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Background <p>This study aims to explore the feasibility to automate the application process of nomograms in clinical medicine, demonstrated through the task of preoperative pleural invasion prediction in non-small cell lung cancer patients using PET/CT imaging.</p> Results <p>The automatic pipeline involves multimodal segmentation, feature extraction, and model prediction. It is validated on a cohort of 1116 patients from two medical centers. The performance of the feature-based diagnostic model outperformed both the radiomics model and individual machine learning models. The segmentation models for CT and PET images achieved mean dice similarity coefficients of 0.85 and 0.89, respectively, and the segmented lung contours showed high consistency with the actual contours. The automatic diagnostic system achieved an accuracy of 0.87 in the internal test set and 0.82 in the external test set, demonstrating comparable overall diagnostic performance to the human-based diagnostic model. In comparative analysis, the automatic diagnostic system showed superior performance relative to other segmentation and diagnostic pipelines.</p> Conclusions <p>The proposed automatic diagnostic system provides an interpretable, automated solution for predicting pleural invasion in non-small cell lung cancer.</p>

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A strategy for the automatic diagnostic pipeline towards feature-based models: a primer with pleural invasion prediction from preoperative PET/CT images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月12日

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  • Xiangxing Kong,
  • Annan Zhang,
  • Xin Zhou,
  • Meixin Zhao,
  • Jiayue Liu,
  • Xinliang Zhang,
  • Weifang Zhang,
  • Xiangxi Meng,
  • Nan Li,
  • Zhi Yang

摘要

Background

This study aims to explore the feasibility to automate the application process of nomograms in clinical medicine, demonstrated through the task of preoperative pleural invasion prediction in non-small cell lung cancer patients using PET/CT imaging.

Results

The automatic pipeline involves multimodal segmentation, feature extraction, and model prediction. It is validated on a cohort of 1116 patients from two medical centers. The performance of the feature-based diagnostic model outperformed both the radiomics model and individual machine learning models. The segmentation models for CT and PET images achieved mean dice similarity coefficients of 0.85 and 0.89, respectively, and the segmented lung contours showed high consistency with the actual contours. The automatic diagnostic system achieved an accuracy of 0.87 in the internal test set and 0.82 in the external test set, demonstrating comparable overall diagnostic performance to the human-based diagnostic model. In comparative analysis, the automatic diagnostic system showed superior performance relative to other segmentation and diagnostic pipelines.

Conclusions

The proposed automatic diagnostic system provides an interpretable, automated solution for predicting pleural invasion in non-small cell lung cancer.