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Development of a deep-learning model for classification of LI-RADS major features by using subtraction images of MRI: a preliminary study.
Park, Junghoan; Bae, Jae Seok; Kim, Jong-Min; Witanto, Joseph Nathanael; Park, Sang Joon; Lee, Jeong Min.
Afiliação
  • Park J; Department of Radiology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Bae JS; Department of Radiology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Kim JM; Research and Science Division, MEDICAL IP Co., Ltd., Seoul, Republic of Korea.
  • Witanto JN; Research and Science Division, MEDICAL IP Co., Ltd., Seoul, Republic of Korea.
  • Park SJ; Research and Science Division, MEDICAL IP Co., Ltd., Seoul, Republic of Korea.
  • Lee JM; Department of Radiology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. jmsh@snu.ac.kr.
Abdom Radiol (NY) ; 48(8): 2547-2556, 2023 08.
Article em En | MEDLINE | ID: mdl-37222771
ABSTRACT

PURPOSE:

Liver Imaging Reporting and Data System (LI-RADS) is limited by interreader variability. Thus, our study aimed to develop a deep-learning model for classifying LI-RADS major features using subtraction images using magnetic resonance imaging (MRI).

METHODS:

This single-center retrospective study included 222 consecutive patients who underwent resection for hepatocellular carcinoma (HCC) between January, 2015 and December, 2017. Subtraction arterial, portal venous, and transitional phase images of preoperative gadoxetic acid-enhanced MRI were used to train and test the deep-learning models. Initially, a three-dimensional (3D) nnU-Net-based deep-learning model was developed for HCC segmentation. Subsequently, a 3D U-Net-based deep-learning model was developed to assess three LI-RADS major features (nonrim arterial phase hyperenhancement [APHE], nonperipheral washout, and enhancing capsule [EC]), utilizing the results determined by board-certified radiologists as reference standards. The HCC segmentation performance was assessed using the Dice similarity coefficient (DSC), sensitivity, and precision. The sensitivity, specificity, and accuracy of the deep-learning model for classifying LI-RADS major features were calculated.

RESULTS:

The average DSC, sensitivity, and precision of our model for HCC segmentation were 0.884, 0.891, and 0.887, respectively, across all the phases. Our model demonstrated a sensitivity, specificity, and accuracy of 96.6% (28/29), 66.7% (4/6), and 91.4% (32/35), respectively, for nonrim APHE; 95.0% (19/20), 50.0% (4/8), and 82.1% (23/28), respectively, for nonperipheral washout; and 86.7% (26/30), 54.2% (13/24), and 72.2% (39/54) for EC, respectively.

CONCLUSION:

We developed an end-to-end deep-learning model that classifies the LI-RADS major features using subtraction MRI images. Our model exhibited satisfactory performance in classifying LI-RADS major features.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Carcinoma Hepatocelular / Aprendizado Profundo / Neoplasias Hepáticas Tipo de estudo: Diagnostic_studies / Observational_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Carcinoma Hepatocelular / Aprendizado Profundo / Neoplasias Hepáticas Tipo de estudo: Diagnostic_studies / Observational_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article