Mustafa Bashir

Overview:

Hepatobiliary and pancreatic imaging
Liver cancer (hepatocellular carcinoma)
Fatty liver, NAFLD, and NASH
Chronic liver disease and cirrhosis
Pancreatic cancer
Technical development in MRI
Quantitative imaging

Positions:

Associate Professor of Radiology

Radiology, Abdominal Imaging
School of Medicine

Associate Professor in the Department of Medicine

Medicine, Gastroenterology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

M.D. 2004

University of Iowa

Grants:

Imaging Core Lab for Madrigal Protocol MGL-3196-05 (A Phase 2, Multi-Center, Double-Blind, Randomized, Placebo-Controlled Study of MGL-3196 in Patients With Non-Alcoholic Steatohepatitis)

Administered By
Radiology, Abdominal Imaging
Awarded By
Madrigal Pharmaceuticals
Role
Principal Investigator
Start Date
End Date

A PHASE 2B RANDOMIZED, DOUBLE-BLIND, PLACEBO-CONTROLLED STUDY EVALUATING THE SAFETY AND EFFICACY OF BMS-986036 (PEG-FGF21) IN ADULTS WITH NONALCOHOLIC STEATOHEPATITIS (NASH) AND STAGE 3 LIVER FIBROSIS.

Administered By
Radiology, Abdominal Imaging
Awarded By
Bristol-Myers Squibb Company
Role
Principal Investigator
Start Date
End Date

3V2640-CLIN-005 A Phase 2, Multi-Center, Single-Blind, Randomized Placebo Controlled Study of TVB-2640 in Subjects with Non-Alcoholic Steatohepatitis

Administered By
Radiology, Abdominal Imaging
Awarded By
Diabetes & Endocrinology Consultants, PC
Role
Principal Investigator
Start Date
End Date

A randomized, open label, phase 1b study to evaluate safety, PK and PD signals of DUR-928 in patients with Non-Alcoholic Steatohepatitis (NASH)

Administered By
Radiology, Abdominal Imaging
Awarded By
High Point Clinical Trial Center
Role
Principal Investigator
Start Date
End Date

A Phase 2, Randomized, Double Blind, Placebo Controlled, Parallel Group, Multiple Center Study to Evaluate the Safety, Tolerability, and Efficacy of NGM282 Administered for 12 Weeks in Patients with Histologically Confirmed Nonalcoholic Steatohepatit

Administered By
Medicine, Gastroenterology
Awarded By
NGM Biopharmaceuticals
Role
Co-Principal Investigator
Start Date
End Date

Publications:

Therapies for hepatocellular carcinoma: overview, clinical indications, and comparative outcome evaluation-part one: curative intention.

Hepatocellular carcinoma (HCC) offers unique management challenges as it commonly occurs in the setting of underlying chronic liver disease. The management of HCC is directed primarily by the clinical stage. The most commonly used staging system is the Barcelona-Clinic Liver Cancer system, which considers tumor burden based on imaging, liver function and the patient's performance status. Early-stage HCC can be managed with therapies of curative intent including surgical resection, liver transplantation, and ablative therapies. This manuscript reviews the various treatment options for HCC with a curative intent, such as locablative therapy types, surgical resection, and transplant. Indications, contraindications and outcomes of the various treatment options are reviewed. Multiple concepts relating to liver transplant are discussed including Milan criteria, OPTN policy, MELD exception points, downstaging to transplant and bridging to transplant.
Authors
Yacoub, JH; Hsu, CC; Fishbein, TM; Mauro, D; Moon, A; He, AR; Bashir, MR; Burke, LMB
MLA Citation
Yacoub, Joseph H., et al. “Therapies for hepatocellular carcinoma: overview, clinical indications, and comparative outcome evaluation-part one: curative intention.Abdom Radiol (Ny), Apr. 2021. Pubmed, doi:10.1007/s00261-021-03069-w.
URI
https://scholars.duke.edu/individual/pub1478665
PMID
33835223
Source
pubmed
Published In
Abdom Radiol (Ny)
Published Date
DOI
10.1007/s00261-021-03069-w

The Varied Modalities of Liver Elastography and How Each Fits Into a Hepatology Practice.

Authors
Jiang, H; Fowler, KJ; Bashir, MR
MLA Citation
Jiang, Hanyu, et al. “The Varied Modalities of Liver Elastography and How Each Fits Into a Hepatology Practice.Clin Liver Dis (Hoboken), vol. 17, no. 4, Apr. 2021, pp. 326–29. Pubmed, doi:10.1002/cld.1099.
URI
https://scholars.duke.edu/individual/pub1482305
PMID
33968398
Source
pubmed
Published In
Clinical Liver Disease
Volume
17
Published Date
Start Page
326
End Page
329
DOI
10.1002/cld.1099

Artificial intelligence in assessment of hepatocellular carcinoma treatment response.

Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates, presents recent trends in the utility of AI applications for the volumetric evaluation and assessment of HCC treatment response. Various topics including radiomics, prognostic imaging findings, and locoregional therapy (LRT) specific issues will be discussed in the framework of HCC treatment response classification systems with focus on the Liver Reporting and Data System treatment response algorithm (LI-RADS TRA).
Authors
Spieler, B; Sabottke, C; Moawad, AW; Gabr, AM; Bashir, MR; Do, RKG; Yaghmai, V; Rozenberg, R; Gerena, M; Yacoub, J; Elsayes, KM
MLA Citation
Spieler, Bradley, et al. “Artificial intelligence in assessment of hepatocellular carcinoma treatment response.Abdom Radiol (Ny), Mar. 2021. Pubmed, doi:10.1007/s00261-021-03056-1.
URI
https://scholars.duke.edu/individual/pub1478682
PMID
33786653
Source
pubmed
Published In
Abdom Radiol (Ny)
Published Date
DOI
10.1007/s00261-021-03056-1

Therapies for hepatocellular carcinoma: overview, clinical indications, and comparative outcome evaluation. Part two: noncurative intention.

Locoregional therapies can be offered to hepatocellular carcinoma patients as a bridge to transplant, to downstage disease burden for transplant eligibility, or for disease control to prolong survival. Systemic therapies also play a large role in HCC treatment, occasionally in conjunction with other methods. This manuscript reviews the various treatment options for HCC with a historically noncurative intent.
Authors
Yacoub, JH; Mauro, D; Moon, A; He, AR; Bashir, MR; Hsu, CC; Fishbein, TM; Burke, LMB
MLA Citation
Yacoub, Joseph H., et al. “Therapies for hepatocellular carcinoma: overview, clinical indications, and comparative outcome evaluation. Part two: noncurative intention.Abdom Radiol (Ny), Apr. 2021. Pubmed, doi:10.1007/s00261-021-03074-z.
URI
https://scholars.duke.edu/individual/pub1480947
PMID
33864107
Source
pubmed
Published In
Abdom Radiol (Ny)
Published Date
DOI
10.1007/s00261-021-03074-z

Deep neural networks trained for segmentation are sensitive to brightness changes: Preliminary results

Medical images of a patient may have a significantly different appearance depending on imaging modality (e.g. MRI vs. CT), sequence type (e.g., T1-weighted MRI vs. T2-weighted MRI), and even manufacturer/model of equipment used for the same modality and sequence type (e.g. SIEMENS vs GE). Since in the context of deep learning training and test data often come from different institutions, it is important to determine how well neural networks generalize when image appearance varies. There is currently no systematic answer to this question. In this study, we investigate how deep neural networks trained for segmentation generalize. Our analysis is based on synthesizing a series of datasets of images with the target object of the same shape but with varying pixel intensity of the foreground object and the background. This simulates basic effects of changing equipment models and sequence types. We also consider scenarios when datasets with different image properties are combined to determine whether generalizability of the network to other scenarios is improved. We found that the generalizability of segmentation networks to changing intensities is poor. We also found that the generalizability is somewhat improved when different datasets are combined but that generalizability is typically limited to data similar to the two types of datasets included in training and not to datasets with different image intensities.
Authors
MLA Citation
Zhu, Z., et al. “Deep neural networks trained for segmentation are sensitive to brightness changes: Preliminary results.” Progress in Biomedical Optics and Imaging  Proceedings of Spie, vol. 11597, 2021. Scopus, doi:10.1117/12.2582190.
URI
https://scholars.duke.edu/individual/pub1478564
Source
scopus
Published In
Progress in Biomedical Optics and Imaging Proceedings of Spie
Volume
11597
Published Date
DOI
10.1117/12.2582190