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A CT-Based Radiomics Nomogram in Predicting the Postoperative Prognosis of Colorectal Cancer: A Two-center Study

Published:March 25, 2022DOI:https://doi.org/10.1016/j.acra.2022.02.006

      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)
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