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

Tomosynthesis Mammographic Imaging Screening Trial (TMIST)

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

Publications:

Mask Embedding for Realistic High-Resolution Medical Image Synthesis

© 2019, Springer Nature Switzerland AG. Generative Adversarial Networks (GANs) have found applications in natural image synthesis and begin to show promises generating synthetic medical images. In many cases, the ability to perform controlled image synthesis using masked priors such as shape and size of organs is desired. However, mask-guided image synthesis is challenging due to the pixel level mask constraint. While the few existing mask-guided image generation approaches suffer from the lack of fine-grained texture details, we tackle the issue of mask-guided stochastic image synthesis via mask embedding. Our novel architecture first encodes the input mask as an embedding vector and then inject these embedding into the random latent vector input. The intuition is to classify semantic masks into partitions before feature up-sampling for improved sample space mapping stability. We validate our approach on a large dataset containing 39,778 patients with 443,556 negative screening Full Field Digital Mammography (FFDM) images. Experimental results show that our approach can generate realistic high-resolution (256 × 512 ) images with pixel-level mask constraints, and outperform other state-of-the-art approaches.
Authors
Ren, Y; Zhu, Z; Li, Y; Kong, D; Hou, R; Grimm, LJ; Marks, JR; Lo, JY
MLA Citation
Ren, Y., et al. “Mask Embedding for Realistic High-Resolution Medical Image Synthesis.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11769 LNCS, 2019, pp. 422–30. Scopus, doi:10.1007/978-3-030-32226-7_47.
URI
https://scholars.duke.edu/individual/pub1423148
Source
scopus
Published In
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
11769 LNCS
Published Date
Start Page
422
End Page
430
DOI
10.1007/978-3-030-32226-7_47

Breast Cancer Radiogenomics: Current Status and Future Directions.

Radiogenomics is an area of research that aims to identify associations between imaging phenotypes ("radio-") and tumor genome ("-genomics"). Breast cancer radiogenomics research in particular has been an especially prolific area of investigation in recent years as evidenced by the wide number and variety of publications and conferences presentations. To date, research has primarily been focused on dynamic contrast enhanced pre-operative breast MRI and breast cancer molecular subtypes, but investigations have extended to all breast imaging modalities as well as multiple additional genetic markers including those that are commercially available. Furthermore, both human and computer-extracted features as well as deep learning techniques have been explored. This review will summarize the specific imaging modalities used in radiogenomics analysis, describe the methods of extracting imaging features, and present the types of genomics, molecular, and related information used for analysis. Finally, the limitations and future directions of breast cancer radiogenomics research will be discussed.
MLA Citation
Grimm, Lars J., and Maciej A. Mazurowski. “Breast Cancer Radiogenomics: Current Status and Future Directions..” Acad Radiol, vol. 27, no. 1, Jan. 2020, pp. 39–46. Pubmed, doi:10.1016/j.acra.2019.09.012.
URI
https://scholars.duke.edu/individual/pub1415625
PMID
31818385
Source
pubmed
Published In
Acad Radiol
Volume
27
Published Date
Start Page
39
End Page
46
DOI
10.1016/j.acra.2019.09.012

Screening for Breast Cancer in Average-Risk Women.

Authors
MLA Citation
Grimm, Lars J. “Screening for Breast Cancer in Average-Risk Women..” Ann Intern Med, vol. 171, no. 6, Sept. 2019. Pubmed, doi:10.7326/L19-0472.
URI
https://scholars.duke.edu/individual/pub1412017
PMID
31525747
Source
pubmed
Published In
Ann Intern Med
Volume
171
Published Date
Start Page
450
DOI
10.7326/L19-0472

Ductal Carcinoma in Situ: Current Concepts in Biology, Imaging, and Treatment.

Ductal carcinoma in situ (DCIS) of the breast is a group of heterogeneous epithelial proliferations confined to the milk ducts that nearly always present in asymptomatic women on breast cancer screening. A stage 0, preinvasive breast cancer, increased detection of DCIS was initially hailed as a means to prevent invasive breast cancer through surgical treatment with adjuvant radiation and/or endocrine therapies. However, controversy in the medical community has emerged in the past two decades that a fraction of DCIS represents overdiagnosis, leading to unnecessary treatments and resulting morbidity. The imaging hallmarks of DCIS include linearly or segmentally distributed calcifications on mammography or nonmass enhancement on breast MRI. Imaging features have been shown to reflect the biological heterogeneity of DCIS lesions, with recent studies indicating MRI may identify a greater fraction of higher-grade lesions than mammography does. There is strong interest in the surgical, imaging, and oncology communities to better align DCIS management with biology, which has resulted in trials of active surveillance and therapy that is less aggressive. However, risk stratification of DCIS remains imperfect, which has limited the development of precision therapy approaches matched to DCIS aggressiveness. Accordingly, there are opportunities for breast imaging radiologists to assist the oncology community by leveraging advanced imaging techniques to identify appropriate patients for the less aggressive DCIS treatments.
Authors
Shehata, M; Grimm, L; Ballantyne, N; Lourenco, A; Demello, LR; Kilgore, MR; Rahbar, H
MLA Citation
Shehata, Mariam, et al. “Ductal Carcinoma in Situ: Current Concepts in Biology, Imaging, and Treatment..” J Breast Imaging, vol. 1, no. 3, Sept. 2019, pp. 166–76. Pubmed, doi:10.1093/jbi/wbz039.
URI
https://scholars.duke.edu/individual/pub1411923
PMID
31538141
Source
pubmed
Published In
J Breast Imaging
Volume
1
Published Date
Start Page
166
End Page
176
DOI
10.1093/jbi/wbz039

Prediction of Upstaged Ductal Carcinoma in situ Using Forced Labeling and Domain Adaptation.

OBJECTIVE: The goal of this study is to use adjunctive classes to improve a predictive model whose performance is limited by the common problems of small numbers of primary cases, high feature dimensionality, and poor class separability. Specifically, our clinical task is to use mammographic features to predict whether ductal carcinoma in situ (DCIS) identified at needle core biopsy will be later upstaged or shown to contain invasive breast cancer. METHODS: To improve the prediction of pure DCIS (negative) versus upstaged DCIS (positive) cases, this study considers the adjunctive roles of two related classes: atypical ductal hyperplasia (ADH), a non-cancer type of breast abnormity, and invasive ductal carcinoma (IDC), with 113 computer vision based mammographic features extracted from each case. To improve the baseline Model A classification of pure vs. upstaged DCIS, we designed three different strategies (Models B, C, D) with different ways of embedding features or inputs. RESULTS: Based on ROC analysis, the baseline Model A performed with AUC of 0.614 (95% CI, 0.496-0.733). All three new models performed better than the baseline, with domain adaptation (Model D) performing the best with an AUC of 0.697 (95% CI, 0.595-0.797). CONCLUSION: We improved the prediction performance of DCIS upstaging by embedding two related pathology classes in different training phases. SIGNIFICANCE: The three new strategies of embedding related class data all outperformed the baseline model, thus demonstrating not only feature similarities among these different classes, but also the potential for improving classification by using other related classes.
Authors
Hou, R; Mazurowski, MA; Grimm, LJ; Marks, JR; King, LM; Maley, CC; Hwang, ES; Lo, JY
MLA Citation
Hou, Rui, et al. “Prediction of Upstaged Ductal Carcinoma in situ Using Forced Labeling and Domain Adaptation..” Ieee Trans Biomed Eng, Sept. 2019. Pubmed, doi:10.1109/TBME.2019.2940195.
URI
https://scholars.duke.edu/individual/pub1409876
PMID
31502960
Source
pubmed
Published In
Ieee Trans Biomed Eng
Published Date
DOI
10.1109/TBME.2019.2940195