5 results on '"Safa Hoodeshenas"'
Search Results
2. Magnetic Resonance Elastography in Primary Sclerosing Cholangitis: Interobserver Agreement for Liver Stiffness Measurement with Manual and Automated Methods
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Safa Hoodeshenas, Christopher L. Welle, Richard L. Ehman, Patrick J. Navin, John E. Eaton, Sudhakar K. Venkatesh, and Bogdan Dzyubak
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Adult ,Male ,Adolescent ,Cholangitis, Sclerosing ,Liver mri ,030218 nuclear medicine & medical imaging ,Primary sclerosing cholangitis ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Liver stiffness ,Humans ,Medicine ,Radiology, Nuclear Medicine and imaging ,Reference standards ,Aged ,Reproducibility ,business.industry ,Middle Aged ,medicine.disease ,Magnetic Resonance Imaging ,Elasticity ,Confidence interval ,Magnetic resonance elastography ,Liver ,ROC Curve ,030220 oncology & carcinogenesis ,Correlation analysis ,Elasticity Imaging Techniques ,Female ,business ,Nuclear medicine - Abstract
Rationale and Objective Primary sclerosing cholangitis, a chronic liver disease causes heterogeneous parenchymal changes and fibrosis. Liver stiffness measurement (LSM) with magnetic resonance Elastography (MRE) may be affected by this heterogeneous distribution. We evaluated interobserver agreement of LSM in primary sclerosing cholangitis (PSC) with manual and automated methods to study the influence of heterogeneous changes. Materials and Methods A total of 79 consecutive patients with PSC who had a liver MRI and MRE formed the study group. Three readers with 1–3 years’ experience in MRE and a MRE expert (11 years’ experience) independently performed LSM. Each reader manually drew free hand (fROI) and average (aROI) on stiffness maps. Automatic liver elasticity calculation (ALEC) was used to generate automated LSM. The expert fROI was the reference standard. Correlation analysis and absolute intra-class correlation coefficient (ICC) analysis was performed. Results LSM data of 79 livers and 315 sections were evaluated. There was excellent ICC between expert and reader fROIs (0.989, 95% confidence interval, and 0.985–0.993) and aROIs (0.971, 95% confidence interval, and 0.953–0.983) and ALEC (0.972, 0.957–0.982) with fROI performing better. The areas measured with fROIs and ALEC had moderate ICC with Expert fROI (0.64 and 0.56, respectively) whereas aROI area had a poor ICC of 0.12. Comparison of multiple methods showed significant differences in LSM between expert fROI and aROI of two readers and no significant differences for fROIs of all three readers. Conclusion LSM with MRE in PSC patients shows excellent interobserver agreement with both fROI and aROI methods with better performance with fROI. fROI may therefore be preferred for LSM measurements in PSC.
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- 2019
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3. Liver stiffness measurement by magnetic resonance elastography is not affected by hepatic steatosis
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Jiahui Li, Terry M. Therneau, Richard L. Ehman, Jie Chen, Bin Song, Meng Yin, Jun Chen, Safa Hoodeshenas, Jingbiao Chen, Zheng Zhu, Sudhakar K. Venkatesh, Xin Lu, and Alina M. Allen
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medicine.medical_specialty ,Gastroenterology ,Article ,Fibrosis ,Non-alcoholic Fatty Liver Disease ,Internal medicine ,Nonalcoholic fatty liver disease ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Neuroradiology ,Retrospective Studies ,medicine.diagnostic_test ,Receiver operating characteristic ,business.industry ,Magnetic resonance imaging ,General Medicine ,medicine.disease ,Magnetic Resonance Imaging ,Magnetic resonance elastography ,Liver ,Liver biopsy ,Elasticity Imaging Techniques ,Radiology ,Steatosis ,business - Abstract
To evaluate the relationship between biopsy-assessed hepatic steatosis, magnetic resonance imaging (MRI)–assessed proton density fat fraction (PDFF), and magnetic resonance elastography (MRE)–assessed liver stiffness measurement (LSM), in patients with or at risk for nonalcoholic fatty liver disease (NAFLD). A retrospective study was performed, encompassing 256 patients who had a liver biopsy and MRI/MRE examination performed within 1 year. Clinical and laboratory data were retrieved from the electronic medical record. Hepatic steatosis and fibrosis were assessed by histopathological grading/staging. First, we analyzed the diagnostic performance of PDFF for distinguishing hepatic steatosis with the receiver operating characteristic analyses. Second, variables influencing LSM were screened with univariant analyses, then identified with multivariable linear regression. Finally, the potential relationship between PDFF and LSM was assessed with linear regression after adjustment for other influencing factors, in patients with diagnosed steatosis (PDFF ≥ 5%). The diagnostic accuracy of PDFF in distinguishing steatosis grades (S0-3) was above 0.82. No significant difference in LSM was found between patients with S1, S2, and S3 steatosis and between all steatosis grades after patients were grouped according to fibrosis stage. No statistically significant relationship was found between the LSM and PDFF (estimate = − 0.02, p = 0.065) after adjustment for fibrosis stage and age in patients with diagnosed steatosis (PDFF ≥ 5%). In patients with NAFLD, the severity of hepatic steatosis has no significant influence on the liver stiffness measurement with magnetic resonance elastography. • The MRI-based proton density fat fraction provides a quantitative assessment of hepatic steatosis with high accuracy. • No significant effect of hepatic steatosis on MRE-based liver stiffness measurement was found in patients with S1, S2, and S3 steatosis and between all steatosis grades after patients were grouped according to fibrosis stage. • After adjusting for fibrosis stage and age, there was no statistically significant relationship between liver stiffness and proton density fat fraction in patients with hepatic steatosis (p = 0.065).
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- 2021
4. Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning
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Gian Marco Conte, Kenneth A. Philbrick, Alexander D. Weston, Bradley J. Erickson, Safa Hoodeshenas, Pouria Rouzrokh, Zeynettin Akkus, Jason Cai, Qiao Huang, Atefeh Zeinoddini, David C Vogelsang, and Arunnit Boonrod
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Radiological and Ultrasound Technology ,business.industry ,Computer science ,Deep learning ,Pattern recognition ,Text mining ,medicine.anatomical_structure ,Fully automated ,Artificial Intelligence ,medicine ,Radiology, Nuclear Medicine and imaging ,Segmentation ,Artificial intelligence ,business ,Original Research ,Neuroanatomy - Abstract
PURPOSE: To develop a deep learning model that segments intracranial structures on head CT scans. MATERIALS AND METHODS: In this retrospective study, a primary dataset containing 62 normal noncontrast head CT scans from 62 patients (mean age, 73 years; age range, 27–95 years) acquired between August and December 2018 was used for model development. Eleven intracranial structures were manually annotated on the axial oblique series. The dataset was split into 40 scans for training, 10 for validation, and 12 for testing. After initial training, eight model configurations were evaluated on the validation dataset and the highest performing model was evaluated on the test dataset. Interobserver variability was reported using multirater consensus labels obtained from the test dataset. To ensure that the model learned generalizable features, it was further evaluated on two secondary datasets containing 12 volumes with idiopathic normal pressure hydrocephalus (iNPH) and 30 normal volumes from a publicly available source. Statistical significance was determined using categorical linear regression with P < .05. RESULTS: Overall Dice coefficient on the primary test dataset was 0.84 ± 0.05 (standard deviation). Performance ranged from 0.96 ± 0.01 (brainstem and cerebrum) to 0.74 ± 0.06 (internal capsule). Dice coefficients were comparable to expert annotations and exceeded those of existing segmentation methods. The model remained robust on external CT scans and scans demonstrating ventricular enlargement. The use of within-network normalization and class weighting facilitated learning of underrepresented classes. CONCLUSION: Automated segmentation of CT neuroanatomy is feasible with a high degree of accuracy. The model generalized to external CT scans as well as scans demonstrating iNPH. Supplemental material is available for this article. © RSNA, 2020
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- 2020
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5. Changes in Liver Stiffness, Measured by Magnetic Resonance Elastography, Associated With Hepatic Decompensation in Patients With Primary Sclerosing Cholangitis
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Konstantinos N. Lazaridis, John E. Eaton, Gregory J. Gores, Andrea A. Gossard, Safa Hoodeshenas, Cathy D. Schleck, Nicholas F. LaRusso, Sudhakar K. Venkatesh, Aditi Sen, and William S. Harmsen
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Liver Cirrhosis ,medicine.medical_specialty ,Cirrhosis ,Cholangitis, Sclerosing ,Esophageal and Gastric Varices ,Severity of Illness Index ,Gastroenterology ,Primary sclerosing cholangitis ,End Stage Liver Disease ,03 medical and health sciences ,Liver disease ,0302 clinical medicine ,Model for End-Stage Liver Disease ,Interquartile range ,Internal medicine ,Ascites ,medicine ,Humans ,Hepatic encephalopathy ,Retrospective Studies ,Hepatology ,business.industry ,medicine.disease ,Liver ,030220 oncology & carcinogenesis ,Elasticity Imaging Techniques ,030211 gastroenterology & hepatology ,medicine.symptom ,Gastrointestinal Hemorrhage ,business ,Transient elastography - Abstract
Single measurements of liver stiffness (LS) by magnetic resonance elastography (MRE) have been associated with outcomes of patients with primary sclerosing cholangitis (PSC), but the significance of changes in LS over time are unclear. We investigated associations between changes in LS measurement and progression of PSC.We performed a retrospective review of 204 patients with patients who underwent 2 MREs at a single center between January 1, 2007 and December 31, 2018. We collected laboratory data and information on revised Mayo PSC risk and model for end-stage liver disease scores, the PSC risk estimate tool, and levels of aspartate transferase at the time of each MRE. The ΔLS/time was determined by the change in LS between the second MRE compared to the first MRE divided by the time between examinations. The primary endpoint was development of hepatic decompensation (ascites, variceal hemorrhage or hepatic encephalopathy).The median LS measurement was 2.72 kPa (interquartile range, 2.32-3.44 kPa) and the overall change in LS was 0.05 kPa/y. However, ΔLS/y was 10-fold higher in patients anticipated to have cirrhosis (0.31 kPa/y) compared to patients with no fibrosis (0.03 kPa/y). The median LS increased over time in patients who ultimately developed hepatic decompensation (0.60 kPa/y; interquartile range, 0.21-1.26 kPa/y) vs but remained static in patients who did not (reduction of 0.04/y; interquartile range, reductions of 0.26 to 0.17 kPa/y) (P.001). The ΔLS/y value associated with the highest risk of hepatic decompensation was Δ0.34 kPa/y (hazard ratio [HR], 13.29; 95% CI, 0.23-33.78). After we adjusted for baseline LS and other risk factors, including serum level of alkaline phosphatase and the Mayo PSC risk score, ΔLS/y continued to be associated with hepatic decompensation. The optimal single LS cut-off associated with the hepatic decompensation was 4.32 kPa (HR, 60.41; 95% CI, 17.85-204.47). A combination of both cut-off values was associated with risk of hepatic decompensation (concordance score, 0.93; 95% CI, 0.88-0.98) CONCLUSIONS: A single LS measurement and changes in LS over time are independently associated with hepatic decompensation in patients with PSC. However, changes in LS occur slowly in patients without advanced fibrosis or hepatic decompensation.
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- 2020
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