Diagnosis of benign and malignant renal cystic lesions based on computed tomography radiomics
Objective To investigate the value of a radiomics nomogram based on computed tomo-graphy(CT)in the diagnosis of benign and malignant cystic renal lesions(CRL).Methods A total of 169 patients with CRL(Bosniak ≥ Ⅱ)in our hospital from August 2014 to May 2023 were enrolled in this study.Clinical data and CT images of each patient were obtained.The patients were randomly divided into the training cohort(n=135)and the validation cohort(n=34)at a ratio of 8:2 using the RAND function of Excel.Chi-squared test,t test and other tests were used to screen relevant clinical risk factors.Pyra-diomics software was used to extract a large number of radiomics features from region of interest(ROI).The least absolute shrinkage and selection operator(LASSO)regression model was used to screen the best radiomics features.Light gradient boosting machine(LightGBM)algorithm was used to construct the clini-cal model,radiomics model and combined model,respectively,and a nomogram combining radiomics score(Rad-score)with independent clinical factors was developed.Receiver operating characteristic(ROC)curve of each model was drawn,and the corresponding area under the ROC curve(AUC)value was calcu-lated to measure discrimination performance of each model in training and validation cohorts.Results The maximum diameter(ttraining cohon=-2.797,tvalidation cohort=-1.490)was larger and the Bosniak classification(x2raining cohort=46.526,x2alidation cohort=13.852)was higher in malignant CRL patients,both showing statisti-cally significant differences(P＜0.05).A total of 18 radiomics features were finally extracted to calculate Rad-score.The clinical model performed an AUC of 0.944[95％confidence interval(CI):0.898-0.990]in the training cohort and an AUC of 0.855(95％CL:0.643-1.000)in the validation cohort.The ra-diomics model performed an AUC of 0.987(95％CI:0.972-1.000)in the training cohort and an AUC of 0.910(95％CI:0.746-1.000)in the validation cohort.The radiomics nomogram combining the above two models performed an AUC of 0.985(95％CI:0.972-1.000)in the training cohort and an AUC of 0.931 in the validation cohort(95％CI:0.746-1.000).Conclusion The CT-based radiomics nomogram has a good predictive value in distinguishing between BCRL and MCRL,which helps clinicians make accu-rate differential diagnosis and formulate individualized treatment strategies.
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