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:

Invited Commentary: Key Role of Imaging in Management and Prognosis of Hepatocellular Carcinoma.

Authors
MLA Citation
Bashir, Mustafa R. “Invited Commentary: Key Role of Imaging in Management and Prognosis of Hepatocellular Carcinoma.Radiographics, vol. 41, no. 6, Oct. 2021, pp. E171–72. Pubmed, doi:10.1148/rg.2021210151.
URI
https://scholars.duke.edu/individual/pub1498533
PMID
34597240
Source
pubmed
Published In
Radiographics
Volume
41
Published Date
Start Page
E171
End Page
E172
DOI
10.1148/rg.2021210151

Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Deep Learning: A Multi-Center and Prospective Validation Study.

Microvascular invasion (MVI) is a critical risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). Preknowledge of MVI would assist tailored surgery planning in HCC management. In this multicenter study, we aimed to explore the validity of deep learning (DL) in MVI prediction using two imaging modalities-contrast-enhanced computed tomography (CE-CT) and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI). A total of 750 HCCs were enrolled from five Chinese tertiary hospitals. Retrospective CE-CT (n = 306, collected between March, 2013 and July, 2019) and EOB-MRI (n = 329, collected between March, 2012 and March, 2019) data were used to train two DL models, respectively. Prospective external validation (n = 115, collected between July, 2015 and February, 2018) was performed to assess the developed models. Furthermore, DL-based attention maps were utilized to visualize high-risk MVI regions. Our findings revealed that the EOB-MRI-based DL model achieved superior prediction outcome to the CE-CT-based DL model (area under receiver operating characteristics curve (AUC): 0.812 vs. 0.736, p = 0.038; sensitivity: 70.4% vs. 57.4%, p = 0.015; specificity: 80.3% vs. 86.9%, p = 0.052). DL attention maps could visualize peritumoral high-risk areas with genuine histopathologic confirmation. Both DL models could stratify high and low-risk groups regarding progression free survival and overall survival (p < 0.05). Thus, DL can be an efficient tool for MVI prediction, and EOB-MRI was proven to be the modality with advantage for MVI assessment than CE-CT.
Authors
Wei, J; Jiang, H; Zeng, M; Wang, M; Niu, M; Gu, D; Chong, H; Zhang, Y; Fu, F; Zhou, M; Chen, J; Lyv, F; Wei, H; Bashir, MR; Song, B; Li, H; Tian, J
MLA Citation
Wei, Jingwei, et al. “Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Deep Learning: A Multi-Center and Prospective Validation Study.Cancers (Basel), vol. 13, no. 10, May 2021. Pubmed, doi:10.3390/cancers13102368.
URI
https://scholars.duke.edu/individual/pub1483457
PMID
34068972
Source
pubmed
Published In
Cancers
Volume
13
Published Date
DOI
10.3390/cancers13102368

Correction to: Artificial intelligence in assessment of hepatocellular carcinoma treatment response.

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. “Correction to: Artificial intelligence in assessment of hepatocellular carcinoma treatment response.Abdom Radiol (Ny), vol. 46, no. 8, Aug. 2021, pp. 3672–73. Pubmed, doi:10.1007/s00261-021-03098-5.
URI
https://scholars.duke.edu/individual/pub1482741
PMID
34028593
Source
pubmed
Published In
Abdom Radiol (Ny)
Volume
46
Published Date
Start Page
3672
End Page
3673
DOI
10.1007/s00261-021-03098-5

LI-RADS treatment response algorithm for detecting incomplete necrosis in hepatocellular carcinoma after locoregional treatment: a systematic review and meta-analysis using individual patient data.

PURPOSE: To perform a systematic review and meta-analysis using individual patient data to investigate the diagnostic performance of Liver Imaging Reporting and Data System (LI-RADS) Treatment Response (TR) algorithm for detecting incomplete necrosis on pathology. METHODS: PubMed and EMBASE were searched from Jan 1, 2017 until October 14, 2020. Studies reporting diagnostic accuracy of LI-RADS TR algorithm on CT or MRI for detecting incomplete necrosis on pathology as a reference standard were included. Sensitivity and specificity were pooled using random-effects model. Subgroup analyses were performed for locoregional treatment (LRT) type and imaging modality. RESULTS: Six studies (393 patients, 534 lesions) were included. Pooled sensitivity was 0.56 (95% confidence interval [CI] 0.43-0.69) and specificity was 0.91 (95%CI 0.84-0.96). Pooled sensitivity was highest using arterial phase hyperenhancement (APHE) (0.67 [95%CI 0.51-0.81]), followed by washout (0.43 [95%CI 0.26-0.62]) and enhancement similar to pretreatment (0.24 [95%CI 0.15-0.36]). Among lesions with incomplete necrosis, 2% (95%CI 0.00-0.05) manifested as washout but no APHE; 0% (95% CI 0.00-0.02) as enhancement similar to pretreatment without both APHE and washout. Pooled sensitivity was lower after ablation than embolization (0.42 [95%CI, 0.28-0.57] vs. 0.65 [95%CI, 0.53-0.77], p = 0.033). MRI and CT were comparable (p = 0.783 and 0.290 for sensitivity and specificity). CONCLUSIONS: LI-RADS TR algorithm shows moderate sensitivity and high specificity for detecting incomplete necrosis after LRT. APHE is the dominant criterion, a washout contributes to small but meaningful extent, while the contribution of enhancement similar to pretreatment may be negligible. LRT type may affect performance of the algorithm.
Authors
Kim, T-H; Woo, S; Joo, I; Bashir, MR; Park, M-S; Burke, LMB; Mendiratta-Lala, M; Do, RKG
MLA Citation
URI
https://scholars.duke.edu/individual/pub1482742
PMID
34027566
Source
pubmed
Published In
Abdom Radiol (Ny)
Volume
46
Published Date
Start Page
3717
End Page
3728
DOI
10.1007/s00261-021-03122-8

Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom.

PURPOSE: Chemical shift-encoded magnetic resonance imaging enables accurate quantification of liver fat content though estimation of proton density fat-fraction (PDFF). Computed tomography (CT) is capable of quantifying fat, based on decreased attenuation with increased fat concentration. Current quantitative fat phantoms do not accurately mimic the CT number of human liver. The purpose of this work was to develop and validate an optimized phantom that simultaneously mimics the MRI and CT signals of fatty liver. METHODS: An agar-based phantom containing 12 vials doped with iodinated contrast, and with a granular range of fat fractions was designed and constructed within a novel CT and MR compatible spherical housing design. A four-site, three-vendor validation study was performed. MRI (1.5T and 3T) and CT images were obtained using each vendor's PDFF and CT reconstruction, respectively. An ROI centered in each vial was placed to measure MRI-PDFF (%) and CT number (HU). Mixed-effects model, linear regression, and Bland-Altman analysis were used for statistical analysis. RESULTS: MRI-PDFF agreed closely with nominal PDFF values across both field strengths and all MRI vendors. A linear relationship (slope = -0.54 ± 0.01%/HU, intercept = 37.15 ± 0.03%) with an R2 of 0.999 was observed between MRI-PDFF and CT number, replicating established in vivo signal behavior. Excellent test-retest repeatability across vendors (MRI: mean = -0.04%, 95% limits of agreement = [-0.24%, 0.16%]; CT: mean = 0.16 HU, 95% limits of agreement = [-0.15HU, 0.47HU]) and good reproducibility using GE scanners (MRI: mean = -0.21%, 95% limits of agreement = [-1.47%, 1.06%]; CT: mean = -0.18HU, 95% limits of agreement = [-1.96HU, 1.6HU]) were demonstrated. CONCLUSIONS: The proposed fat phantom successfully mimicked quantitative liver signal for both MRI and CT. The proposed fat phantom in this study may facilitate broader application and harmonization of liver fat quantification techniques using MRI and CT across institutions, vendors and imaging platforms.
Authors
Zhao, R; Hernando, D; Harris, DT; Hinshaw, LA; Li, K; Ananthakrishnan, L; Bashir, MR; Duan, X; Ghasabeh, MA; Kamel, IR; Lowry, C; Mahesh, M; Marin, D; Miller, J; Pickhardt, PJ; Shaffer, J; Yokoo, T; Brittain, JH; Reeder, SB
MLA Citation
Zhao, Ruiyang, et al. “Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom.Med Phys, vol. 48, no. 8, Aug. 2021, pp. 4375–86. Pubmed, doi:10.1002/mp.15038.
URI
https://scholars.duke.edu/individual/pub1484213
PMID
34105167
Source
pubmed
Published In
Med Phys
Volume
48
Published Date
Start Page
4375
End Page
4386
DOI
10.1002/mp.15038