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Background <p>Deep learning (DL) demonstrates high sensitivity but low specificity in lung cancer (LC) detection during CT screening, and the seven Tumor-associated antigens autoantibodies (7-TAAbs), known for its high specificity in LC, was employed to improve the DL’s specificity for the efficiency of LC screening in China.</p> Purpose <p>To develop and evaluate a risk model combining 7-TAAbs test and DL scores for diagnosing LC with pulmonary lesions &lt; 70&#xa0;mm.</p> Materials and methods <p>Four hundreds and six patients with 406 lesions were enrolled and assigned into training set (<i>n</i> = 313) and test set (<i>n</i> = 93) randomly. The malignant lesions were defined as those lesions with high malignant risks by DL or those with positive expression of 7-TAAbs panel. Model performance was assessed using the area under the receiver operating characteristic curves (AUC).</p> Results <p>In the training set, the AUCs for DL, 7-TAAbs, combined model (DL and 7-TAAbs) and combined model (DL or 7-TAAbs) were 0.771, 0.638, 0.606, 0.809 seperately. In the test set, the combined model (DL or 7-TAAbs) achieved achieved the highest sensitivity (82.6%), NPV (81.8%) and accuracy (79.6%) among four models, and the AUCs of DL model, 7-TAAbs model, combined model (DL and 7-TAAbs), and combined model (DL or 7-TAAbs) were 0.731, 0.679, 0.574, and 0.794, respectively.</p> Conclusion <p>The 7-TAAbs test significantly enhances DL performance in predicting LC with pulmonary leisons &lt; 70&#xa0;mm in China.</p>

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Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月29日

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  • Qingcheng Meng,
  • Pengfei Ren,
  • Lanwei Guo,
  • Pengrui Gao,
  • Tong Liu,
  • Wenda Chen,
  • Wentao Liu,
  • Hui Peng,
  • Mengjia Fang,
  • Shuo Meng,
  • Hong Ge,
  • Meng Li,
  • Xuejun Chen

摘要

Background

Deep learning (DL) demonstrates high sensitivity but low specificity in lung cancer (LC) detection during CT screening, and the seven Tumor-associated antigens autoantibodies (7-TAAbs), known for its high specificity in LC, was employed to improve the DL’s specificity for the efficiency of LC screening in China.

Purpose

To develop and evaluate a risk model combining 7-TAAbs test and DL scores for diagnosing LC with pulmonary lesions < 70 mm.

Materials and methods

Four hundreds and six patients with 406 lesions were enrolled and assigned into training set (n = 313) and test set (n = 93) randomly. The malignant lesions were defined as those lesions with high malignant risks by DL or those with positive expression of 7-TAAbs panel. Model performance was assessed using the area under the receiver operating characteristic curves (AUC).

Results

In the training set, the AUCs for DL, 7-TAAbs, combined model (DL and 7-TAAbs) and combined model (DL or 7-TAAbs) were 0.771, 0.638, 0.606, 0.809 seperately. In the test set, the combined model (DL or 7-TAAbs) achieved achieved the highest sensitivity (82.6%), NPV (81.8%) and accuracy (79.6%) among four models, and the AUCs of DL model, 7-TAAbs model, combined model (DL and 7-TAAbs), and combined model (DL or 7-TAAbs) were 0.731, 0.679, 0.574, and 0.794, respectively.

Conclusion

The 7-TAAbs test significantly enhances DL performance in predicting LC with pulmonary leisons < 70 mm in China.