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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.
Sun et al. (Tue,) studied this question.