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

Autor: Shuanbao Yu, Yang Yang, Zeyuan Wang, Haoke Zheng, Jinshan Cui, Yonghao Zhan, Junxiao Liu, Peng Li, Yafeng Fan, Wendong Jia, Meng Wang, Bo Chen, Jin Tao, Yuhong Li, Xuepei Zhang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Cancer Imaging, Vol 24, Iss 1, Pp 1-12 (2024)
Druh dokumentu: article
ISSN: 1470-7330
74407783
DOI: 10.1186/s40644-024-00775-8
Popis: Abstract Background 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. Methods 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. Results 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
Databáze: Directory of Open Access Journals
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