Preoperative prediction of WHO/ISUP grade of ccRCC using intratumoral and peritumoral habitat imaging: multicenter study

Abstract

Objectives: The World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading of clear cell renal cell carcinoma (ccRCC) is crucial for prognosis and treatment planning. This study aims to predict the grade using intratumoral and peritumoral subregional CT radiomics analysis for better clinical interventions.

Methods: Data from two hospitals included 513 ccRCC patients, who were divided into training (70%), validation (30%), and an external validation set (testing) of 67 patients. Using ITK-SNAP, two radiologists annotated tumor regions of interest (ROI) and extended surrounding areas by 1 mm, 3 mm, and 5 mm. The K-means clustering algorithm divided the tumor region into three sub-regions, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression identified the most predictive features. Various machine learning models were established, including radiomics models, peritumoral radiomics models, models based on intratumoral heterogeneity (ITH) score, clinical models, and comprehensive models. Predictive ability was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC) values, DeLong tests, calibration curves, and decision curves.

Results: The combined model showed strong predictive power with an AUC of 0.852 (95% CI: 0.725-0.979) on the test data, outperforming individual models. The ITH score model was highly precise, with AUCs of 0.891 (95% CI: 0.854-0.927) in training, 0.877 (95% CI: 0.814-0.941) in validation, and 0.847 (95% CI: 0.725-0.969) in testing, proving its superior predictive ability across datasets.

Conclusion: A comprehensive model combining Habitat, Peri1mm, and salient clinical features was significantly more accurate in predicting ccRCC pathologic grading.

Key points: Question: Characterize tumor heterogeneity to non-invasively predict WHO/ISUP pathological grading preoperatively.

Findings: An integrated model combining subregion characterization, peritumoral characteristics, and clinical features can predict ccRCC grade preoperatively.

Clinical relevance: Subregion tumor characterization outperforms the single-entity approach. The integrated model, compared with the radiomics model, boosts grading and prognostic accuracy for more targeted clinical actions.

Keywords: Clear cell renal cell carcinoma; Computed tomography; Habitat analysis; Radiomics; WHO/ISUP grading.

Fig. 1

Overall workflow of this study. ROI: regions of interest; LASSO: Least Absolute Shrinkage and Selection Operator; MSE: Mean Standard Error; Peri: Peritumoral; DCA: Decision Curves Analysis

Fig. 2

Presents the Peritumoral and Intratumor Heterogeneity regions generated. Red indicates the intra-tumoral region, and green represents the peritumoral region. The peritumoral region was expanded at intervals of 1 mm, 3 mm, and 5 mm

Fig. 3

a Presents the Calinski-Harabasz (CH) scores for different numbers of clusters, illustrating the impact of the number of clusters on the segmentation effect for each pattern. b provides a visualization of the DCE features, segmented into three distinct clusters. DCE: Dynamic Contrast-Enhanced

Fig. 4

a Coefficients of ten-fold cross validation; b MSE of ten-fold cross validation; c The histogram of the Rad-score based on the selected features. MSE: Mean Standard Error

Fig. 5

Grading prediction results of ccRCC. ROC Curves of Different Models in each cohort (a-c). Different signatures'AUROC on all cohort(d-f). LR: Logistic Regression; AUC: area under the curve; Peri: Peritumoral: ROC: receiver operating characteristic

Fig. 6

Shows the nomogram for clinical use