CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses - Qian L; Fu B; He H; Liu S; Lu R (Journal of Multidisciplinary Healthcare)
Lu Qian,1,* BinHai Fu,2,* Hong He,2 Shan Liu,1 RenCai Lu2 1Department of Pathology, the First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650032, People’s Republic of China; 2Department of Nuclear Medicine, The First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650032, People’s Republic of China*These authors contributed equally to this workCorrespondence: RenCai Lu, Department of Nuclear Medicine, The First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, No. 157 Jinbi Road, Kunming, Yunnan, 650032, People’s Republic of China, Tel +86 087163614503, Email lrc_09@126.comObjective: This study aimed to explore the value of a radiomic nomogram based on contrast-enhanced computed tomography (CECT) for differentiating benign and malignant solid-containing renal masses.Materials and Methods: A total of 122 patients with pathologically confirmed benign (n=47) or malignant (n=75) solid-containing renal masses were enrolled in this study. Radiomic features were extracted from the arterial, venous and delayed phases and further analysed by dimensionality reduction and selection. Four mainstream machine learning algorithm training models, namely, support vector machine (SVM), k-nearest neighbour (kNN), light gradient boosting (LightGBM) and logistic regression (LR), were constructed to determine the best classifier model. Univariate and multivariate analyses were used to determine the best clinical characteristics for constructing a clinical model. The radiomic and clinical signatures were integrated to construct a combined radiomic nomogram model. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to evaluate the performance of the radiomic nomogram, radiomic signature, and clinical model.Results: Thirteen radiomic features were selected for the development of the radiomic signature. Among the various radiomic models, the LR model demonstrated superior predictive efficiency and robustness, yielding an AUC of 0.952 in the training cohort and 0.887 in the test cohort. The AUC for the clinical model was 0.854 in the training cohort and 0.747 in the test cohort. Furthermore, the radiomic nomogram, which incorporated sex, age, alcohol consumption history, and the radiomic signature, exhibited excellent discriminative performance, yielding an AUC of 0.973 in the training cohort and 0.900 in the test cohort.Conclusion: The radiomic nomogram based on CECT offers a promising and noninvasive approach for distinguishing malignant from benign solid renal masses. This tool can be used to guide treatment strategies effectively and can provide valuable insights for clinicians.Keywords: computed tomography, renal neoplasm, radiomics, machine learning