CT Radiomics for the Prediction of Synchronous Distant Metastasis in Clear Cell Renal Cell Carcinoma

Abstract

The aim of this study was to construct and verify a computed tomography (CT) radiomics model for preoperative prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC) patients.Methods: Overall, 172 patients with ccRCC were enrolled in the present research. Contrast-enhanced CT images were manually sketched, and 2994 quantitative radiomic features were extracted. The radiomic features were then normalized and subjected to hypothesis testing. Least absolute shrinkage and selection operator (LASSO) was applied to dimension reduction, feature selection, and model construction. The performance of the predictive model was validated through analysis of the receiver operating characteristic curve. Multivariate and subgroup analyses were performed to verify the radiomic score as an independent predictor of SDM. Results:The patients randomized into a training (n = 104) and a validation (n = 68) cohort in a 6:4 ratio. Through dimension reduction using LASSO regression, 9 radiomic features were used for the construction of the SDM prediction model. The model yielded moderate performance in both the training (area under the curve, 0.89; 95% confidence interval, 0.81-0.97) and the validation cohort (area under the curve, 0.83; 95% confidence interval, 0.69-0.95). Multivariate analysis showed that the CT radiomic signature was an independent risk factor for clinical parameters of ccRCC. Subgroup analysis revealed a significant connection between the SDM and radiomic signature, except for the lower pole of the kidney subgroup. Conclusions:The CT-based radiomics model could be used as a noninvasive, personalized approach for SDM prediction in patients with ccRCC.

Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma

“…Compared with MRI, CT has a wider range of uses for the detection, identification, and staging of ccRCC due to its high diagnostic accuracy. A study by Wen et al (18) showed that radiomics features extracted from contrast enhanced CT images demonstrated a good performance for prediction of SDM ccRCC (AUC, 0.83 [95%CI, 0.69-0.95]). However, the weakness of their study is lack of relevant clinical factors.…”

“…A study by Wen et al. ( 18 ) showed that radiomics features extracted from contrast enhanced CT images demonstrated a good performance for prediction of SDM ccRCC (AUC, 0.83 [95%CI, 0.69-0.95]). However, the weakness of their study is lack of relevant clinical factors.…”

“…Radiomics is a promising technique using computerized quantitative imaging analysis to extract an enormous quantity of image-related features, such as intensity, geometry, and texture, from medical images ( 15 , 16 ). Radiomics features extracted from computed tomography (CT), and magnetic resonance imaging (MRI) have been successfully applied in predict SDM in ccRCC patients ( 17 , 18 ). However, these models were developed with limited samples or without external validation, making their clinical usefulness very limited.…”

Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review

“…In our review, only one study analyzed the use of radiomics in predicting distant metastasis. Wen et al examined the role of contrast-enhanced CT images in predicting synchronous distant metastasis in 172 ccRCC patients [ 23 ]. They extracted 2994 quantitative radiomic features and the least absolute shrinkage and selection operator regression was applied for dimension reduction, feature selection, and model construction.…”

Bridging radiomics to tumor immune microenvironment assessment in clear cell renal cell carcinoma

“…8 Another area of research is predicting prognosis 9 and metastasis. 10,11 A few studies worked on inferring response to targeted therapies of RCC and one prior work tried using radiomics to predict response to immunotherapy. 12,13 These studies are limited by small sample sizes and might not work when novel therapies are introduced.…”