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A radio-pathologic integrated model for prediction of lymph node metastasis stage in patients with gastric cancer

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Abstract

Background

Accurate prediction of lymph node metastasis stage (LNMs) facilitates precision therapy for gastric cancer. We aimed to develop and validate a deep learning-based radio-pathologic model to predict the LNM stage in patients with gastric cancer by integrating CT images and histopathological whole-slide images (WSIs).

Methods

A total of 252 patients were enrolled and randomly divided into a training set (n = 202) and a testing set (n = 50). Both pretreatment contrast-enhanced abdominal CT and WSI of biopsy specimens were collected for each patient. The deep radiologic and pathologic features were extracted from CT and WSI using ResNet-50 and Vision Transformer (ViT) network, respectively. By fusing both radiologic and pathologic features, a radio-pathologic integrated model was constructed to predict the five LNM stages. For comparison, four single-modality models using CT images or WSIs were also constructed, respectively. All models were trained on the training set and validated on the testing set.

Results

The radio-pathologic integrated mode achieved an overall accuracy of 84.0% and a kappa coefficient of 0.795 on the testing set. The areas under the curves (AUCs) of the integrated model in predicting the five LNM stages were 0.978 (95% Confidence Interval (CI 0.917–1.000), 0.946 (95% CI 0.867–1.000), 0.890 (95% CI 0.718–1.000), 0.971 (95% CI 0.920–1.000), and 0.982 (95% CI 0.911–1.000), respectively. Moreover, the integrated model achieved an AUC of 0.978 (95% CI 0.912–1.000) in predicting the binary status of nodal metastasis.

Conclusion

Our study suggests that radio-pathologic integrated model that combined both macroscale radiologic image and microscale pathologic image can better predict lymph node metastasis stage in patients with gastric cancer.

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Data availability

The data and materials used to support the findings of this study are available from the corresponding authors upon request.

Abbreviations

ACC:

Accuracy

AUC:

Areas under the curves

F1-score:

Balanced F score

CAM:

Class activation maps

CT:

Computed tomography

CI:

Confidence interval

CNN:

Convolutional neural network

GC:

Gastric cancer

LNMs:

Lymph node metastasis stage

MRI:

Magnetic resonance image

ROC:

Receiver operating characteristic

SD:

Standard deviation

3D:

Three dimension

TNM:

Tumor node and metastasis

ViT:

Vision transformer

WSI:

Whole-slide image

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (12126608, 61901458, 62201557, and U20A20171) and Guangdong Basic and Applied Basic Research Foundation (2021A1313110585).

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Author notes

  1. Yuanshen Zhao and Longsong Li have equally contributed to this work.

Authors and Affiliations

  1. Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Yuanshen Zhao, Jingxian Duan, Qiuchang Sun, Chaofan Zhu, Dong Liang & Zhi-Cheng Li

  2. Department of Gastroenterology, The First Medical Center of Chinese, PLA General Hospital, Beijing, 100853, China

    Longsong Li, Ke Han & Ningli Chai

  3. Department of Radiology, The First Medical Center of Chinese, PLA General Hospital, Beijing, China

    Tao Li

  4. National Innovation Center for Advanced Medical Devices, Shenzhen, China

    Dong Liang & Zhi-Cheng Li

  5. Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China

    Dong Liang & Zhi-Cheng Li

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  1. Yuanshen Zhao
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Correspondence to Ningli Chai or Zhi-Cheng Li.

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This retrospective study was approved by the Medial Ethics Committee of the XXX (No. S2021-146-03) and in conformity to the Declaration of Helsinki and its later amendments or comparable ethical standards.

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Zhao, Y., Li, L., Han, K. et al. A radio-pathologic integrated model for prediction of lymph node metastasis stage in patients with gastric cancer. Abdom Radiol 48, 3332–3342 (2023). https://doi.org/10.1007/s00261-023-04037-2

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  • DOI: https://doi.org/10.1007/s00261-023-04037-2

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