Cystic renal mass screening: machine-learning-based radiomics on unenhanced computed tomography

Autor:	Huang L; Guangdong Provincial Hospital of Chinese Medicine, Department of Radiology, Zhuhai, China, Ye Y; Guangdong Provincial Hospital of Chinese Medicine, Department of Radiology, Guangzhou, China, Chen J; Guangdong Provincial Hospital of Chinese Medicine, Department of Radiology, Zhuhai, China; Guangdong Provincial Hospital of Chinese Medicine, Department of Radiology, Guangzhou, China, Feng W; Zhuhai People’s Hospital, Department of Radiology, Zhuhai, China, Peng S; Guangdong Provincial Hospital of Chinese Medicine, Department of Laboratory Medicine, Zhuhai, China, Du X; Guangdong Provincial Hospital of Chinese Medicine, Department of Pathology, Guangzhou, China, Li X; Guangdong Provincial Hospital of Chinese Medicine, Department of Gynaecology, Zhuhai, China, Song Z; Philips Healthcare, Clinical and Technical Support, Guangzhou, China, Liu T; Guangdong Provincial Hospital of Chinese Medicine, Department of Radiology, Zhuhai, China
Jazyk:	angličtina
Zdroj:	Diagnostic and interventional radiology (Ankara, Turkey) [Diagn Interv Radiol] 2024 Jul 08; Vol. 30 (4), pp. 236-247. Date of Electronic Publication: 2024 Jan 02.
DOI:	10.4274/dir.2023.232386
Abstrakt:	Purpose: The present study compares the diagnostic performance of unenhanced computed tomography (CT) radiomics-based machine learning (ML) classifiers and a radiologist in cystic renal masses (CRMs). Methods: Patients with pathologically diagnosed CRMs from two hospitals were enrolled in the study. Unenhanced CT radiomic features were extracted for ML modeling in the training set (Guangzhou; 162 CRMs, 85 malignant). Total tumor segmentation was performed by two radiologists. Features with intraclass correlation coefficients of >0.75 were screened using univariate analysis, least absolute shrinkage and selection operator, and bidirectional elimination to construct random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) models. External validation was performed in the Zhuhai set (45 CRMs, 30 malignant). All images were assessed by a radiologist. The ML models were evaluated using calibration curves, decision curves, and receiver operating characteristic (ROC) curves. Results: Of the 207 patients (102 women; 59.1 ± 11.5 years), 92 (41 women; 58.0 ± 13.7 years) had benign CRMs, and 115 (61 women; 59.8 ± 11.4 years) had malignant CRMs. The accuracy, sensitivity, and specificity of the radiologist's diagnoses were 85.5%, 84.2%, and 91.1%, respectively [area under the (ROC) curve (AUC), 0.87]. The ML classifiers showed similar sensitivity (94.2%-100%), specificity (94.7%-100%), and accuracy (94.3%-100%) in the training set. In the validation set, KNN showed better sensitivity, accuracy, and AUC than DT and RF but weaker specificity. Calibration and decision curves showed excellent and good results in the training and validation set, respectively. Conclusion: Unenhanced CT radiomics-based ML classifiers, especially KNN, may aid in screening CRMs.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu