A new classification and regression tree algorithm: Improved diagnostic sensitivity for HCC ≤ 3.0 cm using gadoxetate disodium-enhanced MRI

• Published in: European journal of radiology Vol. 162; p. 110770
• Main Authors: Pan, Junhan, Ye, Shengli, Song, Mengchen, Yang, Tian, Yang, Lili, Zhu, Yanyan, Zhao, Yanci, Chen, Feng
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.05.2023
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Carcinoma, Hepatocellular - diagnostic imaging; CART; Diagnosis; Early Diagnosis; Female; Gadolinium DTPA; Gadoxetate disodium; Hepatocellular carcinoma; Humans; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - pathology; Magnetic resonance imaging; Magnetic Resonance Imaging - standards; Male; Middle Aged; Reproducibility of Results; Retrospective Studies; Sensitivity and Specificity; Young Adult; OR; LI-RADS; LR; Hepatocellular carcinoma; HCC; PVP; Gadoxetate disodium; APHE; Magnetic resonance imaging; NPV; HBP; PPV; Diagnosis; Gd-EOB-MRI; TP; CART
• ISSN: 0720-048X; 1872-7727; 1872-7727
• DOI: 10.1016/j.ejrad.2023.110770

•Our algorithm developed with targetoid appearance, hepatobiliary phase hypointensity, nonrim arterial phase hyperenhancement, and transitional phase hypointensity plus mild-moderate T2 hyperintensity.•Our algorithm demonstrated superior performances than existing classification and regression tree algorithms and LI-RADS LR-5 for diagnosing HCC ≤ 3.0 cm with gadoxetate disodium‑enhanced MRI.•Our algorithm showed significantly higher sensitivity than LI-RADS LR-5, while maintaining high positive predictive value. To develop and validate an effective algorithm, based on classification and regression tree (CART) analysis and LI-RADS features, for diagnosing HCC ≤ 3.0 cm with gadoxetate disodium‑enhanced MRI (Gd-EOB-MRI). We retrospectively included 299 and 90 high-risk patients with hepatic lesions ≤ 3.0 cm that underwent Gd-EOB-MRI from January 2018 to February 2021 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Through binary and multivariate regression analyses of LI-RADS features in the development cohort, we developed an algorithm using CART analysis, which comprised the targeted appearance and independently significant imaging features. On per-lesion basis, we compared the diagnostic performances of our algorithm, two previously reported CART algorithms, and LI-RADS LR-5 in development and validation cohorts. Our CART algorithm, presenting as a decision tree, included targetoid appearance, HBP hypointensity, nonrim arterial phase hyperenhancement (APHE), and transitional phase hypointensity plus mild-moderate T2 hyperintensity. For definite HCC diagnosis, the overall sensitivity of our algorithm (development cohort 93.2%, validation cohort 92.5%; P < 0.006) was significantly higher than those of Jiang’s algorithm modified LR-5 (defined as targetoid appearance, nonperipheral washout, restricted diffusion, and nonrim APHE) and LI-RADS LR-5, with the comparable specificity (development cohort: 84.3%, validation cohort: 86.7%; P ≥ 0.006). Our algorithm, providing the highest balanced accuracy (development cohort: 91.2%, validation cohort: 91.6%), outperformed other criteria for identifying HCCs from non-HCC lesions. In high-risk patients, our CART algorithm developed with LI-RADS features showed promise for the early diagnosis of HCC ≤ 3.0 cm with Gd-EOB-MRI.