MRI-based radiomic feature analysis of end-stage liver disease for severity stratification

Nitsch, Jennifer; Sack, Jordan; Halle, Morris; Moltz, Jan Hendrik; Wall, April; Rutherford, Anna E.; Kikinis, Ron; Meine, Hans

doi:10.1007/s11548-020-02295-9

Cited by 11 publications

(6 citation statements)

References 25 publications

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“…In a recent study by Nitsch et al, magnetic resonance imaging (MRI)-based radiomic features and random forest classifier were used to predict the severity of liver cirrhosis, with clinical decompensation and model for end-stage liver disease (MELD) scores as reference standard. Nitsch et al concluded that adding splenic MRI-based radiomic features can increase the AUC for predicting a higher median MELD score or clinical decompensation [ 24 ]. However, their study lacked an objective reference standard and was only focused on patients with known cirrhosis.…”

Section: Discussionmentioning

confidence: 99%

Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging

et al. 2022

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Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning.

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Section: Discussionmentioning

confidence: 99%

Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging

et al. 2022

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“…Feature analysis was performed on the contrast-enhanced imaging acquisition [ 17 ]. Due to the homogenous imaging conditions described above, we did not apply any kind of pre-processing such as re-sampling or other normalization.…”

Section: Methodsmentioning

confidence: 99%

“…Due to the homogenous imaging conditions described above, we did not apply any kind of pre-processing such as re-sampling or other normalization. Liver and spleen images were segmented using a U-net-based network architecture successively trained on expert segmentations [ 17 , 19 ]. The PyRadiomics library (version 2.0.1) [ 20 ] was used to extract 1288 features each from the liver and spleen, resulting in a total of 2577 imaging features (including a size ratio).…”

Section: Methodsmentioning

confidence: 99%

“…These two organs were chosen as they manifest findings of cirrhosis and portal hypertension that can be easily segmented radiographically. We recently used this approach to test the ability of MR radiomic features against different measures of cirrhosis severity [ 17 ]. We now first aim to demonstrate the ability of these MR radiomic features of the liver and spleen to detect the presence of cirrhosis when comparing MR imaging in patients with cirrhosis to those without cirrhosis.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen

et al. 2022

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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.

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“…Owing to the association of tumor genetic heterogeneity in HN cancer with patients’ prognoses, it was concluded that higher heterogeneity is related to worse outcomes [ 6 , 7 , 8 ]. This genetic heterogeneity can lead to imaging heterogeneity, which can be quantified by radiomics [ 9 ] on 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) images [ 10 , 11 ]. The feasibility of conventional PET/CT features for risk assessment in patients with HN cancer has been extensively investigated [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Can Persistent Homology Features Capture More Intrinsic Information about Tumors from 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Images of Head and Neck Cancer Patients?

Arimura

Ninomiya

et al. 2022

Metabolites

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This study hypothesized that persistent homology (PH) features could capture more intrinsic information about the metabolism and morphology of tumors from 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) images of patients with head and neck (HN) cancer than other conventional features. PET/CT images and clinical variables of 207 patients were selected from the publicly available dataset of the Cancer Imaging Archive. PH images were generated from persistent diagrams obtained from PET/CT images. The PH features were derived from the PH PET/CT images. The signatures were constructed in a training cohort from features from CT, PET, PH-CT, and PH-PET images; clinical variables; and the combination of features and clinical variables. Signatures were evaluated using statistically significant differences (p-value, log-rank test) between survival curves for low- and high-risk groups and the C-index. In an independent test cohort, the signature consisting of PH-PET features and clinical variables exhibited the lowest log-rank p-value of 3.30 × 10−5 and C-index of 0.80, compared with log-rank p-values from 3.52 × 10−2 to 1.15 × 10−4 and C-indices from 0.34 to 0.79 for other signatures. This result suggests that PH features can capture the intrinsic information of tumors and predict prognosis in patients with HN cancer.

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MRI-based radiomic feature analysis of end-stage liver disease for severity stratification

Cited by 11 publications

References 25 publications

Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging

Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging

Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen

Can Persistent Homology Features Capture More Intrinsic Information about Tumors from 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Images of Head and Neck Cancer Patients?

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