Non-Invasive Visual Prediction of Pathological Grading in Clear Cell Renal Carcinoma Using Habitat Imaging Based on Enhanced CT
10.3969/j.issn.1005-5185.2025.09.003
- VernacularTitle:增强CT生境成像对肾透明细胞癌病理分级的无创可视化预测
- Author:
Danqing YIN
1
;
Lei YUAN
;
Jingliang ZHANG
;
Lina MA
;
Weijun QIN
;
Jing ZHANG
;
Yi HUAN
;
Jing REN
Author Information
1. 空军军医大学西京医院放射诊断科,陕西 西安 710032
- Publication Type:Journal Article
- Keywords:
Carcinoma,renal cell;
Tomography,X-ray computed;
Radiomics;
Habitat imaging;
Pathology,surgical;
Forecasting
- From:
Chinese Journal of Medical Imaging
2025;33(9):906-911,919
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To explore the value of contrast-enhanced CT habitat imaging(HI)in preoperative non-invasive visualization for predicting pathological grading of clear cell renal carcinoma(ccRCC).Materials and Methods A retrospective analysis was conducted on enhanced CT images and clinical data from 240 patients with pathologically confirmed ccRCC at Xijing Hospital,the Fourth Military Medical University from January 2020 to December 2023.All patients were randomly divided into training and test sets at a 7:3 ratio and classified into low-grade group(International Society of Urological Pathology Ⅰ-Ⅱ)and high-grade group(International Society of Urological Pathology Ⅲ-Ⅳ)based on postoperative pathology.Using wash-in and wash-out parametric maps,the tumors were segmented into three perfusion-based habitat subregions(low,medium and high)via K-means clustering,and the volume fraction of each subregion was calculated.Predictive factors were selected from habitat features and clinical variables(including sex,age,tumor size,etc.)using Logistic regression.Three models were constructed:a clinical model,a habitat imaging model and a combined clinical-habitat model.Model performance was evaluated using receiver operating characteristic curve,calibration curve and decision curve analysis.Results Habitat 3 exhibited higher wash-in and wash-out gradients compared to Habitats 1 and 2,indicating hyper perfusion.Its proportion was significantly higher in the low-grade group than in the high-grade group(Z=-7.71,-5.11,both P<0.01).Multivariate Logistic regression identified hypertension,maximum tumor diameter and platelet-to-lymphocyte ratio as independent risk factors for high-grade ccRCC,while the proportion of Habitat 3 was a protective factor(OR=0.297,95％CI 0.184-0.479).The combined clinical-habitat model demonstrated the highest predictive performance[area under the curve(AUC)=0.938],significantly outperforming the clinical model(AUC=0.801,Z=-3.832,P<0.01)and the habitat imaging model(AUC=0.895,Z=-2.157,P=0.031).Conclusion The clinical-habitat imaging model achieves the highest predictive performance for ccRCC pathological grading.Contrast-enhanced CT habitat imaging provides significant incremental value in predicting ccRCC pathological grading,showing potential to guide precision medicine in clinical practice.
