Highlights
- •Computed tomography-based radiomics showed satisfactory diagnostic significance for predicting overall survival in colorectal cancer.
- •The clinical-combined model integrated the optimal radiomics signature, clinical predictors, and genetic characteristics (KRAS/BRAF mutation status) showed a better predictive value of predicting overall survival in colorectal cancer patients.
Rationale and Objectives
This retrospective study aimed to develop a practical model to determine overall survival
after surgery in patients with colorectal cancer according to radiomics signatures
based on computed tomography (CT) images and clinical predictors.
Materials and Methods
A total of 121 colorectal cancer (CRC) patients were selected to construct the model,
and 51 patients and 114 patients were selected for internal validation and external
testing. The radiomics features were extracted from each patient's CT images. Univariable
Cox regression and least absolute shrinkage and selection operator regression were
used to select radiomics features. The performance of the nomogram was evaluated by
calibration curves and the c-index. Kaplan–Meier analysis was used to compare the
overall survival between these subgroups.
Results
The radiomics features of the CRC patients were significantly correlated with survival
time. The c-indexes of the nomogram in the training cohort, internal validation cohort
and external test cohort were 0.782, 0.721, and 0.677. Our nomogram integrated the
optimal radiomics signature with clinical predictors showed a significant improvement
in the prediction of CRC patients’ overall survival. The calibration curves showed
that the predicted survival time was close to the actual survival time. According
to Kaplan–Meier analysis, the 1-, 2-, and 3-year survival rates of the low-risk group
were higher than those of the high-risk group.
Conclusion
The nomogram combining the optimal radiomics signature and clinical predictors further
improved the predicted accuracy of survival prognosis for CRC patients. These findings
might affect treatment strategies and enable a step forward for precise medicine.
Key Words
Abbreviations:
CRC (colorectal cancer), AUC (area under the ROC curve), CEA (carcinoembryonic antigen), DCA (decision curve analysis), EGFR (epidermal growth factor receptor), MAPK (mitogen-activated protein kinase), PI3K (phosphatidylinositol 3 kinase), FFPE (formalin-fixed, paraffin-embedded), GLCM (gray-level co-occurrence matrix), GLDM (gray-level dependence matrix), GLRLM (gray-level run length matrix), GLSZM (gray-level size zone matrix), KRAS (Kirsten rat sarcoma), BRAF (v-raf murine sarcoma viral oncogene homolog B1), LR (logistic regression), LVI (lymphangiovascular invasion), CT (computed tomography), NCCN (National Comprehensive Cancer Network), PACS (picture archiving and communication system), ROC (receiver operating characteristic), ROI (regions of interests)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: March 25, 2022
Accepted:
February 6,
2022
Received in revised form:
January 25,
2022
Received:
December 14,
2021
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
