Head: Thomas Bortfeld, PhD

Our overarching goal is to optimize cancer treatment by addressing some of the key challenges in Radiation Oncology. We aim to make a direct clinical and societal impact with our work. As a group of physicists and mathematicians we are not tied to the use of a particular method, but we identify and apply the right methods to address clinical challenges. We collaborate closely with the world’s leading experts in mathematical optimization, analytics and robotics.

Our current work focusses on three primary areas:

• Defining the clinical tumor target volume (CTV). With today’s high precision of treatment delivery thanks to advanced treatment techniques and image guidance, the definition of the CTV including its invisible microscopic extensions is becoming the weakest link of the radiotherapy chain. We are addressing this problem through auto segmentation of anatomic barrier structures, modeling the spread of the disease, and implementation of consensus guidelines.
  
  
• Optimized personalized treatment delivery. While there has been a long history of optimized individualized shaping of radiation dose distributions in radiation oncology, the individualization of the dose level has long been neglected. We are working on the mathematical aspects of identifying the right dose level and the right type of treatment for each patient, while respecting modeling and data uncertainty. The current focus is on the dynamic uncertainty-aware response assessment during the treatment course and optimal stopping or optimal switching to other treatment modalities.
  
  
• Democratizing proton therapy. Even though more than 15% of all radiotherapy patients are expected to benefit from the physical advantages of proton therapy, less than 1% actually receive this more expensive form of treatment. We are working on the science and engineering challenges to shrink its size and the cost, with the ultimate goal of making proton therapy fit into conventional treatment rooms at a cost similar to conventional treatments with linear accelerators.

We gratefully acknowledge support from RaySearch AB, Koninklijke Philips N.V. (Diagnosis and Treatment Division), the MGH Therapy Imaging Program (TIP), the Marie Skłodowska-Curie Actions of the European Commission, and the Deutsche Forschungsgemeinschaft DFG (German Research Foundation).

Lab Head: Clemens Grassberger, PhD

The majority of patients treated with radiation therapy also receive other treatments, ranging from surgery and chemotherapy to recent additions like targeted agents and immunotherapeutic approaches. In the Radiation-Drug Treatment Design Lab we develop and apply methodologies to describe the interaction of these systemic therapies with radiation in the context of specific clinical indications, with the aim to design rational clinical trials and patient-specific treatments.

Research Areas

• Impact of radiotherapy on lymphocytes & patient immunity
• How to integrate radiotherapy with checkpoint inhibitors
• Resistance development to targeted agents (EGFR inhibitors) & role of radiotherapy
• Mechanistic and data-driven modeling to predict treatment outcome

Recent News

• Wonmo Sung's award-winning (best translational research, ASTRO 2019) now published in Radiotherapy & Oncology: https://authors.elsevier.com/a/1bVcBcA0-4Oqx
• Abdelkhalek Hammi paper on calculating dose to the circulating blood during brain radiation now out in PMB; featured in best abstracts at ICCR 2019 & AAPM 2019 -->  https://iopscience.iop.org/article/10.1088/1361-6560/ab6c41/meta

Funding

• 2 NIH/NCI R21s
• CHEST Foundation
• MGH ECOR Funding

Project Opportunities: to find out about opportunities in our group, have a look at our list of open projects --> here

Go to main Lab page

Proton beams have a finite range and allow radiation treatments to be delivered with less dose to the healthy tissue surrounding the tumor. There is however the potential to take even more benefit from the physical advantage of proton beams, by placing the sharp distal dose gradient exactly on the interface between the target volume and nearby healthy organs. This is currently not feasible because of the uncertainty in the range of the beams in the patient.

The Proton Dosimetry Verification Lab develops new technology to determine precisely what the actual dose distribution is that is delivered by proton beams to the patient. We have developed the prompt gamma-ray spectroscopy method for determining the range of delivered proton pencil-beams and are currently conducting the first clinical studies. In the future, we plan to fine-tune proton treatments such that we can use the full advantage of protons and with minimal difference between the treatment plan and the delivered dose.

Lab head: Joost Verburg, Ph.D.

Clinical outcome of radiotherapy can potentially be improved by increasing the precision of tumor localization and dose delivery during the treatment. To achieve this goal, various techniques related to image guidance, dynamic targeting and adaptive therapy are being developed.

Although radiation therapy (RT) is one of the main curative modalities in cancer treatment, unfortunately in some patients it is not effective in curbing cancer progression. This is mainly due to the differences in tumor's and patient's overall biological makeup which makes the tumor cells extremely radioresistant. In such cases, continuation of the treatment course might not only be harmful for patients (due to the radiation-induced damage to healthy tissues), but, due to the prolonged nature of most RT treatment courses, might prevent from undertaking a more effective treatment option. Therefore, it is highly desirable to identify those patients for whom RT might not be the best course of action, and stop the treatment immediately. This problem can be formulated as a special case of Optimal Stopping problem. We have termed this Optimal Stopping in Radiation Therapy (OSRT) [1].

OSRT can be seen as a special case of the broader personalized medicine, in which the treatment course is adapted based on individual's biological characteristics. Owing to the recent advancements in functional imaging and micro-array analysis, more patient-specific data is available before, during, and after the treatment. The crux of the problem is how to integrate this extremely complex, heterogeneous, and noisy data into a panoptic view of the patient's response landscape. The idea is to view the patient response to RT as a stochastic problem whose states (i.e., tumor progression, normal tissue damage) stochastically evolve during and after the course of the treatment, while only partial information can be reliably obtained about these states in the form of various biological markers (biomarkers). Tackling this problem requires a multi-faceted and inter-disciplinary approach and involves borrowing techniques from or devising novel ones based in mathematics, optimization, and machine learning. 

OSRT consists of three interconnected and complimentary steps: (1) Predictive Biomarker Discovery, (2) Interpretable Predictive Modeling, and (3) Adaptive Treatment Personalization. We are actively working to develop effective tools in all three fronts to realize the full potential of OSRT. We heavily rely on advanced methods rooted in Statistics, Machine Learning, and Optimization to perform these steps. In the first one (Biomarker Discovery), we mainly use image processing and advanced statistical and data analytics tools to find novel or re-purpose existing biomarkers of RT response. In the second, state-of-the-art machine learning tools are employed to derive interpretable predictive models of RT response. Finally, the third step involves various forms of optimization methods to address model uncertainty and adapt the treatment plans according to biomarker information and model's predictions. You can find more information about each of these steps in their dedicated sections below.

 [1] A. Ajdari, M. Niyazi, N. Nicolay, C. Thieke, R. Jeraj, T. Bortfeld (2019), Towards optimal stopping in radiation therapy, Radiotherapy and Oncology, 137: 96-100.