Cargando…
Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen
Background: Radiomics extracts quantitative image features to identify biomarkers for characterizing disease. Our aim was to characterize the ability of radiomic features extracted from magnetic resonance (MR) imaging of the liver and spleen to detect cirrhosis by comparing features from patients wi...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9605113/ https://www.ncbi.nlm.nih.gov/pubmed/36286371 http://dx.doi.org/10.3390/jimaging8100277 |
_version_ | 1784817984777224192 |
---|---|
author | Sack, Jordan Nitsch, Jennifer Meine, Hans Kikinis, Ron Halle, Michael Rutherford, Anna |
author_facet | Sack, Jordan Nitsch, Jennifer Meine, Hans Kikinis, Ron Halle, Michael Rutherford, Anna |
author_sort | Sack, Jordan |
collection | PubMed |
description | Background: Radiomics extracts quantitative image features to identify biomarkers for characterizing disease. Our aim was to characterize the ability of radiomic features extracted from magnetic resonance (MR) imaging of the liver and spleen to detect cirrhosis by comparing features from patients with cirrhosis to those without cirrhosis. Methods: This retrospective study compared MR-derived radiomic features between patients with cirrhosis undergoing hepatocellular carcinoma screening and patients without cirrhosis undergoing intraductal papillary mucinous neoplasm surveillance between 2015 and 2018 using the same imaging protocol. Secondary analyses stratified the cirrhosis cohort by liver disease severity using clinical compensation/decompensation and Model for End-Stage Liver Disease (MELD). Results: Of 167 patients, 90 had cirrhosis with 68.9% compensated and median MELD 8. Combined liver and spleen radiomic features generated an AUC 0.94 for detecting cirrhosis, with shape and texture components contributing more than size. Discrimination of cirrhosis remained high after stratification by liver disease severity. Conclusions: MR-based liver and spleen radiomic features had high accuracy in identifying cirrhosis, after stratification by clinical compensation/decompensation and MELD. Shape and texture features performed better than size features. These findings will inform radiomic-based applications for cirrhosis diagnosis and severity. |
format | Online Article Text |
id | pubmed-9605113 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96051132022-10-27 Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen Sack, Jordan Nitsch, Jennifer Meine, Hans Kikinis, Ron Halle, Michael Rutherford, Anna J Imaging Article Background: Radiomics extracts quantitative image features to identify biomarkers for characterizing disease. Our aim was to characterize the ability of radiomic features extracted from magnetic resonance (MR) imaging of the liver and spleen to detect cirrhosis by comparing features from patients with cirrhosis to those without cirrhosis. Methods: This retrospective study compared MR-derived radiomic features between patients with cirrhosis undergoing hepatocellular carcinoma screening and patients without cirrhosis undergoing intraductal papillary mucinous neoplasm surveillance between 2015 and 2018 using the same imaging protocol. Secondary analyses stratified the cirrhosis cohort by liver disease severity using clinical compensation/decompensation and Model for End-Stage Liver Disease (MELD). Results: Of 167 patients, 90 had cirrhosis with 68.9% compensated and median MELD 8. Combined liver and spleen radiomic features generated an AUC 0.94 for detecting cirrhosis, with shape and texture components contributing more than size. Discrimination of cirrhosis remained high after stratification by liver disease severity. Conclusions: MR-based liver and spleen radiomic features had high accuracy in identifying cirrhosis, after stratification by clinical compensation/decompensation and MELD. Shape and texture features performed better than size features. These findings will inform radiomic-based applications for cirrhosis diagnosis and severity. MDPI 2022-10-09 /pmc/articles/PMC9605113/ /pubmed/36286371 http://dx.doi.org/10.3390/jimaging8100277 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sack, Jordan Nitsch, Jennifer Meine, Hans Kikinis, Ron Halle, Michael Rutherford, Anna Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen |
title | Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen |
title_full | Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen |
title_fullStr | Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen |
title_full_unstemmed | Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen |
title_short | Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen |
title_sort | quantitative analysis of liver disease using mri-based radiomic features of the liver and spleen |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9605113/ https://www.ncbi.nlm.nih.gov/pubmed/36286371 http://dx.doi.org/10.3390/jimaging8100277 |
work_keys_str_mv | AT sackjordan quantitativeanalysisofliverdiseaseusingmribasedradiomicfeaturesoftheliverandspleen AT nitschjennifer quantitativeanalysisofliverdiseaseusingmribasedradiomicfeaturesoftheliverandspleen AT meinehans quantitativeanalysisofliverdiseaseusingmribasedradiomicfeaturesoftheliverandspleen AT kikinisron quantitativeanalysisofliverdiseaseusingmribasedradiomicfeaturesoftheliverandspleen AT hallemichael quantitativeanalysisofliverdiseaseusingmribasedradiomicfeaturesoftheliverandspleen AT rutherfordanna quantitativeanalysisofliverdiseaseusingmribasedradiomicfeaturesoftheliverandspleen |