4439
MRI-based radiomics nomogram for preoperatively differentiating intrahepatic cholangiocarcinoma from colorectal liver metastases Abstract
Ying Xu1, Lu Li1, Yi Yang1, Feng Ye1, Sicong Wang2, Lizhi Xie2, Yanan Wang3, and Xinming Zhao1
1Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, China, 2GE Healthcare, China, Beijing, China, 3Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zheng Zhou, China
1Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, China, 2GE Healthcare, China, Beijing, China, 3Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zheng Zhou, China
Synopsis
Keywords: Liver, Radiomics, Cholangiocarcinoma; colorectal liver metastases; nomogram; differential diagnosis.
A total of 133 patients in training cohort (64 IMCC and 69 CRLM) and 57 patients in validation cohort (29 IMCC and 28 CRLM) were included. Radiomics features were extracted from the DCE-MR images and selected by the least absolute shrinkage and selection operator algorithm to establish the radiomics model.The radiomics nomogram was constructed combining the radiomics model and clinical model.The radiomics nomogram combining radiomics signatures based on DCE-MRI with clinical factors (serum CEA level and tumor diameter) may provide a reliable and noninvasive tool to discriminate IMCC from CRLM, which could help guide treatment strategies and prognosis prediction preoperatively.Objectives
To establish a radiomics nomogram based on dynamic contrast-enhanced (DCE) MR images for preoperatively differentiating intrahepatic mass-forming cholangiocarcinoma (IMCC) from colorectal cancer liver metastasis (CRLM).Methods
A total of 133 patients in training cohort (64 IMCC and 69 CRLM) and 57 patients in validation cohort (29 IMCC and 28 CRLM) were included. Radiomics features were extracted from the DCE-MR images and selected by the least absolute shrinkage and selection operator algorithm to establish the radiomics model. Clinical variables and MRI findings were assessed and selected by univariate and multivariate analyses to construct a clinical model. The radiomics nomogram was constructed combining the radiomics model and clinical model. Performance of the radiomics nomogram, radiomics model, and clinical model was evaluated by receiver operating characteristic and decision curve analysis.Results
Six features were selected to construct the radiomics model. The radiomics signature showed better discrimination than the clinical model in the training cohort (AUC [area under the curve], 0.92; 95% confidence interval [CI], 0.87-0.96 vs AUC, 0.74; 95% CI, 0.66-0.83;) and the internal validation cohort (AUC, 0.91; 95% CI, 0.84-0.98 vs AUC, 0.73; 95% CI, 0.60-0.86). The radiomics nomogram incorporated CEA, tumor diameter and radiomics signatures. It showed favorable calibration and best discrimination performance in the training cohort (AUC,0.94; 95% CI, 0.90-0.97) and the internal validation cohort (AUC, 0.93; 95% CI, 0.86–1.00) in the three models. Decision curve analysis demonstrated that the radiomics nomogram outperformed the radiomics model and clinical model.Conclusions
The radiomics nomogram combining radiomics signatures based on DCE-MRI with clinical factors (serum CEA level and tumor diameter) may provide a reliable and noninvasive tool to discriminate IMCC from CRLM, which could help guide treatment strategies and prognosis prediction preoperatively.Acknowledgements
NoneReferences
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Figure
1. Flowchart of patient selection.
Figure
2. Workflow of the key steps to conduct radiomics
analysis and the three models.
Figure
3. Violin plots comparing the Rad-score of the
IMCC and CRLM in the training cohort and internal validation cohort.
Figure
4. The radiomics nomogram incorporating the CEA
level, tumor diameter, and radiomics signature (Rad-score).
Figure
5. Calibrations of the nomogram in the training
cohort (A), internal validation cohort (B) and external validation cohort (C).
Diagnostic performance of the radiomics model, clinical model and radiomics
nomogram was compared through ROC curves in the training cohort (D), internal
validation cohort (E) and external validation cohort (F).
DOI: https://doi.org/10.58530/2023/4439