developed, and the top-performing PET and CT models (ResNet50) were fused for optimal prediction. The model’s clinical utility was assessed through a two-stage reader study.

Results

The fused PET/CT model achieved an area under the curve (AUC) of 0.956 (95% CI 0.9230–0.9881) in the training set and 0.889 (95% CI 0.7624–1.0000) in the test set. Compared to three physicians, the model demonstrated superior sensitivity and specificity. After the artificial intelligence (AI) assistance's participation, the diagnostic accuracy of the physicians improved during their subsequent reading session.

Conclusion

Our DL model demonstrates potential as a resource to aid physicians in predicting STAS status and preoperative treatment planning for stage I LUAD, though prospective validation is required.