Techna Institute for the Advancement of Technology for Health

Toronto, Canada

Techna Institute for the Advancement of Technology for Health

Toronto, Canada
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Welch M.L.,University of Toronto | Jaffray D.A.,University of Toronto | Jaffray D.A.,Techna Institute for the Advancement of Technology for Health
Physics in Medicine and Biology | Year: 2017

Previously developed MR-based three-dimensional (3D) Fricke-xylenol orange (FXG) dosimeters can provide end-to-end quality assurance and validation protocols for pre-clinical radiation platforms. FXG dosimeters quantify ionizing irradiation induced oxidation of Fe2+ ions using pre- and post-irradiation MR imaging methods that detect changes in spin-lattice relaxation rates (R 1 = ) caused by irradiation induced oxidation of Fe2+. Chemical changes in MR-based FXG dosimeters that occur over time and with changes in temperature can decrease dosimetric accuracy if they are not properly characterized and corrected. This paper describes the characterization, development and utilization of an empirical model-based correction algorithm for time and temperature effects in the context of a pre-clinical irradiator and a 7 T pre-clinical MR imaging system. Time and temperature dependent changes of R 1 values were characterized using variable TR spin-echo imaging. R 1-time and R 1-temperature dependencies were fit using non-linear least squares fitting methods. Models were validated using leave-one-out cross-validation and resampling. Subsequently, a correction algorithm was developed that employed the previously fit empirical models to predict and reduce baseline R 1 shifts that occurred in the presence of time and temperature changes. The correction algorithm was tested on R 1-dose response curves and 3D dose distributions delivered using a small animal irradiator at 225 kVp. The correction algorithm reduced baseline R 1 shifts from -2.8 10-2 s-1 to 1.5 10-3 s-1. In terms of absolute dosimetric performance as assessed with traceable standards, the correction algorithm reduced dose discrepancies from approximately 3% to approximately 0.5% (2.90 2.08% to 0.20 0.07%, and 2.68 1.84% to 0.46 0.37% for the 10 10 and 8 12 mm2 fields, respectively). Chemical changes in MR-based FXG dosimeters produce time and temperature dependent R 1 values for the time intervals and temperature changes found in a typical small animal imaging and irradiation laboratory setting. These changes cause baseline R 1 shifts that negatively affect dosimeter accuracy. Characterization, modeling and correction of these effects improved in-field reported dose accuracy to less than 1% when compared to standardized ion chamber measurements. © 2017 Institute of Physics and Engineering in Medicine.

Wong S.W.H.,York University | Wong S.W.H.,TECHNA Institute for the Advancement of Technology for Health | Cercone N.,York University | Jurisica I.,TECHNA Institute for the Advancement of Technology for Health | Jurisica I.,University of Toronto
Proteomics | Year: 2015

While current protein interaction data provides a rich resource for molecular biology, it mostly lacks condition-specific details. Abundance of mRNA data for most diseases provides potential to model condition-specific transcriptional changes. Transcriptional data enables modeling disease mechanisms, and in turn provide potential treatments. While approaches to compare networks constructed from healthy and disease samples have been developed, they do not provide the complete comparison, evaluations are performed on very small networks, or no systematic network analyses are performed on differential network structures. We propose a novel method for efficiently exploiting network structure information in the comparison between any graphs, and validate results in non-small cell lung cancer. We introduce the notion of differential graphlet community to detect deregulated subgraphs between any graphs such that the network structure information is exploited. The differential graphlet community approach systematically captures network structure differences between any graphs. Instead of using connectivity of each protein or each edge, we used shortest path distributions on differential graphlet communities in order to exploit network structure information on identified deregulated subgraphs. We validated the method by analyzing three non-small cell lung cancer datasets and validated results on four independent datasets. We observed that the shortest path lengths are significantly longer for normal graphs than for tumor graphs between genes that are in differential graphlet communities, suggesting that tumor cells create "shortcuts" between biological processes that may not be present in normal conditions. © 2014 The Authors. PROTEOMICS Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Lim K.,Liverpool Hospital | Stewart J.,University of Toronto | Kelly V.,University of Toronto | Xie J.,University of Toronto | And 10 more authors.
International Journal of Radiation Oncology Biology Physics | Year: 2014

Purpose The widespread use of intensity modulated radiation therapy (IMRT) for cervical cancer has been limited by internal target and normal tissue motion. Such motion increases the risk of underdosing the target, especially as planning margins are reduced in an effort to reduce toxicity. This study explored 2 adaptive strategies to mitigate this risk and proposes a new, automated method that minimizes replanning workload. Methods and Materials Thirty patients with cervical cancer participated in a prospective clinical study and underwent pretreatment and weekly magnetic resonance (MR) scans over a 5-week course of daily external beam radiation therapy. Target volumes and organs at risk (OARs) were contoured on each of the scans. Deformable image registration was used to model the accumulated dose (the real dose delivered to the target and OARs) for 2 adaptive replanning scenarios that assumed a very small PTV margin of only 3 mm to account for setup and internal interfractional motion: (1) a preprogrammed, anatomy-driven midtreatment replan (A-IMRT); and (2) a dosimetry-triggered replan driven by target dose accumulation over time (D-IMRT). Results Across all 30 patients, clinically relevant target dose thresholds failed for 8 patients (27%) if 3-mm margins were used without replanning. A-IMRT failed in only 3 patients and also yielded an additional small reduction in OAR doses at the cost of 30 replans. D-IMRT assured adequate target coverage in all patients, with only 23 replans in 16 patients. Conclusions A novel, dosimetry-triggered adaptive IMRT strategy for patients with cervical cancer can minimize the risk of target underdosing in the setting of very small margins and substantial interfractional motion while minimizing programmatic workload and cost. © 2014 Elsevier Inc. All rights reserved.

Pastrello C.,Instituto Nazionale Tumori | Pivetta F.,Instituto Nazionale Tumori | Lo Sardo A.,Instituto Nazionale Tumori | Li H.,Mount Sinai Hospital | And 11 more authors.
Nature Methods | Year: 2014

Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only a 1/410% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining-based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions ( and the prediction software (∼juris/data/fpclass/).

Lee T.,King's College | Hammad M.,King's College | Hammad M.,Techna Institute for the Advancement of Technology for Health | Chan T.C.Y.,King's College | And 4 more authors.
Medical Physics | Year: 2013

Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. A regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, using l2 distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. Conclusions: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore. © 2013 American Association of Physicists in Medicine.

Boutilier J.J.,King's College | Craig T.,University of Toronto | Sharpe M.B.,University of Toronto | Sharpe M.B.,Techna Institute for the Advancement of Technology for Health | And 2 more authors.
Medical Physics | Year: 2016

Purpose: To determine how training set size affects the accuracy of knowledge-based treatment planning (KBP) models. Methods: The authors selected four models from three classes of KBP approaches, corresponding to three distinct quantities that KBP models may predict: dose-volume histogram (DVH) points, DVH curves, and objective function weights. DVH point prediction is done using the best plan from a database of similar clinical plans; DVH curve prediction employs principal component analysis and multiple linear regression; and objective function weights uses either logistic regression or K-nearest neighbors. The authors trained each KBP model using training sets of sizes n = 10, 20, 30, 50, 75, 100, 150, and 200. The authors set aside 100 randomly selected patients from their cohort of 315 prostate cancer patients from Princess Margaret Cancer Center to serve as a validation set for all experiments. For each value of n, the authors randomly selected 100 different training sets with replacement from the remaining 215 patients. Each of the 100 training sets was used to train a model for each value of n and for each KBT approach. To evaluate the models, the authors predicted the KBP endpoints for each of the 100 patients in the validation set. To estimate the minimum required sample size, the authors used statistical testing to determine if the median error for each sample size from 10 to 150 is equal to the median error for the maximum sample size of 200. Results: The minimum required sample size was different for each model. The DVH point prediction method predicts two dose metrics for the bladder and two for the rectum. The authors found that more than 200 samples were required to achieve consistent model predictions for all four metrics. For DVH curve prediction, the authors found that at least 75 samples were needed to accurately predict the bladder DVH, while only 20 samples were needed to predict the rectum DVH. Finally, for objective function weight prediction, at least 10 samples were needed to train the logistic regression model, while at least 150 samples were required to train the K-nearest neighbor methodology. Conclusions: In conclusion, the minimum required sample size needed to accurately train KBP models for prostate cancer depends on the specific model and endpoint to be predicted. The authors' results may provide a lower bound for more complicated tumor sites. © 2016 American Association of Physicists in Medicine.

Mahmoudzadeh H.,University of Toronto | Lee J.,Princess Margaret Cancer Center | Chan T.C.Y.,University of Toronto | Chan T.C.Y.,Techna Institute for the Advancement of Technology for Health | And 2 more authors.
Medical Physics | Year: 2015

Purpose: In left-sided tangential breast intensity modulated radiation therapy (IMRT), the heart may enter the radiation field and receive excessive radiation while the patient is breathing. The patients breathing pattern is often irregular and unpredictable. We verify the clinical applicability of a heart-sparing robust optimization approach for breast IMRT.We compare robust optimized plans with clinical plans at free-breathing and clinical plans at deep inspiration breath-hold (DIBH) using active breathing control (ABC). Methods: Eight patients were included in the study with each patient simulated using 4D-CT. The 4D-CT image acquisition generated ten breathing phase datasets. An average scan was constructed using all the phase datasets. Two of the eight patients were also imaged at breath-hold using ABC. The 4D-CT datasets were used to calculate the accumulated dose for robust optimized and clinical plans based on deformable registration. We generated a set of simulated breathing probability mass functions, which represent the fraction of time patients spend in different breathing phases. The robust optimization method was applied to each patient using a set of dose-influence matrices extracted from the 4D-CT data and a model of the breathing motion uncertainty. The goal of the optimization models was to minimize the dose to the heart while ensuring dose constraints on the target were achieved under breathing motion uncertainty. Results: Robust optimized plans were improved or equivalent to the clinical plans in terms of heart sparing for all patients studied. The robust method reduced the accumulated heart dose (D10cc) by up to 801 cGy compared to the clinical method while also improving the coverage of the accumulated whole breast target volume. On average, the robust method reduced the heart dose (D10cc) by 364 cGy and improved the optBreast dose (D99%) by 477 cGy. In addition, the robust method had smaller deviations from the planned dose to the accumulated dose. The deviation of the accumulated dose from the planned dose for the optBreast (D99%) was 12 cGy for robust versus 445 cGy for clinical. The deviation for the heart (D10cc) was 41 cGy for robust and 320 cGy for clinical. Conclusions: The robust optimization approach can reduce heart dose compared to the clinical method at free-breathing and can potentially reduce the need for breath-hold techniques. © 2015 American Association of Physicists in Medicine.

Stewart J.M.P.,University of Toronto | Ansell S.,University of Toronto | Lindsay P.E.,University of Toronto | Jaffray D.A.,University of Toronto | Jaffray D.A.,Techna Institute for the Advancement of Technology for Health
Physics in Medicine and Biology | Year: 2015

Advances in precision microirradiators for small animal radiation oncology studies have provided the framework for novel translational radiobiological studies. Such systems target radiation fields at the scale required for small animal investigations, typically through a combination of on-board computed tomography image guidance and fixed, interchangeable collimators. Robust targeting accuracy of these radiation fields remains challenging, particularly at the millimetre scale field sizes achievable by the majority of microirradiators. Consistent and reproducible targeting accuracy is further hindered as collimators are removed and inserted during a typical experimental workflow. This investigation quantified this targeting uncertainty and developed an online method based on a virtual treatment isocenter to actively ensure high performance targeting accuracy for all radiation field sizes. The results indicated that the two-dimensional field placement uncertainty was as high as 1.16 mm at isocenter, with simulations suggesting this error could be reduced to 0.20 mm using the online correction method. End-to-end targeting analysis of a ball bearing target on radiochromic film sections showed an improved targeting accuracy with the three-dimensional vector targeting error across six different collimators reduced from 0.56 ± 0.05 mm (mean ± SD) to 0.05 ± 0.05 mm for an isotropic imaging voxel size of 0.1 mm. © 2015 Institute of Physics and Engineering in Medicine.

Boutilier J.J.,King's College | Lee T.,King's College | Craig T.,University of Toronto | Sharpe M.B.,University of Toronto | And 3 more authors.
Medical Physics | Year: 2015

Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR weight prediction methodologies perform comparably to the LR model and can produce clinical quality treatment plans by simultaneously predicting multiple weights that capture trade-offs associated with sparing multiple OARs. © 2015 American Association of Physicists in Medicine.

Stewart J.M.P.,University of Toronto | Lindsay P.E.,University of Toronto | Jaffray D.A.,University of Toronto | Jaffray D.A.,Techna Institute for the Advancement of Technology for Health
Medical Physics | Year: 2013

Purpose: Recent advances in preclinical radiotherapy systems have provided the foundation for scaling many of the elements of clinical radiation therapy practice to the dimensions and energy demanded in small animal studies. Such systems support the technical capabilities to accurately deliver highly complex dose distributions, but methods to optimize and deliver such distributions remain in their infancy. This study developed an optimization method based on empirically measured two-dimensional dose kernel measurements to deliver arbitrary planar dose distributions on a recently developed small animal radiotherapy platform. Methods: A two-dimensional dose kernel was measured with repeated radiochromic film measurements for the circular 1 mm diameter fixed collimator of the small animal radiotherapy system at 1 cm depth in a solid water phantom. This kernel was utilized in a sequential quadratic programming optimization framework to determine optimal beam positions and weights to deliver an arbitrary desired dose distribution. The positions and weights were then translated to a set of stage motions to automatically deliver the optimized dose distribution. End-to-end efficacy of the framework was quantified through five repeated deliveries of two dosimetric challenges: (1) a 5 mm radius bullseye distribution, and (2) a "sock" distribution contained within a 9 × 13 mm bounding box incorporating rectangular, semicircular, and exponentially decaying geometric constructs and a rectangular linear dose gradient region. These two challenges were designed to gauge targeting, geometric, and dosimetric fidelity. Results: Optimization of the bullseye and sock distributions required 2.1 and 5.9 min and utilized 50 and 77 individual beams for delivery, respectively. Automated delivery of the resulting optimized distributions, validated using radiochromic film measurements, revealed an average targeting accuracy of 0.32 mm, and a dosimetric delivery error along four line profiles taken through the sock distribution of 3.9%. Mean absolute delivery error across the 0-1 Gy linear dose gradient over 7.5 mm was 0.01 Gy. Conclusions: The work presented here demonstrates the potential for complex dose distributions to be planned and automatically delivered with millimeter scale heterogeneity at submillimeter accuracy. This capability establishes the technical foundation for preclinical validation of biologically guided radiotherapy investigations and development of unique radiobiological experiments. © 2013 American Association of Physicists in Medicine.

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