A nomogram based on dual-layer spectral detector CT-derived 40KeV virtual monoenergetic images for preoperative prediction of simultaneous distant metastasis in colorectal cancer

Abstract

BACKGROUND: To establish and validate a nomogram combining dual-layer spectral detector CT(DLSCT)-derived 40KeV virtual monoenergetic images (VMI) of radiomics features, spectral parameters and clinical features for preoperative prediction of simultaneous distant metastases (SDM) in colorectal cancer (CRC). METHODS: (n = 86)] with pathologically confirmed CRC who attended two hospitals between June 2022 and April 2023. Patients were divided into a training group (n = 90, from hospital A) and an external validation group (n = 47, from hospital B). Clinical characteristics and spectral parameters of arterial phase (AP), venous phase (VP) and delayed phase (DP) were collected to establish a clinical model. Radiomics modeling by extracting radiomics features in the three-dimensional region of interest of the 40 KeV-VMI. Combining radiomics scores, clinical features, and spectral parameters to create a nomogram. The performance of each model was assessed by the area under the curve (AUC), plotting calibration curves and decision curve analysis (DCA). RESULTS: In the training group, the nomogram (AUC = 0.938) was remarkably better than that of the radiomics models. In the external validation group, the nomogram (AUC = 0.930) was remarkably superior to that of the DP model and the clinical model. In the vast majority of threshold probabilities, the nomogram had a better critical net benefit than the other four models in predicting SDM of CRC. CONCLUSIONS: The nomogram incorporating radiomics features of DLSCT-derived 40KeV-VMI, spectral parameters and clinical features showed excellent predictive performance in preoperatively predicting SDM in CRC, which can help clinicians make accurate individualized treatment plans.

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