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

•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...

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Vydané v:European journal of radiology Ročník 162; s. 110770
Hlavní autori: Pan, Junhan, Ye, Shengli, Song, Mengchen, Yang, Tian, Yang, Lili, Zhu, Yanyan, Zhao, Yanci, Chen, Feng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Ireland Elsevier B.V 01.05.2023
Predmet:
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
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Popis
Shrnutí:•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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0720-048X
1872-7727
1872-7727
DOI:10.1016/j.ejrad.2023.110770