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Research Article
Hongsheng Tang
Department of Abdominal Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Accurate characterization of small nodules in a cirrhotic liver is challenging. We aimed to determine the additive value of MRI-based radiomics analysis to Liver Imaging Reporting and Data System version 2018 (LI-RADS v 2018) algorithm in differentiating small (≤ 3 cm) hepatocellular carcinomas (HCCs) from benign nodules in cirrhotic liver.
Methods
Cirrhosis patients (150) with histopathologically confirmed small liver nodules (HCC, 112; benign nodules, 44) were included. Based on the LI-RADS algorithm, a LI-RADS category was assigned for each lesion. A radiomics signature was generated based on texture features extracted from T1-weighted, T2W, and apparent diffusion coefficient (ADC) images by using the least absolute shrinkage and selection operator regression model. A nomogram model was developed for the combined diagnosis. Diagnostic performance was assessed using receiver operating characteristic curve (ROC) analysis.
Results
A radiomics signature consisting of eight features were significantly associated with the differentiation of HCCs from benign nodules. Both LI-RADS algorithm (area under ROC [Az] = 0.898) and the MRI-Based radiomics signature (Az = 0.917) demonstrated good discrimination; and the nomogram model showed a superior classification performance (Az = 0.975). Compared with LI-RADS alone, the combined approach significantly improved the specificity (97.7% vs 81.8%, p = 0.030) and positive predictive value (99.1% vs 92.9%, p = 0.031) and afforded comparable sensitivity (97.3% vs 93.8%, p = 0.215) and negative predictive value (93.5% vs 83.7%, p = 0.188).
Conclusions
MRI-based radiomics analysis showed additive value to the LI-RADS v 2018 algorithm for differentiating small HCCs from benign nodules in cirrhotic liver.
Keywords
Hepatocellular carcinoma, Diagnosis, Magnetic resonance imaging, Radiomics
Figure 1
Figure 2
Figure 3
Figure 4
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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View the published article at BMC Gastroenterology.
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Subject Areas
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A new preprint design is coming!
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See the published version of this preprint at BMC Gastroenterology.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Hongsheng Tang
Department of Abdominal Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Gastroenterology.
Version 1
Posted 08 Feb, 2021
No community comments so far
Editorial decision: Major revision
On 09 Feb, 2021
Reviews received
Received 08 Feb, 2021
Reviewers agreed
On 05 Feb, 2021
Reviewers invited
Invitations sent on 05 Feb, 2021
Editor assigned
On 05 Feb, 2021
Editor invited
On 05 Feb, 2021
Submission checks complete
On 05 Feb, 2021
First submitted
On 24 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Gastroenterology & Hepatology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Gastroenterology.
Background
Accurate characterization of small nodules in a cirrhotic liver is challenging. We aimed to determine the additive value of MRI-based radiomics analysis to Liver Imaging Reporting and Data System version 2018 (LI-RADS v 2018) algorithm in differentiating small (≤ 3 cm) hepatocellular carcinomas (HCCs) from benign nodules in cirrhotic liver.
Methods
Cirrhosis patients (150) with histopathologically confirmed small liver nodules (HCC, 112; benign nodules, 44) were included. Based on the LI-RADS algorithm, a LI-RADS category was assigned for each lesion. A radiomics signature was generated based on texture features extracted from T1-weighted, T2W, and apparent diffusion coefficient (ADC) images by using the least absolute shrinkage and selection operator regression model. A nomogram model was developed for the combined diagnosis. Diagnostic performance was assessed using receiver operating characteristic curve (ROC) analysis.
Results
A radiomics signature consisting of eight features were significantly associated with the differentiation of HCCs from benign nodules. Both LI-RADS algorithm (area under ROC [Az] = 0.898) and the MRI-Based radiomics signature (Az = 0.917) demonstrated good discrimination; and the nomogram model showed a superior classification performance (Az = 0.975). Compared with LI-RADS alone, the combined approach significantly improved the specificity (97.7% vs 81.8%, p = 0.030) and positive predictive value (99.1% vs 92.9%, p = 0.031) and afforded comparable sensitivity (97.3% vs 93.8%, p = 0.215) and negative predictive value (93.5% vs 83.7%, p = 0.188).
Conclusions
MRI-based radiomics analysis showed additive value to the LI-RADS v 2018 algorithm for differentiating small HCCs from benign nodules in cirrhotic liver.
Figure 1
Figure 2
Figure 3
Figure 4
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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