Associate Professor of Radiation Oncology
Brigham and Women's Hospital
Brigham and Womens Hospital
Radiation Oncology, ASB1 - L2
75 Francis St
Boston MA 02115
Available: 10/11/18, Expires: 10/10/21
My main research interests are to develop clinically-applicable biomarkers of both tumor response and radiation-induced toxicity after radiation therapy in patients with non-small cell lung cancer, using both novel imaging technologies and genetic analyses. Combining these genetic and imaging approaches will allow for an integrated “personalized” radiation therapy approach with a goal of widening the therapeutic window: both increasing the probability of tumor control while reducing the risk of radiation-induced injury.
Ongoing clinical-translational research work includes developing clinical databases with paired tissue and imaging repositories. Ongoing work includes analyzing clinical outcomes in non-small cell lung carcinoma (NSCLC) by tumor genotype and known mutations in tumor oncogenes such as EGFR and KRAS (Mak et al. Oncologist. 2011;16(6):886-95. PMCID:PMC3228219; Mak et al. Clinical Lung Cancer. 2015;16(1):24-32. PMCID: PMC4427190), and collaborations with Dr. Hugo Aerts to apply artificial intelligence and radiomics (quantitative analyses of clinically-acquired images of tumors) techniques to understand tumor phenotype in relation to tumor genotype, improve radiation therapy techniques, and outcomes in patients with non-small cell lung carcinoma (NSCLC). The critical component of this collaboration has been the development of a clinical database of over 1000 patients with NSCLC, paired tumor tissue repository, and a paired radiological imaging repository with manually segmented tumor volumes in over 600 patients, which we have assembled in the past 3 years.
Both scholarly projects and part-time projects available
Available: 05/03/19, Expires: 05/05/22
techniques to predict outcomes after radiation therapy for a variety of cancers. Recent work involves developing AI algorithms to replicate the skills of human experts in tumor targeting and RT planning.
Recent publications below:
Available: 05/05/19, Expires: 05/31/21
Patient safety and quality improvement (QI) has become an increasingly important part of radiation oncology due to the increasingly complexity of radiation therapy delivery and technology. As a radiation oncology department delivering some of the newest, most complex, and technology-intensive treatment techniques, we have become increasingly concerned engineering work flows and processes that promote safe and high quality delivery of radiation therapy.
We have developed a customized safety reporting system that maps to our complex work flows and QI procedures, which allows frontline staff to report safety events and our QI team to perform quantitative analyses of safety events, and to conduct research into ways of improving radiation therapy safety. Projects are available on an ongoing basis, and can be tailored to varying student time commitments.
Available: 07/01/19, Expires: 07/01/24
Work with a group of data scientists, radiation oncologists and physicists to develop AI applications to automate radiation therapy planning. Student roles could include more technical work (coding) or more clinically oriented projects such as gathering and curating clinical data and imaging, designing and helping to run clinical trials to test AI applications. Time commitments are flexible.
Available: 05/21/14, Expires: 06/30/21
Ongoing clinical research projects involve evaluating genetic predictors of tumor response to radiation (e.g. tumor mutations in EGFR and KRAS genes) and germline single nucleotide polymorphisms that predict risk of radiation toxicity. Additional projects incorporate functional imaging (e.g. MRI perfusion scans) to identify radiation-induced injury and tumor response. Specific sub-projects in the frame work of these studies would be identified in discussion with interested students and could entail either more short term or more long term studies.
Genetic and Clinical Predictors of Radiation Esophagitis in Lung Cancer Treatment
Summer, 06/17/13 - 08/16/13
Evaluation of CT Imaging Features of Non-Small Cell Lung Cancer During Chemoradiation
Full Time/Year Long, 08/01/14 - 08/15/15
Use of Frailty to Predict Survival in Elderly Patients with Early Stage Non-Small-Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy
Full Time/Year Long, 09/01/15 - 09/30/16
Local representatives can answer questions about the Profiles website or help with editing a profile or issues with profile data. For assistance with this profile: HMS/HSDM faculty should contact feedbackcatalyst.harvard.edu. For faculty or fellow appointment updates and changes, please ask your appointing department to contact HMS. For fellow personal and demographic information, contact HMS Human Resources at human_resourceshms.harvard.edu. For faculty personal and demographic information, contact HMS Office for Faculty Affairs at facappthms.harvard.edu.