David Kirsch

Overview:

My clinical interests are the multi-modality care of patients with bone and soft tissue sarcomas and developing new sarcoma therapies. My laboratory interests include utilizing mouse models of cancer to study cancer and radiation biology in order to develop new cancer therapies in the pre-clinical setting.

Positions:

Barbara Levine University Distinguished Professor

Radiation Oncology
School of Medicine

Professor of Radiation Oncology

Radiation Oncology
School of Medicine

Professor of Pharmacology and Cancer Biology

Pharmacology & Cancer Biology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Affiliate of the Regeneration Next Initiative

Regeneration Next Initiative
School of Medicine

Education:

M.D. 2000

Johns Hopkins University School of Medicine

Ph.D. 2000

Johns Hopkins University School of Medicine

Grants:

Awakening the dormant tumor: the role of the tumor microenvironment in breast cancer recurrence

Administered By
Pharmacology & Cancer Biology
Awarded By
National Institutes of Health
Role
Co-Sponsor
Start Date
End Date

Defining the Cellular Target of Radiation Therapy

Administered By
Radiation Oncology
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Investigating the role of the transcriptional coactivator TAZ in alveolar rhabdomyosarcoma

Administered By
Pediatrics, Hematology-Oncology
Role
Collaborator
Start Date
End Date

Clinical Trials Umbrella - Scanned Beam

Administered By
Radiation Oncology
Awarded By
Massachusetts General Hospital
Role
Co-Principal Investigator
Start Date
End Date

Engineered imaging nanoparticles for real-time detection of cancer in the tumor bed

Administered By
Orthopaedics
Role
Investigator
Start Date
End Date

Publications:

MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice.

Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variability in image analysis techniques can lead to inconsistent results. We have developed quantitative imaging for application in the preclinical arm of a coclinical trial by using a genetically engineered mouse model of soft tissue sarcoma. Magnetic resonance imaging (MRI) images were acquired 1 day before and 1 week after radiation therapy. After the second MRI, the primary tumor was surgically removed by amputating the tumor-bearing hind limb, and mice were followed for up to 6 months. An automatic analysis pipeline was used for multicontrast MRI data using a convolutional neural network for tumor segmentation followed by radiomics analysis. We then calculated radiomics features for the tumor, the peritumoral area, and the 2 combined. The first radiomics analysis focused on features most indicative of radiation therapy effects; the second radiomics analysis looked for features that might predict primary tumor recurrence. The segmentation results indicated that Dice scores were similar when using multicontrast versus single T2-weighted data (0.863 vs 0.861). One week post RT, larger tumor volumes were measured, and radiomics analysis showed greater heterogeneity. In the tumor and peritumoral area, radiomics features were predictive of primary tumor recurrence (AUC: 0.79). We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies.
Authors
Holbrook, MD; Blocker, SJ; Mowery, YM; Badea, A; Qi, Y; Xu, ES; Kirsch, DG; Johnson, GA; Badea, CT
MLA Citation
Holbrook, M. D., et al. “MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice.Tomography, vol. 6, no. 1, Mar. 2020, pp. 23–33. Pubmed, doi:10.18383/j.tom.2019.00021.
URI
https://scholars.duke.edu/individual/pub1437329
PMID
32280747
Source
pubmed
Published In
Tomography
Volume
6
Published Date
Start Page
23
End Page
33
DOI
10.18383/j.tom.2019.00021

Kaposi sarcoma in a patient with postpolio syndrome.

Authors
Whitley, MJ; Barrow, W; Craciunescu, OI; Pavlis, M; Kirsch, DG
MLA Citation
Whitley, Melodi Javid, et al. “Kaposi sarcoma in a patient with postpolio syndrome.Cutis, vol. 104, no. 5, Nov. 2019, pp. E20–22.
URI
https://scholars.duke.edu/individual/pub1423287
PMID
31886796
Source
pubmed
Published In
Cutis
Volume
104
Published Date
Start Page
E20
End Page
E22

Lack of supporting data make the risks of a clinical trial of radiation therapy as a treatment for COVID-19 pneumonia unacceptable.

Authors
Kirsch, DG; Diehn, M; Cucinotta, FA; Weichselbaum, R
MLA Citation
Kirsch, David G., et al. “Lack of supporting data make the risks of a clinical trial of radiation therapy as a treatment for COVID-19 pneumonia unacceptable.Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology, May 2020. Epmc, doi:10.1016/j.radonc.2020.04.060.
URI
https://scholars.duke.edu/individual/pub1441729
PMID
32413531
Source
epmc
Published In
Radiother Oncol
Published Date
DOI
10.1016/j.radonc.2020.04.060

Intravital imaging of mouse embryos.

Embryonic development is a complex process that is unamenable to direct observation. In this study, we implanted a window to the mouse uterus to visualize the developing embryo from embryonic day 9.5 to birth. This removable intravital window allowed manipulation and high-resolution imaging. In live mouse embryos, we observed transient neurotransmission and early vascularization of neural crest cell (NCC)-derived perivascular cells in the brain, autophagy in the retina, viral gene delivery, and chemical diffusion through the placenta. We combined the imaging window with in utero electroporation to label and track cell division and movement within embryos and observed that clusters of mouse NCC-derived cells expanded in interspecies chimeras, whereas adjacent human donor NCC-derived cells shrank. This technique can be combined with various tissue manipulation and microscopy methods to study the processes of development at unprecedented spatiotemporal resolution.
Authors
Huang, Q; Cohen, MA; Alsina, FC; Devlin, G; Garrett, A; McKey, J; Havlik, P; Rakhilin, N; Wang, E; Xiang, K; Mathews, P; Wang, L; Bock, C; Ruthig, V; Wang, Y; Negrete, M; Wong, CW; Murthy, PKL; Zhang, S; Daniel, AR; Kirsch, DG; Kang, Y; Capel, B; Asokan, A; Silver, DL; Jaenisch, R; Shen, X
MLA Citation
Huang, Qiang, et al. “Intravital imaging of mouse embryos.Science, vol. 368, no. 6487, Apr. 2020, pp. 181–86. Pubmed, doi:10.1126/science.aba0210.
URI
https://scholars.duke.edu/individual/pub1436476
PMID
32273467
Source
pubmed
Published In
Science
Volume
368
Published Date
Start Page
181
End Page
186
DOI
10.1126/science.aba0210

An intravital window to image the colon in real time.

Intravital microscopy is a powerful technique to observe dynamic processes with single-cell resolution in live animals. No intravital window has been developed for imaging the colon due to its anatomic location and motility, although the colon is a key organ where the majority of microbiota reside and common diseases such as inflammatory bowel disease, functional gastrointestinal disorders, and colon cancer occur. Here we describe an intravital murine colonic window with a stabilizing ferromagnetic scaffold for chronic imaging, minimizing motion artifacts while maximizing long-term survival by preventing colonic obstruction. Using this setup, we image fluorescently-labeled stem cells, bacteria, and immune cells in live animal colons. Furthermore, we image nerve activity via calcium imaging in real time to demonstrate that electrical sacral nerve stimulation can activate colonic enteric neurons. The simple implantable apparatus enables visualization of live processes in the colon, which will open the window to a broad range of studies.
Authors
Rakhilin, N; Garrett, A; Eom, C-Y; Chavez, KR; Small, DM; Daniel, AR; Kaelberer, MM; Mejooli, MA; Huang, Q; Ding, S; Kirsch, DG; Bohórquez, DV; Nishimura, N; Barth, BB; Shen, X
MLA Citation
Rakhilin, Nikolai, et al. “An intravital window to image the colon in real time.Nat Commun, vol. 10, no. 1, Dec. 2019, p. 5647. Pubmed, doi:10.1038/s41467-019-13699-w.
URI
https://scholars.duke.edu/individual/pub1423159
PMID
31827103
Source
pubmed
Published In
Nature Communications
Volume
10
Published Date
Start Page
5647
DOI
10.1038/s41467-019-13699-w