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Background <p>CT images and circulating tumor cells (CTCs) are indispensable for diagnosing the mediastinal lesions by providing radiological and intra-tumoral information. This study aimed to develop and validate a deep multimodal fusion network (DMFN) combining CTCs and CT images for the multi-classification of mediastinal lesions.</p> Methods <p>In this retrospective diagnostic study, we enrolled 1074 patients with 1500 enhanced CT images and 1074 CTCs results between Jan 1, 2020, and Dec 31, 2023. Patients were divided into the training cohort (<i>n</i> = 434), validation cohort (<i>n</i> = 288), and test cohort (<i>n</i> = 352). The DMFN and monomodal convolutional neural network (CNN) models were developed and validated using the CT images and CTCs results. The diagnostic performances of DMFN and monomodal CNN models were based on the Paraffin-embedded pathologies from surgical tissues. The predictive abilities were compared with thoracic resident physicians, attending physicians, and chief physicians by the area under the receiver operating characteristic (ROC) curve, and diagnostic results were visualized in the heatmap.</p> Results <p>For binary classification, the predictive performances of DMFN (AUC = 0.941, 95% CI 0.901–0.982) were better than the monomodal CNN model (AUC = 0.710, 95% CI 0.664–0.756). In addition, the DMFN model achieved better predictive performances than the thoracic chief physicians, attending physicians, and resident physicians (<i>P</i> = 0.054, 0.020, 0.016) respectively. For the multiclassification, the DMFN achieved encouraging predictive abilities (AUC = 0.884, 95%CI 0.837–0.931), significantly outperforming the monomodal CNN (AUC = 0.722, 95%CI 0.705–0.739), also better than the chief physicians (AUC = 0.787, 95%CI 0.714–0.862), attending physicians (AUC = 0.632, 95%CI 0.612–0.654), and resident physicians (AUC = 0.541, 95%CI 0.508–0.574).</p> Conclusions <p>This study showed the feasibility and effectiveness of CNN model combing CT images and CTCs levels in predicting the diagnosis of mediastinal lesions. It could serve as a useful method to assist thoracic surgeons in improving diagnostic accuracy and has the potential to make management decisions.</p>

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A deep learning model combining circulating tumor cells and radiological features in the multi-classification of mediastinal lesions in comparison with thoracic surgeons: a large-scale retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月7日

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  • Feng Wang,
  • Minwei Bao,
  • Bo Tao,
  • Fugui Yang,
  • Guangxue Wang,
  • Lei Zhu

摘要

Background

CT images and circulating tumor cells (CTCs) are indispensable for diagnosing the mediastinal lesions by providing radiological and intra-tumoral information. This study aimed to develop and validate a deep multimodal fusion network (DMFN) combining CTCs and CT images for the multi-classification of mediastinal lesions.

Methods

In this retrospective diagnostic study, we enrolled 1074 patients with 1500 enhanced CT images and 1074 CTCs results between Jan 1, 2020, and Dec 31, 2023. Patients were divided into the training cohort (n = 434), validation cohort (n = 288), and test cohort (n = 352). The DMFN and monomodal convolutional neural network (CNN) models were developed and validated using the CT images and CTCs results. The diagnostic performances of DMFN and monomodal CNN models were based on the Paraffin-embedded pathologies from surgical tissues. The predictive abilities were compared with thoracic resident physicians, attending physicians, and chief physicians by the area under the receiver operating characteristic (ROC) curve, and diagnostic results were visualized in the heatmap.

Results

For binary classification, the predictive performances of DMFN (AUC = 0.941, 95% CI 0.901–0.982) were better than the monomodal CNN model (AUC = 0.710, 95% CI 0.664–0.756). In addition, the DMFN model achieved better predictive performances than the thoracic chief physicians, attending physicians, and resident physicians (P = 0.054, 0.020, 0.016) respectively. For the multiclassification, the DMFN achieved encouraging predictive abilities (AUC = 0.884, 95%CI 0.837–0.931), significantly outperforming the monomodal CNN (AUC = 0.722, 95%CI 0.705–0.739), also better than the chief physicians (AUC = 0.787, 95%CI 0.714–0.862), attending physicians (AUC = 0.632, 95%CI 0.612–0.654), and resident physicians (AUC = 0.541, 95%CI 0.508–0.574).

Conclusions

This study showed the feasibility and effectiveness of CNN model combing CT images and CTCs levels in predicting the diagnosis of mediastinal lesions. It could serve as a useful method to assist thoracic surgeons in improving diagnostic accuracy and has the potential to make management decisions.