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Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: A multicentre cohort study

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

Postoperative early recurrence (ER) leads to a poor prognosis for intrahepatic cholangiocarcinoma (ICC). We aimed to develop machine learning (ML) radiomics models to predict ER in ICC after curative resection.

Methods

Patients with ICC undergoing curative surgery from three institutions were retrospectively recruited and assigned to training and external validation cohorts. Preoperative arterial and venous phase contrast-enhanced computed tomography (CECT) images were acquired and segmented. Radiomics features were extracted and ranked through their importance. Univariate and multivariate logistic regression analysis was used to identify clinical characteristics. Various ML algorithms were used to construct radiomics-based models, and the predictive performance was evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.

Results

127 patients were included for analysis: 90 patients in the training set and 37 patients in the validation set. Ninety-two patients (72.4%) experienced recurrence, including 71 patients exhibiting ER. Male sex, microvascular invasion, TNM stage, and serum CA19-9 were identified as independent risk factors for ER, with the corresponding clinical model having a poor predictive performance (AUC of 0.685). Fifty-seven differential radiomics features were identified, and the 10 most important features were utilized for modelling. Seven ML radiomics models were developed with a mean AUC of 0.87 ± 0.02, higher than the clinical model. Furthermore, the clinical-radiomics models showed similar predictive performance to the radiomics models (AUC of 0.87 ± 0.03).

Conclusion

ML radiomics models based on CECT are valuable in predicting ER in ICC.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank our radiological colleagues and the clinicians for their assistance in collection and analysis of clinical information.

Funding

This study was supported by National Natural Science Foundation of China (Grant numbers: 82072685, 81703310, 81772628), and Science and Technology Plan Project of Wenzhou (Grant number Y2020938).

Author information

Author notes

  1. Zhiyuan Bo, Bo Chen and Yi Yang are Equal contribution.

Authors and Affiliations

  1. Department of Hepatobiliary Surgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

    Zhiyuan Bo, Bo Chen, Jiangqiao Yao, Jinhuan Yang, Qikuan He, Zhengping Yu & Gang Chen

  2. Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

    Zhiyuan Bo & Gang Chen

  3. Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China

    Yi Yang & Yi Wang

  4. Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

    Fei Yao & Yunjun Yang

  5. Department of Optometry and Ophthalmology College, Wenzhou Medical University, Wenzhou, China

    Yicheng Mao

  6. Department of Oncology, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China

    Zhengxiao Zhao

  7. Department of Hepatobiliary Surgery, the Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China

    Xintong Shi

  8. Department of General Surgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

    Jicai Chen

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  1. Zhiyuan Bo
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Contributions

Zhiyuan Bo, Bo Chen, Yi Wang and Gang Chen contributed to the study conception and design. Material preparation, data collection and analysis were performed by Zhiyuan Bo, Bo Chen, Yi Yang, Fei Yao, Yicheng Mao, Jiangqiao Yao, Jinhuan Yang, Qikuan He, Zhengxiao Zhao, Xintong Shi, Jicai Chen, Zhengping Yu, and Yunjun Yang. The first draft of the manuscript was written by Zhiyuan Bo, Bo Chen, Yi Yang, Zhengxiao Zhao, Yi Yang and Gang Chen. The draft revising and study supervision were performed by Zhiyuan Bo, Bo Chen, Yi Yang, Yi Wang, Jicai Chen and Gang Chen. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yunjun Yang, Yi Wang or Gang Chen.

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Ethics approval

The study was approved by the Ethics Committee of local institutional review boards and adhered to the Declaration of Helsinki. Written informed consents containing information such as the use of clinical data for scientific research were obtained from each patient before surgery.

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Informed consent was obtained from all individual participants included in the study.

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Supplementary material

ESM 1

(PNG 1677 kb) Supplementary Figure 1. Representative ROI delineation from preoperative arterial and venous phase CECT images of ICC patients. (A) primary arterial phase image and ROI (red region) marked image; (B) primary venous phase image and ROI (red region) marked image. ICC, intrahepatic cholangiocarcinoma; ROI, region of interest; CECT, contrast-enhanced computed tomography.

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ESM 2

(PNG 2168 kb) Supplementary Figure 2. Construction of new ML models by swapping the parameters of previous radiomics and clinical-radiomics models. (A) ROC curves of the new radiomics models using the parameters of the previous clinical-radiomics models; (B) Calibration plots of the new radiomics models; (C) Decision curve analysis of the new radiomics models. (D) ROC curves of the new clinical-radiomics models using the parameters of the previous radiomics models; (E) Calibration plot of the new clinical-radiomics models; (F) Decision curve analysis of the new clinical-radiomics models. ML, machine learning; ROC, receiver operating characteristic curves; TPR, true positive rate; FPR, false positive rate; AUC, area under the receiver operating characteristic curve; SVM, Support Vector Machine; LightGBM, Light Gradient Boosting Machine; XGBoost, eXtreme Gradient Boosting.

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ESM 3

(PNG 2177 kb) Supplementary Figure 3. Construction of the ML radiomics-based models using the radiomics features derived from arterial phase CECT images. (A) ROC curves of the ML radiomics models; (B) Calibration plots of the ML radiomics models; (C) Decision curve analysis of the ML radiomics models. (D) ROC curves of the ML clinical-radiomics models; (E) Calibration plot of the ML clinical-radiomics models; (F) Decision curve analysis of the ML clinical-radiomics models. ML, machine learning; ROC, receiver operating characteristic curves; TPR, true positive rate; FPR, false positive rate; AUC, area under the receiver operating characteristic curve; SVM, Support Vector Machine; LightGBM, Light Gradient Boosting Machine; XGBoost, eXtreme Gradient Boosting; CECT, contrast-enhanced computed tomography.

High resolution image (TIF 3146 kb)

ESM 4

(PNG 2160 kb) Supplementary Figure 4. Construction of the ML radiomics-based models using the radiomics features derived from venous phase CECT images. (A) ROC curves of the ML radiomics models; (B) Calibration plots of the ML radiomics models; (C) Decision curve analysis of the ML radiomics models. (D) ROC curves of the ML clinical-radiomics models; (E) Calibration plot of the ML clinical-radiomics models; (F) Decision curve analysis of the ML clinical-radiomics models. ML, machine learning; ROC, receiver operating characteristic curves; TPR, true positive rate; FPR, false positive rate; AUC, area under the receiver operating characteristic curve; SVM, Support Vector Machine; LightGBM, Light Gradient Boosting Machine; XGBoost, eXtreme Gradient Boosting; CECT, contrast-enhanced computed tomography.

High resolution image (TIF 3174 kb)

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Bo, Z., Chen, B., Yang, Y. et al. Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: A multicentre cohort study. Eur J Nucl Med Mol Imaging 50, 2501–2513 (2023). https://doi.org/10.1007/s00259-023-06184-6

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