Project Title: Knowledge-based Radiation Treatment Planning for Glioblastoma Multiforme
Project Title: Nanoparticle-aided radiation therapy with scintillating high-Z materials
Project Title: To be added .
Project Title: Development of a probe-format calorimeter for absolute clinical dosimetry of high-energy photon, electron, and proton beams
Project Title: Integrative analysis of oncological data via a distributed learning framework to support clinical decision-making in cancer care .
The project involves creation of biocompatible rare earth-based nanoparticles and testing them for activity against cancer in vitro and in vivo, with or without radiation therapy.
We will develop a new kind of particle for radiosensitization of tumours by direct intratumoral or possibly intravenous injection and test them in vitro and in vivo. The particles, cerium-doped lanthanum fluoride (LaF3:Ce), are made of low-toxicity constituents and have many desirable photophysical properties compared to currently existing radiosensitizers in the field. They can radiosensitize tumours on their own via the photoelectric effect, or they may be conjugated to a photosensitizer for X-ray based singlet oxygen generation.
Additionally, software modelling and simulation of nanoparticle localization and radiation response will help corroborate the mechanisms by which the nanoparticles induce different varieties of “insults” and affect cell survival, as well as quantify dose enhancement at the nanoparticle level.
1. D.R. Cooper, D. Bekah, J.L. Nadeau, (2014) Gold Nanoparticles and Their Alternatives for Radiation Therapy Enhancement, Frontiers in Chemistry 2, Article 86, 13 pages October 2014, doi: 10.3389/fchem.2014.00086.
2. Kudinov, K, Cooper D., Tyagi P., Bekah D., Bhattacharyya D., Hill C., Kin Ha J., Nadeau J.L., Bradforth S.G. (2015), Evidence of energy transfer in nanoparticle-porphyrins conjugates for radiation therapy enhancement. Proc. SPIE 9338 Colloidal Nanoparticles for Biomedical Applications X 93380H (March 12, 2015); doi:10.1117/12.2077985.
3. Devesh Bekah, Daniel Cooper, Konstantin Kudinov, Colin Hill, Jan Seuntjens, Stephen Bradforth and Jay Nadeau (2016) Synthesis and Characterization of Biologically Stable, Doped LaF3 Nanoparticles Co-Conjugated to PEG and Photosensitizers, Journal of Photochemistry and Photobiology A: Chemistry Volume 329, 1 October 2016, Pages 26–34.
4. Cooper DR , Capobianco JA , Seuntjens J (2018) Radioluminescence studies of colloidal oleate-capped β-Na(Gd,Lu)F4:Ln3+ nanoparticles (Ln = Ce, Eu, Tb), Nanoscale 2018 Apr 26;10(16):7821-7832. doi: 10.1039/c8nr01262h.
5. Kudinov KA, Cooper DR, Ha JK, Hill CK, Nadeau JL, Seuntjens JP, Bradforth SE (2018) Scintillation Yield Estimates of Colloidal Cerium-Doped LaF3 Nanoparticles and Potential for “Deep PDT”, Radiat Res. 2018 Apr 19. doi: 10.1667/RR14944.1. [Epub ahead of print].
Purpose: To create a knowledge-based radiation treatment planning model for brain tumor patients, specifically Glioblastoma multiforme (GBM), using RapidPlan, a commercially available product. Methods and Materials: The model predicts achievable dose sparing for organs at risk (OARs) using a library of 82 inverse treatment plans, and provides optimization objectives to be used for treatment planning. The model is then validated on an independent set consisting of 45 patients. Results: The plans created by the model are clinically acceptable, having slightly improved planning target volume (PTV) coverage (ΔD98% = 0.5 Gy) and slightly lower mean doses to the optic apparatus (1.5 Gy) and eye (0.6 Gy). The planning time can be as low as 7 minutes, compared to about two hours for planning without RapidPlan. Conclusions: Knowledge-based planning model for GBM delivers quick, high-quality plans for a diverse set of patients.
1. André Diamant, Avishek Chatterjee, Sergio Faria, Issam El Naqa, Houda Bahig, Edith Filion, Cliff Robinson, Hani Al-Halabi, Jan Seuntjens (2018) Can dose outside the PTV influence the risk of distant metastases in stage I lung cancer patients treated with stereotactic body radiotherapy (SBRT)?, Radiotherapy and Oncology, Available online 2018 May 22. doi.org/10.1016/j.radonc.2018.05.012. [Epub ahead of print].
2. Avishek Chatterjee, PhD, Monica Serban, MSc, Bassam Abdulkarim, MD, PhD, Valerie Panet-Raymond, MD, Luis Souhami, MD, FASTRO, George Shenouda, MBBCh, PhD, FRCP (C), Siham Sabri, PhD, Bertrand Jean-Claude, PhD, Jan Seuntjens, PhD (2017) Performance of Knowledge-Based Radiation Therapy Planning for the Glioblastoma Disease Site, International Journal of Radiation Oncology • Biology • Physics 99(4): 1021-1028.
Our work involves the development of an innovative probe-format calorimeter (Arrow) for clinical dosimetry. Calorimetry-based detectors are unique in that no radiation is required for calibration and thus offer the most absolute method to measure dose. Calorimeters have been used by standards laboratories for decades, however these calorimeters are too bulky and technically cumbersome for routine clinical use. The enhancement of our unique GPC design will be more compact and simple to operate than prior art. With a number of prototypes and experimental studies suggesting the feasibility of the technology, and the backing of an industry partner, the objective is to further refine and validate this cutting edge device for end-user testing and eventual commercialization .
1) Renaud J, Sarfehnia A, Marchant K, McEwen M R, Ross C, Seuntjens J P (2015) Direct measurement of electron beam quality conversion factors using water calorimetry, Med. Phys. 42(11):6357-6368.
2) James Renaud, A. Sarfehnia, J. Seuntjens (2016) Experimental benchmarking of a probe-format calorimeter for use as an absolute clinical dosimeter, ESTRO 35 Conference 2016, April 29 to May 3, 2016, Turin, Italy. One of the five abstracts that was selected out of the numerous submitted under physics track – publication of the conference report (conference paper).
3) Renaud J, Rossomme S, Sarfehnia A, Vynckier S, Palmans H, Kacperek A, Seuntjens J (2016) Development and application of a water calorimeter for the absolute dosimetry of short-range particle beams, Phys Med Biol. 2016 Sep 21;61(18):6602-6619.
4) James Renaud, Arman Sarfehnia, Julien Bancheri, Jan Seuntjens (2017) Aerrow: A probe-format graphite calorimeter for absolute dosimetry of high-energy photon beams in the clinical environment, Med Phys 45(1):414-428, January 2018; DOI: 10.1002/mp.12669. 0094-2405/2018/45(1)/414/15.
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Each patient with cancer is unique owing to intratumoural heterogeneity. Precision oncology promises to tailor the full spectrum of cancer care to an individual patient, notably in terms of personalization of risk stratification, therapy and response assessment. Nowadays, medical imaging plays a central role in the investigation of intratumoural heterogeneity, and we can in fact envision the quantitative analysis of diagnostic medical images as an essential prognostic tool for cancer risk assessment (i.e. characterization of tumour aggressiveness) within the precision oncology paradigm. Unfortunately, at the moment, quantitative image-based analyses in oncology suffers from one major bottleneck: imaging data collection, curation, anonymization and sharing among worldwide cancer centers.
In order to improve the long-term efficiency in utilizing data from many different cancer institutions, a distributed learning framework can be used. In this approach, data is always kept within the confines of each health care or research institution. Only derived results/models of a given institution is shared with other partner institutions, thereby accelerating the construction of worldwide models aiming at better assessing cancer risks.
In this work, we first propose to develop novel image analysis methods based on deep learning (a sub-branch of artificial intelligence) to construct risk assessment models of local recurrences in brain meningioma cancer. Secondly, we propose to establish a distributed learning network between three international research institutions: one in Canada, one in USA and one in the Netherlands. Ultimately, the image analysis methods developed in this project could improve the personalization of cancer treatments, and the distributed learning network could be extended to multiple institutions in Canada and in other parts of the world to accelerate imaging research in oncology.
1) Martin Vallières†, Monica Serban, Ibtissam Benzyane, Zaki Ahmed, Shu Xing, Issam El Naqa, Ives R. Levesque, Jan Seuntjens, Carolyn R. Freeman (2018) Investigating the role of functional imaging in the management of soft-tissue sarcomas of the extremities, Physics and Imaging in Radiation Oncology 6: 53-60.
2) Martin Vallières†, Emily Kay-Rivest, L´eo Jean Perrin, Xavier Liem, Christophe, Furstoss, Hugo J. W. L. Aerts, Nader Khaouam, Phuc Felix Nguyen-Tan, Chang-Shu Wang, Khalil Sultanem, Jan Seuntjens & Issam El Naqa (2017) Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer, Sci Rep 7(1):10117. Epub 2017 Aug 31.
3) Martin Vallières, Sébastien Laberge, André Diamant and Issam El Naqa (2017) Enhancement of multimodality texture- based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept, Phys. Med. Biol. 62: 8536–8565.