Lars Grimm

Positions:

Associate Professor of Radiology

Radiology, Breast Imaging
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

B.S. 2004

Stanford University

M.D. 2009

Yale University School of Medicine

M.H.S. 2009

Yale University

Residency, Diagnostic Radiology

Duke University School of Medicine

Fellowship, Radiology, Breast Imaging

Duke University School of Medicine

Grants:

Tomosynthesis Mammographic Imaging Screening Trial (TMIST)

Administered By
Radiology, Breast Imaging
Role
Principal Investigator
Start Date
End Date

Prediction of upstaging DCIS to invasive disease: performance comparison between breast radiologists and computer vision algorithms

Administered By
Radiology, Breast Imaging
Awarded By
Radiological Society of North America
Role
Principal Investigator
Start Date
End Date

Development of a personalized evidence-based algorithm for the management of suspicious calcifications

Administered By
Radiology, Breast Imaging
Awarded By
Ge-Aur Radiology Research
Role
Principal Investigator
Start Date
End Date

Tomosynthesis Mammographic Imaging Screening Trial (TMIST)

Administered By
Radiology, Breast Imaging
Awarded By
ECOG-ACRIN Medical Research Foundation Inc.
Role
Principal Investigator
Start Date
End Date

Mixed Methods Study of Trainee Perceptions of Radiology with a Focus on Gender and Racial Inequality

Administered By
Radiology, Breast Imaging
Awarded By
Association of University Radiologists Research and Education Foundation
Role
Principal Investigator
Start Date
End Date

Publications:

Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline

In mammography and tomosynthesis, radiologists use the geometric relationship of the four standard screening views to detect breast abnormalities. To date, computer aided detection methods focus on formulations based only on a single view. Recent multi-view methods are either black box approaches using methods such as relation blocks, or perform extensive, case-level feature aggregation requiring large data redundancy. In this study, we propose Retina-Match, an end-to-end trainable pipeline for detection, matching, and refinement that can effectively perform ipsilateral lesion matching in paired screening mammography images. We demonstrate effectiveness on a private, digital mammography data set with 1,016 biopsied lesions and 2,000 negative cases.
Authors
Ren, Y; Lu, J; Liang, Z; Grimm, LJ; Kim, C; Taylor-Cho, M; Yoon, S; Marks, JR; Lo, JY
MLA Citation
Ren, Y., et al. “Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12905 LNCS, 2021, pp. 345–54. Scopus, doi:10.1007/978-3-030-87240-3_33.
URI
https://scholars.duke.edu/individual/pub1499563
Source
scopus
Published In
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
12905 LNCS
Published Date
Start Page
345
End Page
354
DOI
10.1007/978-3-030-87240-3_33

Factors Influential in the Selection of Radiology Residents in the Post-Step 1 World: A Discrete Choice Experiment.

OBJECTIVES: Reporting of United States Medical Licensing Examination Step 1 results will transition from a numerical score to a pass or fail result. We sought an objective analysis to determine changes in the relative importance of resident application attributes when numerical Step 1 results are replaced. METHODS: A discrete choice experiment was designed to model radiology resident selection and determine the relative weights of various application factors when paired with a numerical or pass or fail Step 1 result. Faculty involved in resident selection at 14 US radiology programs chose between hypothetical pairs of applicant profiles between August and November 2020. A conditional logistic regression model assessed the relative weights of the attributes, and odds ratios (ORs) were calculated. RESULTS: There were 212 participants. When a numerical Step 1 score was provided, the most influential attributes were medical school (OR: 2.35, 95% confidence interval [CI]: 2.07-2.67), Black or Hispanic race or ethnicity (OR: 2.04, 95% CI: 1.79-2.38), and Step 1 score (OR: 1.8, 95% CI: 1.69-1.95). When Step 1 was reported as pass, the applicant's medical school grew in influence (OR: 2.78, 95% CI: 2.42-3.18), and there was a significant increase in influence of Step 2 scores (OR: 1.31, 95% CI: 1.23-1.40 versus OR 1.57, 95% CI: 1.46-1.69). There was little change in the relative influence of race or ethnicity, gender, class rank, or clerkship honors. DISCUSSION: When Step 1 reporting transitions to pass or fail, medical school prestige gains outsized influence and Step 2 scores partly fill the gap left by Step 1 examination as a single metric of decisive importance in application decisions.
Authors
Maxfield, CM; Montano-Campos, JF; Chapman, T; Desser, TS; Ho, CP; Hull, NC; Kelly, HR; Kennedy, TA; Koontz, NA; Knippa, EE; McLoud, TC; Milburn, J; Mills, MK; Morgan, DE; Morgan, R; Peterson, RB; Salastekar, N; Thorpe, MP; Zarzour, JG; Reed, SD; Grimm, LJ
MLA Citation
Maxfield, Charles M., et al. “Factors Influential in the Selection of Radiology Residents in the Post-Step 1 World: A Discrete Choice Experiment.J Am Coll Radiol, vol. 18, no. 11, Nov. 2021, pp. 1572–80. Pubmed, doi:10.1016/j.jacr.2021.07.005.
URI
https://scholars.duke.edu/individual/pub1492912
PMID
34332914
Source
pubmed
Published In
Journal of the American College of Radiology : Jacr
Volume
18
Published Date
Start Page
1572
End Page
1580
DOI
10.1016/j.jacr.2021.07.005

Axillary Imaging Following a New Invasive Breast Cancer Diagnosis—A Radiologist’s Dilemma

<jats:title>Abstract</jats:title> <jats:p>Traditionally, patients with newly diagnosed invasive breast cancer underwent axillary US to assess for suspicious axillary lymph nodes (LNs), which were then targeted for image-guided needle biopsy to determine the presence of metastasis. Over the past decade, there has been a shift towards axillary preservation. For patients with palpable lymphadenopathy, the decision to perform axillary imaging with documentation of the number and location of abnormal LNs in preparation for image-guided LN sampling is straightforward. Since LN involvement correlates with cancer size, it is reasonable to image the axilla in patients with tumors larger than 5 cm; however, for tumors smaller than 5 cm, axillary imaging is often deferred until after the tumor molecular subtype and treatment plan are established. Over the last decade, neoadjuvant chemotherapy (NACT) is increasingly used for smaller cancers with more aggressive molecular subtypes. In most cases, detecting axillary metastasis is critical when deciding whether the patient would benefit from NACT. There is increasing evidence that abnormal axillary US findings correlates with LN metastases and reliably establishes a baseline to monitor response to NACT. Depending on hormone receptor status, practices may choose to image the axilla in the setting of clinical stage T1 and T2 cancers to evaluate nodal status and help determine further steps in care. Radiologists should understand the nuances of axillary management and the scope and challenges of LN marking techniques that significantly increase the precision of limited axillary surgery.</jats:p>
Authors
Dialani, V; Dogan, B; Dodelzon, K; Dontchos, BN; Modi, N; Grimm, L
MLA Citation
Dialani, Vandana, et al. “Axillary Imaging Following a New Invasive Breast Cancer Diagnosis—A Radiologist’s Dilemma.” Journal of Breast Imaging, Oxford University Press (OUP). Crossref, doi:10.1093/jbi/wbab082.
URI
https://scholars.duke.edu/individual/pub1500591
Source
crossref
Published In
Journal of Breast Imaging
DOI
10.1093/jbi/wbab082

Radiology Stereotypes, Application Barriers, and Hospital Integration: A Mixed-methods Study of Medical Student Perceptions of Radiology.

RATIONALE AND OBJECTIVES: Limited exposure to radiology by medical students can perpetuate negative stereotypes and hamper recruitment efforts. The purpose of this study is to understand medical students' perceptions of radiology and how they change based on medical education and exposure. MATERIALS AND METHODS: A single-institution mixed-methods study included four groups of medical students with different levels of radiology exposure. All participants completed a 16-item survey regarding demographics, opinions of radiology, and perception of radiology stereotypes. Ten focus groups were administered to probe perceptions of radiology. Focus groups were coded to identify specific themes in conjunction with the survey results. RESULTS: Forty-nine participants were included. Forty-two percent of participants had positive opinions of radiology. Multiple radiology stereotypes were identified, and false stereotypes were diminished with increased radiology exposure. Opinions of the impact of artificial intelligence on radiology closely aligned with positive or negative views of the field overall. Multiple barriers to applying for a radiology residency position were identified including board scores and lack of mentorship. COVID-19 did not affect perceptions of radiology. There was broad agreement that students do not enter medical school with many preconceived notions of radiology, but that subsequent exposure was generally positive. Exposure both solidified and eliminated various stereotypes. Finally, there was general agreement that radiology is integral to the health system with broad exposure on all services. CONCLUSION: Medical student perceptions of radiology are notably influenced by exposure and radiology programs should take active steps to engage in medical student education.
Authors
Grimm, LJ; Fish, LJ; Carrico, CW; Martin, JG; Nwankwo, VC; Farley, S; Meltzer, CC; Maxfield, CM
MLA Citation
Grimm, Lars J., et al. “Radiology Stereotypes, Application Barriers, and Hospital Integration: A Mixed-methods Study of Medical Student Perceptions of Radiology.Acad Radiol, Sept. 2021. Pubmed, doi:10.1016/j.acra.2021.08.020.
URI
https://scholars.duke.edu/individual/pub1497665
PMID
34563441
Source
pubmed
Published In
Acad Radiol
Published Date
DOI
10.1016/j.acra.2021.08.020

Multimodal Patient-Specific Registration for Breast Imaging Using Biomechanical Modeling with Reference to AI Evaluation of Breast Tumor Change.

<h4>Background</h4>The strategy to combat the problem associated with large deformations in the breast due to the difference in the medical imaging of patient posture plays a vital role in multimodal medical image registration with artificial intelligence (AI) initiatives. How to build a breast biomechanical model simulating the large-scale deformation of soft tissue remains a challenge but is highly desirable.<h4>Methods</h4>This study proposed a hybrid individual-specific registration model of the breast combining finite element analysis, property optimization, and affine transformation to register breast images. During the registration process, the mechanical properties of the breast tissues were individually assigned using an optimization process, which allowed the model to become patient specific. Evaluation and results:&nbsp;The proposed method has been extensively tested on two datasets collected from two independent institutions, one from America and another from Hong Kong.<h4>Conclusions</h4>Our method can accurately predict the deformation of breasts from the supine to prone position for both the Hong Kong and American samples, with a small target registration error of lesions.
Authors
Xue, C; Tang, F-H; Lai, CWK; Grimm, LJ; Lo, JY
MLA Citation
Xue, Cheng, et al. “Multimodal Patient-Specific Registration for Breast Imaging Using Biomechanical Modeling with Reference to AI Evaluation of Breast Tumor Change.Life (Basel, Switzerland), vol. 11, no. 8, July 2021. Epmc, doi:10.3390/life11080747.
URI
https://scholars.duke.edu/individual/pub1489690
PMID
34440490
Source
epmc
Published In
Life (Basel)
Volume
11
Published Date
DOI
10.3390/life11080747