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Preoperative Prediction of BRAF Mutation Status in Colorectal Cancer Using a Clinical-radiomics Model

Published:January 13, 2022DOI:https://doi.org/10.1016/j.acra.2021.12.016

      Highlights

      • CT-based radiomics showed satisfactory diagnostic significance for the BRAF status in colorectal cancer.
      • Key baseline clinical characteristics were associated with BRAF mutations.
      • The combined model may be applied in the individual preoperative prediction of BRAF mutation.

      Rationale and Objectives

      This study aimed to develop a clinically practical model to predict V-raf murine sarcoma viral oncogene homolog B1 (BRAF) mutation in colorectal cancer according to radiomic signatures based on computed tomography (CT) and clinical risk factors, and to determine the model's diagnostic accuracy for BRAF mutation status.

      Materials and Methods

      This retrospective study included 140 patients with colorectal cancer. The significant clinical risk factors were used to build the clinical model; the least absolute shrinkage and selection operator algorithm was adopted to construct a radiomics signature according to imaging features of the tumor lesion, and stepwise logistic regression was applied to select the significant variables to develop the clinical-radiomics model. The predictive performance was evaluated by receiver operating characteristic curve analysis, calibration curve analysis, and decision curve analysis.

      Results

      The radscore, generated by 5 selected radiomics features, demonstrated a favorable ability to predict BRAF mutation in both the training (area under the receiver operating characteristic curve [AUC] 0.93) and validation (AUC 0.87) cohorts. Subsequently, integrating two independent predictors (including the radscore and clinical risk factors) into a nomogram exhibited more favorable discriminatory performance, with the AUC improved to 0.95 and 0.88 in both cohorts. Moreover, the accuracy for predicting BRAF mutations was higher than that of the clinical model, ranging from 0.70 to 0.89.

      Conclusion

      The proposed CT-based radiomics signature is associated with BRAF mutations. The present study also proposes a combined model can potentially be applied in the individual preoperative prediction of BRAF mutation status in colorectal cancer.

      Advances in knowledge

      CT-based radiomics showed satisfactory diagnostic significance for the BRAF status in colorectal cancer, the clinical-combined model may be applied in the individual preoperative prediction of BRAF mutation.

      Key Words

      Abbreviations:

      CRC (Colorectal cancer), AUC (Area under the ROC curve), A/G (Albumin/globulin ratio), 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), SDE (Small dependence emphasis), KRAS (Kirsten rat sarcoma), BRAF (V-raf murine sarcoma viral oncogene homolog B1), LR (Logistic regression), SVM (Support vector machine), LVI (Lymphangiovascular invasion), PNI (Peripheral nerve infiltration), CT (Computed tomography), NCCN (National Comprehensive Cancer Network), PACS (Picture archiving and communication system), ROC (Receiver operating characteristic), ROI (Regions of interests), VOI (Volume of interest)
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