Abstract
Purpose
To develop a computed tomography (CT)-based radiomics nomogram for pre-treatment prediction of histopathologic growth patterns (HGPs) in colorectal liver metastases (CRLM) and to validate its accuracy and clinical value.
Materials and methods
This retrospective study included a total of 197 CRLM from 92 patients. Lesions from CRLM were randomly divided into the training study (n = 137) and the validation study (n = 60) with the ratio of 3:1 for model construction and internal validation. The least absolute shrinkage and selection operator (LASSO) was used to screen features. Radiomics score (rad-score) was calculated to generate radiomics features. A predictive radiomics nomogram based on rad-score and clinical features was developed using random forest (RF). The performances of clinical model, radiomic model and radiomics nomogram were thoroughly evaluated by the DeLong test, decision curve analysis (DCA) and clinical impact curve (CIC) allowing for generation of an optimal predictive model.
Results
The radiological nomogram model consists of three independent predictors, including rad-score, T-stage, and enhancement rim on PVP. Training and validation results demonstrated the high-performance level of the model of area under curve (AUC) of 0.86 and 0.84, respectively. The radiomic nomogram model can achieve better diagnostic performance than the clinical model, yielding greater net clinical benefit compared to the clinical model alone.
Conclusions
A CT-based radiomics nomogram can be used to predict HGPs in CRLM. Preoperative non-invasive identification of HGPs could further facilitate clinical treatment and provide personalized treatment plans for patients with liver metastases from colorectal cancer.
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Data availability
The authors declare that all data and materials supporting the findings of this study are available within the article.
Abbreviations
- CT:
-
Computed tomography
- CECT:
-
Contrast enhanced computed tomography
- HGPs:
-
Histopathologic growth patterns
- CRLM:
-
Colorectal liver metastases
- ROI:
-
Region of interest
- PVP:
-
Portal venous phase
- LASSO:
-
Least absolute shrinkage and selection operator
- RF:
-
Random forest
- DCA:
-
Decision curve analysis
- CIC:
-
Clinical impact curve
- ICC:
-
Intra-class correlation coefficient
- AUC:
-
Area under the receiver operator characteristic curve
- CRC:
-
Colorectal cancer
- H&E:
-
Hematoxylin- and eosin-stained
- SVM:
-
Support vector machine
- XGBoost:
-
EXtreme Gradient Boosting
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Funding
This work was supported in part by foundation of the committee on science and technology of Tianjin (21JCQNJC01330) and Foundation of Tianjin Union Medical Center (2018YJ007 and 2017YJ015). The funders had no roles in the design of the study, data collection, analysis and interpretation, or decision to write and publish the work.
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CS: investigation, data curation, writing-original draft, software. XHL: data curation, writing-original draft. JS: investigation and supervision. LCD formal analysis, software. FW: formal analysis and methodology. JZ: validation. YL: conceptualization, review and editing. All authors read and approved the final manuscript.
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This retrospective study was submitted to the ethics committee of Tianjin Union Medicine Center for review and approval prior to the start of the clinical study.
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Sun, C., Liu, X., Sun, J. et al. A CT-based radiomics nomogram for predicting histopathologic growth patterns of colorectal liver metastases. J Cancer Res Clin Oncol 149, 9543–9555 (2023). https://doi.org/10.1007/s00432-023-04852-6
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DOI: https://doi.org/10.1007/s00432-023-04852-6