Allison Hall

Positions:

Associate Professor of Pathology

Pathology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

MD./PhD. 2007

Duke University School of Medicine

Resident, Pathology

Duke University School of Medicine

Grants:

Culturally appropriate screening and diagnosis of cervical cancer in East Africa

Administered By
Biomedical Engineering
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

A novel strategy to see and treat breast cancer: translation to intra-operative breast margin assessment

Administered By
Biomedical Engineering
Awarded By
National Institutes of Health
Role
Co Investigator
Start Date
End Date

Towards a viable solution for a see and treat paradigm for cervical pre-cancer in East Africa

Administered By
Biomedical Engineering
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Anti-HPV RNA Interference Using Modified RNA's

Administered By
Pediatrics, Infectious Diseases
Awarded By
National Institutes of Health
Role
Research Assistant
Start Date
End Date

Novel see and treat strategies for cervical cancer prevention in low-resource settings

Administered By
Biomedical Engineering
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Publications:

Unmasking the immune microecology of ductal carcinoma in situ with deep learning.

Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial variability within ductal carcinoma in situ (DCIS) samples and its association with progression are not well understood. To characterise tissue spatial architecture and the microenvironment of DCIS, we designed and validated a new deep learning pipeline, UNMaSk. Following automated detection of individual DCIS ducts using a new method IM-Net, we applied spatial tessellation to create virtual boundaries for each duct. To study local TIL infiltration for each duct, DRDIN was developed for mapping the distribution of TILs. In a dataset comprising grade 2-3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, the colocalisation of TILs with DCIS ducts was significantly lower in pure DCIS compared to adjacent DCIS, which may suggest a more inflamed tissue ecology local to DCIS ducts in adjacent DCIS cases. Our study demonstrates that technological developments in deep convolutional neural networks and digital pathology can enable an automated morphological and microenvironmental analysis of DCIS, providing a new way to study differential immune ecology for individual ducts and identify new markers of progression.
Authors
Narayanan, PL; Raza, SEA; Hall, AH; Marks, JR; King, L; West, RB; Hernandez, L; Guppy, N; Dowsett, M; Gusterson, B; Maley, C; Hwang, ES; Yuan, Y
MLA Citation
Narayanan, Priya Lakshmi, et al. “Unmasking the immune microecology of ductal carcinoma in situ with deep learning.Npj Breast Cancer, vol. 7, no. 1, Mar. 2021, p. 19. Pubmed, doi:10.1038/s41523-020-00205-5.
URI
https://scholars.duke.edu/individual/pub1475719
PMID
33649333
Source
pubmed
Published In
Npj Breast Cancer
Volume
7
Published Date
Start Page
19
DOI
10.1038/s41523-020-00205-5

Pathology Engagement in Global Health: Exploring Opportunities to Get Involved.

Authors
Razzano, D; Hall, A; Gardner, JM; Jiang, XS
MLA Citation
Razzano, Dana, et al. “Pathology Engagement in Global Health: Exploring Opportunities to Get Involved.Arch Pathol Lab Med, vol. 143, no. 4, Apr. 2019, pp. 418–21. Pubmed, doi:10.5858/arpa.2018-0280-ED.
URI
https://scholars.duke.edu/individual/pub1477867
PMID
30920866
Source
pubmed
Published In
Arch Pathol Lab Med
Volume
143
Published Date
Start Page
418
End Page
421
DOI
10.5858/arpa.2018-0280-ED

Endometrial Adenocarcinomas With No Specific Molecular Profile: Morphologic Features and Molecular Alterations of "Copy-number Low" Tumors.

The study evaluated morphologic patterns, mutational profiles, and β-catenin immunohistochemistry (IHC) in copy-number low (CNL) endometrial adenocarcinomas (EAs). CNL EAs (n=19) with next-generation or whole genome sequencing results and available tissue for IHC were identified from our institutional database. Clinical data and histologic slides were reviewed. IHC for β-catenin was performed and correlated with mutation status. Images of digital slides of CNL EAs from The Cancer Genome Atlas (TCGA) database (n=90) were blindly reviewed by 4 pathologists, and morphology was correlated with mutation status. Categorical variables were analyzed using the Fisher exact test, and agreement was assessed using Fleiss κ. CTNNB1 mutations were present in 63% (12/19) of CNL EAs. β-catenin nuclear localization was present in 83% of CTNNB1-mutated tumors (10/12) and in 0% (0/7) of CTNNB1-wildtype tumors (sensitivity 0.83, specificity 1.00). Squamous differentiation (SD) was present in 47% (9/19) and was more often observed in CTNNB1-mutated tumors (P=0.02). Mucinous differentiation (MD) was associated with KRAS mutations (P<0.01). Digital image review of TCGA CNL EAs revealed that pathologist agreement on SD was strong (κ=0.82), whereas agreement on MD was weak (κ=0.48). Pathologists identified SD in 22% (20/90), which was significantly associated with the presence of CTNNB1 mutations (P<0.01). CNL EAs demonstrate several morphologies with divergent molecular profiles. SD was significantly associated with CTNNB1 mutations and nuclear localization of β-catenin in these tumors. Nuclear expression of β-catenin is a sensitive and specific IHC marker for CTNNB1 mutations in CNL EAs. CNL EAs with KRAS mutations often displayed MD.
Authors
Meljen, VT; Mittenzwei, R; Wong, J; Puechl, A; Whitaker, R; Broadwater, G; Hall, AH; Bean, SM; Bentley, RC; Elvin, JA; Berchuck, A; Previs, RA; Strickland, KC
MLA Citation
Meljen, Vivienne T., et al. “Endometrial Adenocarcinomas With No Specific Molecular Profile: Morphologic Features and Molecular Alterations of "Copy-number Low" Tumors.Int J Gynecol Pathol, vol. 40, no. 6, Nov. 2021, pp. 587–96. Pubmed, doi:10.1097/PGP.0000000000000747.
URI
https://scholars.duke.edu/individual/pub1476627
PMID
33720082
Source
pubmed
Published In
Int J Gynecol Pathol
Volume
40
Published Date
Start Page
587
End Page
596
DOI
10.1097/PGP.0000000000000747

Survey of Global Health Education and Training in Pathology Residency Programs in the United States.

OBJECTIVES: This study assessed the prevalence, general interest, and barriers to implementing global health curricula in pathology residency programs. METHODS: We conducted a survey of 166 US pathology residency programs. RESULTS: Thirty-two (195) of 166 programs responded. Of these, 13% have a formalized global health program (n = 4), and the majority indicated at least some general interest in global health among trainees (88%, n = 28) and faculty (94%, n = 30), albeit at a low to moderate level. Funding limitations, regulatory constraints, and insufficient knowledge of global health were frequently cited barriers to developing a global health program. CONCLUSIONS: Few US pathology departments incorporate global health education into postgraduate training. The importance of pathology in global health has been underappreciated, despite its critical role in the delivery of health care in resource-limited settings. One solution is for pathology departments to expand global health educational opportunities for trainees.
Authors
Glynn, EH; Guarner, J; Hall, A; Nelson, AM; Andiric, LR; Milner, DA; Eichbaum, Q
MLA Citation
Glynn, Emily H., et al. “Survey of Global Health Education and Training in Pathology Residency Programs in the United States.Am J Clin Pathol, vol. 153, no. 3, Feb. 2020, pp. 374–79. Pubmed, doi:10.1093/ajcp/aqz178.
URI
https://scholars.duke.edu/individual/pub1422452
PMID
31755908
Source
pubmed
Published In
Am J Clin Pathol
Volume
153
Published Date
Start Page
374
End Page
379
DOI
10.1093/ajcp/aqz178

Derivation of a nuclear heterogeneity image index to grade DCIS.

Abnormalities in cell nuclear morphology are a hallmark of cancer. Histological assessment of cell nuclear morphology is frequently used by pathologists to grade ductal carcinoma in situ (DCIS). Objective methods that allow standardization and reproducibility of cell nuclear morphology assessment have potential to improve the criteria needed to predict DCIS progression and recurrence. Aggressive cancers are highly heterogeneous. We asked whether cell nuclear morphology heterogeneity could be incorporated into a metric to classify DCIS. We developed a nuclear heterogeneity image index to objectively, and quantitatively grade DCIS. A whole-tissue cell nuclear morphological analysis, that classified tumors by the worst ten percent in a duct-by-duct manner, identified nuclear size ranges associated with each DCIS grade. Digital image analysis further revealed increasing heterogeneity within ducts or between ducts in tissues of worsening DCIS grade. The findings illustrate how digital image analysis comprises a supplemental tool for pathologists to objectively classify DCIS and in the future, may provide a method to predict patient outcome through analysis of nuclear heterogeneity.
Authors
Hayward, M-K; Louise Jones, J; Hall, A; King, L; Ironside, AJ; Nelson, AC; Shelley Hwang, E; Weaver, VM
MLA Citation
Hayward, Mary-Kate, et al. “Derivation of a nuclear heterogeneity image index to grade DCIS.Comput Struct Biotechnol J, vol. 18, 2020, pp. 4063–70. Pubmed, doi:10.1016/j.csbj.2020.11.040.
URI
https://scholars.duke.edu/individual/pub1469320
PMID
33363702
Source
pubmed
Published In
Computational and Structural Biotechnology Journal
Volume
18
Published Date
Start Page
4063
End Page
4070
DOI
10.1016/j.csbj.2020.11.040