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Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer

  • Hepatobiliary
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objective

Our study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to predict serosal involvement in gallbladder cancer (GBC) patients.

Materials and methods

A total of 152 patients diagnosed with GBC were retrospectively enrolled and divided into the serosal involvement group and no serosal involvement group according to paraffin pathology results. The regions of interest (ROIs) in the lesion on all CT images were drawn by two radiologists using ITK-SNAP software (version 3.8.0). A total of 412 features were extracted from the CT images of each patient. The Mann‒Whitney U test was applied to identify features with significant differences between groups. Seven machine learning algorithms and a deep learning model based on fully connected neural networks (f-CNNs) were used for radiomics model construction. The prediction efficacy of the models was evaluated using receiver operating characteristic (ROC) curve analysis.

Results

Through the Mann‒Whitney U test, 75 of the 412 features extracted from the CT images of patients were significantly different between groups (P < 0.05). Among all the algorithms, logistic regression achieved the highest performance with an area under the curve (AUC) of 0.944 (sensitivity 0.889, specificity 0.8); the f-CNN deep learning model had an AUC of 0.916, and the model showed high predictive power for serosal involvement, with a sensitivity of 0.733 and a specificity of 0.801.

Conclusion

Radiomics models based on features derived from CECT showed convincing performances in predicting serosal involvement in GBC.

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Funding

This study was funded by National High Level Hospital Clinical Research Funding (Grant No. 2022-PUMCH-B-003).

Author information

Author notes

  1. Xianlin Han and Xiaodong He have contributed to the work equally.

Authors and Affiliations

  1. Department of Gastrointestinal Surgery, China-Japan Friendship Hospital, Beijing, China

    Shengnan Zhou

  2. General Surgery Department, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China

    Shaoqi Han

  3. General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China

    Weijie Chen, Xuesong Bai, Xianlin Han & Xiaodong He

  4. Radiology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, Beijing, China

    Weidong Pan

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  1. Shengnan Zhou
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Zhou, S., Han, S., Chen, W. et al. Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer. Abdom Radiol 49, 3–10 (2024). https://doi.org/10.1007/s00261-023-04029-2

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  • DOI: https://doi.org/10.1007/s00261-023-04029-2

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