Intensity modulated radiation therapy and arc therapy: validation and evolution as applied to tumours of the head and neck, abdominal and pelvic regions

Intensiteitsgemoduleerde radiotherapie (IMRT) laat een betere controle over de dosisdistributie (DD) toe dan meer conventionele bestralingstechnieken. Zo is het met IMRT mogelijk om concave DDs te bereiken en om de risico-organen conformeel uit te sparen. IMRT werd in het UZG klinisch toegepast voor een hele waaier van tumorlocalisaties. De toepassing van IMRT voor de bestraling van hoofd- en halstumoren (HHT) vormt het onderwerp van het eerste deel van deze thesis. De planningsstrategie voor herbestralingen en bestraling van HHT, uitgaande van de keel en de mondholte wordt beschreven, evenals de eerste klinische resultaten hiervan. IMRT voor tumoren van de neus(bij)holten leidt tot minstens even goede lokale controle (LC) en overleving als conventionele bestralingstechnieken, en dit zonder stralingsgeïnduceerde blindheid. IMRT leidt dus tot een gunstiger toxiciteitprofiel maar heeft nog geen bewijs kunnen leveren van een gunstig effect op LC of overleving. De meeste hervallen van HHT worden gezien in het gebied dat tot een hoge dosis bestraald werd, wat erop wijst dat deze “hoge dosis” niet volstaat om alle clonogene tumorcellen uit te schakelen. We startten een studie op, om de mogelijkheid van dosisescalatie op geleide van biologische beeldvorming uit te testen. Naast de toepassing en klinische validatie van IMRT bestond het werk in het kader van deze thesis ook uit de ontwikkeling en het klinisch opstarten van intensiteitgemoduleerde arc therapie (IMAT). IMAT is een rotationele vorm van IMRT (d.w.z. de gantry draait rond tijdens de bestraling), waarbij de modulatie van de intensiteit bereikt wordt door overlappende arcs. IMAT heeft enkele duidelijke voordelen ten opzichte van IMRT in bepaalde situaties. Als het doelvolume concaaf rond een risico-orgaan ligt met een grote diameter, biedt IMAT eigenlijk een oneindig aantal bundelrichtingen aan. Een planningsstrategie voor IMAT werd ontwikkeld, en type-oplossingen voor totaal abdominale bestraling en rectumbestraling werden onderzocht en klinisch toegepast

Methodological issues in radiation dose–volume outcome analyses: Summary of a joint AAPM/NIH workshop

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135063/1/mp1473.pd

Incorporating Deep Learning Techniques into Outcome Modeling in Non-Small Cell Lung Cancer Patients after Radiation Therapy

Radiation therapy (radiotherapy) together with surgery, chemotherapy, and immunotherapy are common modalities in cancer treatment. In radiotherapy, patients are given high doses of ionizing radiation which is aimed at killing cancer cells and shrinking tumors. Conventional radiotherapy usually gives a standard prescription to all the patients, however, as patients are likely to have heterogeneous responses to the treatment due to multiple prognostic factors, personalization of radiotherapy treatment is desirable. Outcome models can serve as clinical decision-making support tools in the personalized treatment, helping evaluate patients’ treatment options before the treatment or during fractionated treatment. It can further provide insights into designing of new clinical protocols. In the outcome modeling, two indices including tumor control probability (TCP) and normal tissue complication probability (NTCP) are usually investigated. Current outcome models, e.g., analytical models and data-driven models, either fail to take into account complex interactions between physical and biological variables or require complicated feature selection procedures. Therefore, in our studies, deep learning (DL) techniques are incorporated into outcome modeling for prediction of local control (LC), which is TCP in our case, and radiation pneumonitis (RP), which is NTCP in our case, in non-small-cell lung cancer (NSCLC) patients after radiotherapy. These techniques can improve the prediction performance of outcomes and simplify model development procedures. Additionally, longitudinal data association, actuarial prediction, and multi-endpoints prediction are considered in our models. These were carried out in 3 consecutive studies. In the first study, a composite architecture consisting of variational auto-encoder (VAE) and multi-layer perceptron (MLP) was investigated and applied to RP prediction. The architecture enabled the simultaneous dimensionality reduction and prediction. The novel VAE-MLP joint architecture with area under receiver operative characteristics (ROC) curve (AUC) [95% CIs] 0.781 [0.737-0.808] outperformed a strategy which involves separate VAEs and classifiers (AUC 0.624 [ 0.577-0.658]). In the second study, composite architectures consisted of 1D convolutional layer/ locally-connected layer and MLP that took into account longitudinal associations were applied to predict LC. Composite architectures convolutional neural network (CNN)-MLP that can model both longitudinal and non-longitudinal data yielded an AUC 0.832 [ 0.807-0.841]. While plain MLP only yielded an AUC 0.785 [CI: 0.752-0.792] in LC control prediction. In the third study, rather than binary classification, time-to-event information was also incorporated for actuarial prediction. DL architectures ADNN-DVH which consider dosimetric information, ADNN-com which further combined biological and imaging data, and ADNN-com-joint which realized multi-endpoints prediction were investigated. Analytical models were also conducted for comparison purposes. Among all the models, ADNN-com-joint performed the best, yielding c-indexes of 0.705 [0.676-0.734] for RP2, 0.740 [0.714-0.765] for LC and an AU-FROC 0.720 [0.671-0.801] for joint prediction. The performance of proposed models was also tested on a cohort of newly-treated patients and multi-institutional RTOG0617 datasets. These studies taken together indicate that DL techniques can be utilized to improve the performance of outcome models and potentially provide guidance to physicians during decision making. Specifically, a VAE-MLP joint architectures can realize simultaneous dimensionality reduction and prediction, boosting the performance of conventional outcome models. A 1D CNN-MLP joint architecture can utilize temporal-associated variables generated during the span of radiotherapy. A DL model ADNN-com-joint can realize multi-endpoint prediction, which allows considering competing risk factors. All of those contribute to a step toward enabling outcome models as real clinical decision support tools.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162923/1/sunan_1.pd

Improving Dose-Response Correlations for Locally Advanced NSCLC Patients Treated with IMRT or PSPT

The standard of care for locally advanced non-small cell lung cancer (NSCLC) is concurrent chemo-radiotherapy. Despite recent advancements in radiation delivery methods, the median survival time of NSCLC patients remains below 28 months. Higher tumor dose has been found to increase survival but also a higher rate of radiation pneumonitis (RP) that affects breathing capability. In fear of such toxicity, less-aggressive treatment plans are often clinically preferred, leading to metastasis and recurrence. Therefore, accurate RP prediction is crucial to ensure tumor coverage to improve treatment outcome. Current models have associated RP with increased dose but with limited accuracy as they lack spatial correlation between accurate dose representation and quantitative RP representation. These models represent lung tissue damage with radiation dose distribution planned pre-treatment, which assumes a fixed patient geometry and inevitably renders imprecise dose delivery due to intra-fractional breathing motion and inter-fractional anatomy response. Additionally, current models employ whole-lung dose metrics as the contributing factor to RP as a qualitative, binary outcome but these global dose metrics discard microscopic, voxel-(3D pixel)-level information and prevent spatial correlations with quantitative RP representation. To tackle these limitations, we developed advanced deformable image registration (DIR) techniques that registered corresponding anatomical voxels between images for tracking and accumulating dose throughout treatment. DIR also enabled voxel-level dose-response correlation when CT image density change (IDC) was used to quantify RP. We hypothesized that more accurate estimates of biologically effective dose distributions actually delivered, achieved through (a) dose accumulation using deformable registration of weekly 4DCT images acquired over the course or radiotherapy and (b) the incorporation of variable relative biological effectiveness (RBE), would lead to statistically and clinically significant improvement in the correlation of RP with biologically effective dose distributions. Our work resulted in a robust intra-4DCT and inter-4DCT DIR workflow, with the accuracy meeting AAPM TG-132 recommendations for clinical implementation of DIR. The automated DIR workflow allowed us to develop a fully automated 4DCT-based dose accumulation pipeline in RayStation (RaySearch Laboratories, Stockholm, Sweden). With a sample of 67 IMRT patients, our results showed that the accumulated dose was statistically different than the planned dose across the entire cohort with an average MLD increase of ~1 Gy and clinically different for individual patients where 16% resulted in difference in the score of the normal tissue complication probability (NTCP) using an established, clinically used model, which could qualify the patients for treatment planning re-evaluation. Lastly, we associated dose difference with accuracy difference by establishing and comparing voxel-level dose-IDC correlations and concluded that the accumulated dose better described the localized damage, thereby a closer representation of the delivered dose. Using the same dose-response correlation strategy, we plotted the dose-IDC relationships for both photon patients (N = 51) and proton patients (N = 67), we measured the variable proton RBE values to be 3.07–1.27 from 9–52 Gy proton voxels. With the measured RBE values, we fitted an established variable proton RBE model with pseudo-R2 of 0.98. Therefore, our results led to statistically and clinically significant improvement in the correlation of RP with accumulated and biologically effective dose distributions and demonstrated the potential of incorporating the effect of anatomical change and biological damage in RP prediction models

Patterns of injury and violence in Yaoundé Cameroon: an analysis of hospital data.

BackgroundInjuries are quickly becoming a leading cause of death globally, disproportionately affecting sub-Saharan Africa, where reports on the epidemiology of injuries are extremely limited. Reports on the patterns and frequency of injuries are available from Cameroon are also scarce. This study explores the patterns of trauma seen at the emergency ward of the busiest trauma center in Cameroon's capital city.Materials and methodsAdministrative records from January 1, 2007, through December 31, 2007, were retrospectively reviewed; information on age, gender, mechanism of injury, and outcome was abstracted for all trauma patients presenting to the emergency ward. Univariate analysis was performed to assess patterns of injuries in terms of mechanism, date, age, and gender. Bivariate analysis was used to explore potential relationships between demographic variables and mechanism of injury.ResultsA total of 6,234 injured people were seen at the Central Hospital of Yaoundé's emergency ward during the year 2007. Males comprised 71% of those injured, and the mean age of injured patients was 29 years (SD = 14.9). Nearly 60% of the injuries were due to road traffic accidents, 46% of which involved a pedestrian. Intentional injuries were the second most common mechanism of injury (22.5%), 55% of which involved unarmed assault. Patients injured in falls were more likely to be admitted to the hospital (p < 0.001), whereas patients suffering intentional injuries and bites were less likely to be hospitalized (p < 0.001). Males were significantly more likely to be admitted than females (p < 0.001)DiscussionPatterns in terms of age, gender, and mechanism of injury are similar to reports from other countries from the same geographic region, but the magnitude of cases reported is high for a single institution in an African city the size of Yaoundé. As the burden of disease is predicted to increase dramatically in sub-Saharan Africa, immediate efforts in prevention and treatment in Cameroon are strongly warranted