Intratumoral and peritumoral CT radiomics combined with clinical and imaging features for predicting renal capsule invasion of clear cell renal cell carcinoma
10.13929/j.issn.1003-3289.2025.03.021
- VernacularTitle:瘤内及瘤周CT影像组学联合临床及影像学特征预测透明细胞肾细胞癌侵犯肾被膜
- Author:
Chenyang ZHANG
1
;
Junhong HE
;
Pengfei WANG
;
Cong ZHANG
;
Jinwu REN
Author Information
1. 承德医学院研究生学院,河北 承德 067000
- Publication Type:Journal Article
- Keywords:
carcinoma,renal cell;
tomography,X-ray computed;
radiomics
- From:
Chinese Journal of Medical Imaging Technology
2025;41(3):447-451
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of intratumoral and peritumoral ROI-based CT radiomics combined with clinical and imaging features for preoperatively predicting renal capsule invasion of clear cell renal cell carcinoma(ccRCC).Methods Totally 105 ccRCC patients were retrospectively collected and divided into invasion group(n=70)and non-invasion group(n=35)according to post operation pathology,also divided into training set(n=84,including 56 cases of invasion group and 28 of non-invasion group)and test set(n=21,including 14 cases of invasion group and 7 of non-invasion group)at a ratio of 8∶2.A clinical-imaging model was constructed based on clinical and CT features being significantly different between groups.Radiomics features related to renal capsule invasion were extracted and selected from intratumoral and of 1-6 mm peritumoral ROI on unenhanced phase(UP),corticomedullary phase(CMP)and nephrographic phase(NP)CT images,respectively.The optimal algorithm was selected among 6 machine learning algorisms based on CMP intratumoral ROI.With the optimal and selected features,single intratumoral or peritumoral models,combined intratumoral and peritumoral models within the same phase and combined pairwise models within the same range across different phases images were established.The best performing radiomics model was chosen and integrated with clinical and imaging features to form a combined model.Receiver operating characteristic(ROC)curves were drawn,the area under the curve(AUC)was calculated to evaluate the efficacy of model for predicting renal capsule invasion of ccRCC,which were compared using DeLong's test.Results Hypertension,presence of clinical symptoms and high enhancement degree on CMP images were all independent predicting factors for renal capsule invasion of ccRCC,which were used to establish clinical-imaging model.Support vector machine(SVM)was the optimal algorithm.CMP peritumoral 3 mm model,CMP intratumoral model,NP peritumoral 4 mm model,NP intratumoral+peritumoral 4 mm model and CMP peritumoral 3 mm+NP peritumoral 3 mm model showed higher performance than the others,with AUC being not significantly different(all P＞0.05).CMP peritumoral 3 mm model was the optimal radiomics model,with the highest AUC(0.898)in test set.The combined model demonstrated superior AUC(0.979)in training set compared to both clinical-imaging model and the optimal radiomics model alone(both P＜0.05),while in test set(AUC 0.918)showed comparable performance with the latter two(both P＞0.05).Conclusion CT-based peritumoral radiomics models were equally effective for preoperatively predicting renal capsule invasion of ccRCC.Combining with clinical and imaging features might further enhance diagnostic performance.
