Rachel Blitzblau

Positions:

Associate Professor of Radiation Oncology

Radiation Oncology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

MD./PhD. 2005

Tufts University

Intern (PGY1), Radiation Oncology

Harvard University

Resident, Radiation Oncology

Yale University

Chief Resident, Radiation Oncology

Yale University

Publications:

Optimizing Radiation Therapy to Boost Systemic Immune Responses in Breast Cancer: A Critical Review for Breast Radiation Oncologists.

Immunotherapy using immune checkpoint blockade has revolutionized the treatment of many types of cancer. Radiation therapy (RT)-particularly when delivered at high doses using newer techniques-may be capable of generating systemic antitumor effects when combined with immunotherapy in breast cancer. These systemic effects might be due to the local immune-priming effects of RT resulting in the expansion and circulation of effector immune cells to distant sites. Although this concept merits further exploration, several challenges need to be overcome. One is an understanding of how the heterogeneity of breast cancers may relate to tumor immunogenicity. Another concerns the need to develop knowledge and expertise in delivery, sequencing, and timing of RT with immunotherapy. Clinical trials addressing these issues are under way. We here review and discuss the particular opportunities and issues regarding this topic, including the design of informative clinical and translational studies.
Authors
Ho, AY; Wright, JL; Blitzblau, RC; Mutter, RW; Duda, DG; Norton, L; Bardia, A; Spring, L; Isakoff, SJ; Chen, JH; Grassberger, C; Bellon, JR; Beriwal, S; Khan, AJ; Speers, C; Dunn, SA; Thompson, A; Santa-Maria, CA; Krop, IE; Mittendorf, E; King, TA; Gupta, GP
MLA Citation
Ho, Alice Y., et al. “Optimizing Radiation Therapy to Boost Systemic Immune Responses in Breast Cancer: A Critical Review for Breast Radiation Oncologists.Int J Radiat Oncol Biol Phys, vol. 108, no. 1, Sept. 2020, pp. 227–41. Pubmed, doi:10.1016/j.ijrobp.2020.05.011.
URI
https://scholars.duke.edu/individual/pub1441266
PMID
32417409
Source
pubmed
Published In
Int J Radiat Oncol Biol Phys
Volume
108
Published Date
Start Page
227
End Page
241
DOI
10.1016/j.ijrobp.2020.05.011

Clinical Experience With Machine Learning-Based Automated Treatment Planning for Whole Breast Radiation Therapy.

Purpose: The machine learning-based automated treatment planning (MLAP) tool has been developed and evaluated for breast radiation therapy planning at our institution. We implemented MLAP for patient treatment and assessed our clinical experience for its performance. Methods and Materials: A total of 102 patients of breast or chest wall treatment plans were prospectively evaluated with institutional review board approval. A human planner executed MLAP to create an auto-plan via automation of fluence maps generation. If judged necessary, a planner further fine-tuned the fluence maps to reach a final plan. Planners recorded the time required for auto-planning and manual modification. Target (ie, breast or chest wall and nodes) coverage and dose homogeneity were compared between the auto-plan and final plan. Results: Cases without nodes (n = 71) showed negligible (<1%) differences for target coverage and dose homogeneity between the auto-plan and final plan. Cases with nodes (n = 31) also showed negligible difference for target coverage. However, mean ± standard deviation of volume receiving 105% of the prescribed dose and maximum dose were reduced from 43.0% ± 26.3% to 39.4% ± 23.7% and 119.7% ± 9.5% to 114.4% ± 8.8% from auto-plan to final plan, respectively, all with P ≤ .01 for cases with nodes (n = 31). Mean ± standard deviation time spent for auto-plans and additional fluence modification for final plans were 12.1 ± 9.3 and 13.1 ± 12.9 minutes, respectively, for cases without nodes, and 16.4 ± 9.7 and 26.4 ± 16.4 minutes, respectively, for cases with nodes. Conclusions: The MLAP tool has been successfully implemented for routine clinical practice and has significantly improved planning efficiency. Clinical experience indicates that auto-plans are sufficient for target coverage, but improvement is warranted to reduce high dose volume for cases with nodal irradiation. This study demonstrates the clinical implementation of auto-planning for patient treatment and the significant importance of integrating human experience and feedback to improve MLAP for better clinical translation.
Authors
Yoo, S; Sheng, Y; Blitzblau, R; McDuff, S; Champ, C; Morrison, J; O'Neill, L; Catalano, S; Yin, F-F; Wu, QJ
MLA Citation
Yoo, Sua, et al. “Clinical Experience With Machine Learning-Based Automated Treatment Planning for Whole Breast Radiation Therapy.Adv Radiat Oncol, vol. 6, no. 2, Mar. 2021, p. 100656. Pubmed, doi:10.1016/j.adro.2021.100656.
URI
https://scholars.duke.edu/individual/pub1476592
PMID
33748540
Source
pubmed
Published In
Advances in Radiation Oncology
Volume
6
Published Date
Start Page
100656
DOI
10.1016/j.adro.2021.100656

Implementation of Machine Learning-Based Treatment Planning Tool for Whole Breast Radiotherapy Using Irregular Surface Compensator Technique

Authors
Yoo, S; Sheng, Y; Blitzblau, RC; Suneja, G; O'Neill, L; Morrison, J; Catalano, S; Yin, FF; Wu, QJJ
MLA Citation
Yoo, S., et al. “Implementation of Machine Learning-Based Treatment Planning Tool for Whole Breast Radiotherapy Using Irregular Surface Compensator Technique.” International Journal of Radiation Oncology*Biology*Physics, vol. 105, no. 1, Elsevier BV, 2019, pp. S94–S94. Crossref, doi:10.1016/j.ijrobp.2019.06.572.
URI
https://scholars.duke.edu/individual/pub1415077
Source
crossref
Published In
International Journal of Radiation Oncology, Biology, Physics
Volume
105
Published Date
Start Page
S94
End Page
S94
DOI
10.1016/j.ijrobp.2019.06.572

Erratum to: Blitzblau RC, Horton JK. First, Do No Harm. Int J Radiat Oncol Biol Phys 2019;103:295-296.

MLA Citation
Erratum to: Blitzblau RC, Horton JK. First, Do No Harm. Int J Radiat Oncol Biol Phys 2019;103:295-296.International Journal of Radiation Oncology*Biology*Physics, vol. 104, no. 3, Elsevier BV, July 2019, pp. 701–701. Crossref, doi:10.1016/j.ijrobp.2019.03.033.
URI
https://scholars.duke.edu/individual/pub1393799
Source
crossref
Published In
International Journal of Radiation Oncology, Biology, Physics
Volume
104
Published Date
Start Page
701
End Page
701
DOI
10.1016/j.ijrobp.2019.03.033

Image-Guided Radiation Therapy (IGRT) for Preoperative Partial Breast Radiosurgery: A Single-Institution Experience

Authors
Yoo, S; Blitzblau, RC; Yin, FF; Horton, JK
MLA Citation
Yoo, S., et al. “Image-Guided Radiation Therapy (IGRT) for Preoperative Partial Breast Radiosurgery: A Single-Institution Experience.” International Journal of Radiation Oncology*Biology*Physics, vol. 102, no. 3, Elsevier BV, 2018, pp. e523–24. Crossref, doi:10.1016/j.ijrobp.2018.07.1474.
URI
https://scholars.duke.edu/individual/pub1358468
Source
crossref
Published In
International Journal of Radiation Oncology, Biology, Physics
Volume
102
Published Date
Start Page
e523
End Page
e524
DOI
10.1016/j.ijrobp.2018.07.1474