"Quaere et invenies"



The multi-scale Monte Carlo lab aims to connect macroscopic simulations of patient dose distributions with simulation at the sub-cellular scale, connecting physics to biology. The research efforts use Monte Carlo simulations to study ionizing beams and their effects on human tissues. The overall goal is, to develop a comprehensive model that predicts macroscopic outcome from microscopic Monte Carlo simulations. To achieve this goal, we use a two-pronged approach. 

Macroscopic scale: We use Monte Carlo simulations to better understand uncertainties in patient treatment deliveries, and investigate how treatments can be improved using additional information available in the Monte Carlo method, such as the linear energy deposit (LET) and the relative biological effectiveness (RBE). The TOPAS project (TOol for PArticle Simulations, TOPAS websiteDocumentation) developed a Monte Carlo system in collaboration with UCSF and the SLAC National Accelerator Laboratory that is widely used in (proton) radiation therapy. 

Microscopic scale: We use nanometer-scale simulations of the initial radiation insult to biological structures, such as the DNA, mitochondria or other sub-cellular components to investigate the dependence of biological outcome on the radiation type, the presence of (gold) nanoparticles, and other biological properties of the irradiated tissue. Our goal is to provide an extension to the TOPAS platform, called TOPAS-nBio, to model radiobiological experiments.

By approaching biology from two sides, we aim to close the gap between the macroscopic and microscopic scales. At the nanometer scale, we work on including chemical reactions, biological structures and mechanistic modeling of repair kinetics. At the macroscopic scale, we try to capture the underlying biology by modeling outcome based on additional information, such as the LET and the microscopic equivalent, the lineal energy transfer, y.

Lab Head: Jan Schuemann, Ph.D.


Lab Research topics:

We work on treatment plan optimization to maximize the benefit of the treatment for the patient. Current commercial “inverse” planning systems do not come up with plans that are clinically or mathematically optimal. Our research addresses three critical questions:

  • How to deal with the multiple conflicting objectives and inevitable tradeoffs that we face even in advanced IMRT or proton treatments
  • How to account for (known) motion of the patient or internal organs in the treatment planning and optimization process (“4D” optimization)
  • How to make the treatment plan insensitive against various sources of uncertainty (motion related and otherwise)

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.

We work mainly on combinations of radiation with:

  • Immunotherapy
  • Targeted Agents
  • Chemotherapy

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

Lab Head: Clemens Grassberger, PhD

Lab Research Topics:

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.