Abstract
The aim of the present study was to develop and evaluate a clinical-imaging-radiomic nomogram based on pre-enhanced computed tomography (CT) for pre-operative differentiation lipid-poor adenomas (LPAs) from metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas (AIs). A total of 196 consecutive patients with lung cancer, who underwent initial chest or abdominal pre-enhanced CT scan with small hyperattenuating AIs, were included. The patients were randomly divided into a training cohort with 71 cases of LPAs and 66 cases of metastases, and a testing cohort with 31 cases of LPAs and 28 cases of metastases. Plain CT radiological and clinical features were evaluated, including sex, age, size, pre-enhanced CT value (CT(pre)), shape, homogeneity and border. A total of 1,316 radiomic features were extracted from the plain CT images of the AIs, and the significant features selected by the least absolute shrinkage and selection operator were used to establish a Radscore. Subsequently, a clinical-imaging-radiomic model was developed by multivariable logistic regression incorporating the Radscore with significant clinical and imaging features. This model was then presented as a nomogram. The performance of the nomogram was assessed by calibration curves and decision curve analysis (DCA). A total of 4 significant radiomic features were incorporated in the Radscore, which yielded notable area under the receiver operating characteristic curves (AUCs) of 0.920 in the training dataset and 0.888 in the testing dataset. The clinical-imaging-radiomic nomogram incorporating the Radscore, CT(pre), sex and age revealed favourable differential diagnostic performance (AUC: Training, 0.968; testing, 0.915) and favourable calibration curves. The nomogram was revealed to be more useful than the Radscore and the clinical-imaging model in clinical practice by DCA. The clinical-imaging-radiomics nomogram based on initial plain CT images by integrating the Radscore and clinical-imaging factors provided a potential tool to effectively differentiate LPAs from metastases in patients with lung cancer with small hyperattenuating AIs.