CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions

• Published in: Cancer imaging Vol. 24; no. 1; pp. 130 - 12
• Main Authors: Yu, Shuanbao, Yang, Yang, Wang, Zeyuan, Zheng, Haoke, Cui, Jinshan, Zhan, Yonghao, Liu, Junxiao, Li, Peng, Fan, Yafeng, Jia, Wendong, Wang, Meng, Chen, Bo, Tao, Jin, Li, Yuhong, Zhang, Xuepei
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 02.10.2024; BioMed Central; BMC
• Subjects: Adult; Aged; Aged, 80 and over; Benign renal lesion; Computed tomography; CT imaging; Decision-making; Diagnostic imaging; Female; Humans; Intratumoral heterogeneity; Kidney Neoplasms - diagnostic imaging; Kidney Neoplasms - pathology; Machine Learning; Male; Medical imaging equipment; Middle Aged; Radiomics; Renal cell carcinoma; Retrospective Studies; Small renal lesion; Tomography, X-Ray Computed - methods; Small renal lesion; Benign renal lesion; Computed tomography; Renal cell carcinoma; Intratumoral heterogeneity; Radiomics
• ISSN: 1470-7330; 1740-5025; 1470-7330
• DOI: 10.1186/s40644-024-00775-8

With the increasing incidence of renal lesions, pretreatment differentiation between benign and malignant lesions is crucial for optimized management. This study aimed to develop a machine learning model utilizing radiomic features extracted from various regions of interest (ROIs), intratumoral ecological diversity features, and clinical factors to classify renal lesions. CT images (arterial phase) of 1,795 renal lesions with confirmed pathology from three hospital sites were split into development (1184, 66%) and test (611, 34%) cohorts by surgery date. Conventional radiomic features were extracted from eight ROIs of arterial phase images. Intratumoral ecological diversity features were derived from intratumoral subregions. The combined model incorporating these features with clinical factors was developed, and its performance was compared with radiologists' interpretation. Combining intratumoral and peritumoral radiomic features, along with ecological diversity features yielded the highest AUC of 0.929 among all combinations of features extracted from CT scans. After incorporating clinical factors into the features extracted from CT images, our combined model outperformed the interpretation of radiologists in the whole (AUC = 0.946 vs 0.823, P < 0.001) and small renal lesion (AUC = 0.935 vs 0.745, P < 0.001) test cohorts. Furthermore, the combined model exhibited favorable concordance and provided the highest net benefit across threshold probabilities exceeding 60%. In the whole and small renal lesion test cohorts, the AUCs for subgroups with predicted risk below or above 95% sensitivity and specificity cutoffs were 0.974 and 0.978, respectively. The combined model, incorporating intratumoral and peritumoral radiomic features, ecological diversity features, and clinical factors showed good performance for distinguishing benign from malignant renal lesions, surpassing radiologists' diagnoses in both whole and small renal lesions. It has the potential to save patients from unnecessary invasive biopsies/surgeries and to enhance clinical decision-making.