Machine learning using radiomics and dosiomics for normal tissue complication probability modeling of radiation-induced xerostomia

In routine clinical practice, the risk of xerostomia is typically managed by limiting the mean radiation dose to parotid glands. This approach used to give satisfying results. In recent years, however, several studies have reported mean-dose models to fail in the recognition of xerostomia risk. This can be explained by a strong improvement of overall dose conformality in radiotherapy due to recent technological advances, and thereby a substantial reduction of the mean dose to parotid glands. This thesis investigated novel approaches to building reliable normal tissue complication probability (NTCP) models of xerostomia in this context. For the purpose of the study, a cohort of 153 head-and-neck cancer patients treated with radiotherapy at Heidelberg University Hospital was retrospectively collected. The predictive performance of the mean-dose to parotid glands was evaluated with the Lyman-Kutcher-Burman (LKB) model. In order to examine the individual predictive power of predictors describing parotid shape (radiomics), dose shape (dosiomics), and demographic characteristics, a total of 61 different features was defined and extracted from the DICOM files. These included the patient’s age and sex, parotid shape features, features related to the dose-volume histogram, the mean dose to subvolumes of parotid glands, spatial dose gradients, and three-dimensional dose moments. In the multivariate analysis, a variety of machine learning algorithms was evaluated: 1) classification methods, that discriminated patients between a high and a low risk of complication, 2) feature selection techniques, that aimed to select a number of highly informative covariates from a large set of predictors, 3) sampling methods, that reduced the class imbalance, 4) data cleaning methods, that reduced noise in the data set. The predictive performance of the models was validated internally, using nested cross-validation, and externally, using an independent patient cohort from the PARSPORT clinical trial. The LKB model showed fairly good performance on mild-to-severe (G1+) xerostomia predictions. The corresponding dose-response curve revealed that even small doses to parotid glands increase the risk of xerostomia and should be kept as low as possible. For the patients who did develop moderate-to-severe (G2+) xerostomia, the mean dose was not an informative predictor, even though the efficient sparing of parotid glands allowed to achieve low G2+ xerostomia rates. The features describing the shape of a parotid gland and the shape of a dose proved to be highly predictive of xerostomia. In particular, the parotid volume and the spatial dose gradients in the transverse plane explained xerostomia well. The results of the machine learning algorithms comparison showed that a particular choice of a classifier and a feature selection method can significantly influence predictive performance of the NTCP model. In general, support vector machines and extra-trees achieved top performance, especially for the endpoints with a large number of observations. For the endpoints with a smaller number of observations, simple logistic regression often performed on a par with the top-ranking machine learning algorithms. The external validation showed that the analyzed multivariate models did not generalize well to the PARSPORT cohort. The only features that were predictive of xerostomia both in the Heidelberg (HD) and the PARSPORT cohort were the spatial dose gradients in the right-left and the anterior-posterior directions. Substantial differences in the distribution of covariates between the two cohorts were observed, which may be one of the reasons for the weak generalizability of the HD models. The results presented in this thesis undermine the applicability of NTCP models of xerostomia based only on the mean dose to parotid glands in highly conformal radiotherapy treatments. The spatial dose gradients in the left-right and the anterior-posterior directions proved to be predictive of xerostomia both in the HD and the PARSPORT cohort. This finding is especially important as it is not limited to a single cohort but describes a general pattern present in two independent data sets. The performance of the sophisticated machine learning methods may indicate a need for larger patient cohorts in studies on NTCP models in order to fully benefit from their advantages. Last but not least, the observed covariate-shift between the HD and the PARSPORT cohort motivates, in the author’s opinion, a need for reporting information about the covariate distribution when publishing novel NTCP models

Automatic classification of treatment-deviation sources in proton therapy using prompt-gamma-imaging information

Prompt-gamma imaging (PGI) was proposed in the 2000s as a promising in vivo range-verification method to maintain the physical advantage of proton beams by reducing unwanted range-uncertainties. Recently, PGI with a slit camera has been successfully implemented in clinical application. Despite its high accuracy and sensitivity to range deviation being shown in several studies, the clinical benefits of PGI have not yet been investigated. Hence, to fully exploit the advantages of PGI, this thesis aims to investigate the feasibility of PGI-based range verification for the automatic classification of treatment deviations and differentiation of relevant from non-relevant changes in the treatment of head-and-neck (H&N) tumors. In the first part of this thesis, the four most common types of treatment deviations in proton therapy (PT) were investigated regarding their PGI signature and by considering clinically relevant and non-relevant scenarios. A heuristic decision tree (DT) model was iteratively developed. To gain understanding of the specific signature of the error sources, different levels of geometrical complexities were explored, from simple to complex. At the simplest level, a phantom with homogeneous density was used to distinguish range-prediction and setup errors. Next, in the intermediate complexity level, a phantom with heterogeneous density was used to inspect the additional error scenarios of anatomical changes. Finally, real patient CT scans were used to investigate the relevance of changes based on clinical constraints. In the final model, a five-step filtering approach was used during pre-processing to select reliable pencil-beam-scanning spots for range verification. In this study, five features extracted from the filtered PGI data were used to classify the treatment deviation. The model is able distinguish four introduced scenarios into six classes as follows: (1) overestimation of range prediction, (2) underestimation of range prediction, (3) setup error with larger air gap, (4) setup error with smaller air gap, (5) anatomical change, and (6) non-relevant change. To ensure the application was effective, independent patient CT datasets were used to test the model. The results yielded an excellent performance of the DT classifier, with high accuracy, sensitivity, and specificity of 96%, 100%, and 85.7%, respectively. According to these findings, this model can sensitively detect treatment deviations in PT based on simulated PGI data. In the second part of this work, an alternative approach based on machine learning (ML) was taken to automatically classify the error sources. In the first stage, the two approaches were compared, using the same features as well as the same training and test datasets. The results show that the ML approach was slightly better than the heuristic DT approach in terms of accuracy. However, the performance of both approaches was excellent for the individual scenarios. Thus, these results confirm that the PGI-based data classification with five features can be applied to detect individual sources of treatment deviation in PT. In the second stage, there was an investigation of more complex and more realistic combinations of error scenarios, which was out of the scope of the DT approach. The results demonstrated that the performance of the ML-based classifiers declined in general. Furthermore, the additional features of the PG shift did not substantially improve the performance of the classifiers. As a consequence, these findings mark important issues for future research. Potentially, usage of the spatial information from the spot-based PGI data and more complex techniques such as deep learning may improve the performance of classifiers with respect to scenarios with multiple error sources. However, regardless of this, it is recommended that these findings be confirmed and validated in simulations under measurement-like conditions or with real PG measurements of H&N patients themselves. Moreover, this classification model could eventually be tested with other body sites and entities in order to assess its compatibility and adaptation requirements. In summary, this study yielded promising results regarding the automatic classification of treatment-deviation sources and the differentiation of relevant and non-relevant changes in H&N-tumor treatment in PT with PGI data. This simulation study marks an important step towards fully automated PGI-based proton-range verification, which could contribute to closing the treatment-workflow loop of adaptive therapy by supporting clinical decision-making and, ultimately, improving clinical PT.:1 Introduction 2 Background 2.1 Proton therapy 2.1.1 Rationale for proton therapy 2.1.2 Uncertainties and their mitigation 2.2 In vivo range-verification techniques 2.2.1 Range probing 2.2.2 Proton tomography 2.2.3 Magnetic resonance imaging 2.2.4 Ionoacoustic detection 2.2.5 Treatment-activated positron-emission tomography imaging 2.2.6 Prompt-gamma based detection 3 Prompt-gamma imaging with a knife-edged slit camera 3.1 Current state-of-the-art 3.2 Prompt-gamma camera system 3.3 Data acquisition and analysis 4 Error-source classification using heuristic decision tree approach 4.1 Study design 4.1.1 Case selection 4.1.2 Investigated scenarios 4.1.3 Prompt-gamma simulation and range shift determination 4.2 Development of the model 4.2.1 First-generation model 4.2.2 Second-generation model 4.2.3 Third-generation model 4.3 Model testing 4.4 Discussion: decision-tree model 5 Error-source classification using a machine-learning approach 5.1 Machine learning for classification 5.1.1 Support-vector-machine algorithm 5.1.2 Ensemble algorithm – random forest 5.1.3 Logistic-regression algorithm 5.2 Study design 5.2.1 Case selection 5.2.2 Feature selection 5.3 Model generation 5.4 Model testing 5.5 Discussion 6 Summary/ Zusammenfassung Bibliography Appendix List of Figures List of Tables List of Abbreviation

Virtual patient-specific treatment verification using machine learning methods to assist the dose deliverability evaluation of radiotherapy prostate plans

Machine Learning (ML) methods represent a potential tool to support and optimize virtual patient-specific plan verifications within radiotherapy workflows. However, previously reported applications did not consider the actual physical implications in the predictor’s quality and modelperformance and did not report the implementation pertinence nor their limitations. Therefore, the main goal of this thesis was to predict dose deliverability using different ML models and input predictor features, analysing the physical aspects involved in the predictions to propose areliable decision-support tool for virtual patient-specific plan verification protocols. Among the principal predictors explored in this thesis, numerical and high-dimensional features based on modulation complexity, treatment-unit parameters, and dosimetric plan parameters were all implemented by designing random forest (RF), extreme gradient boosting (XG-Boost), neural networks (NN), and convolutional neural networks (CNN) models to predict gamma passing rates (GPR) for prostate treatments. Accordingly, this research highlights three principal findings. (1) The dataset composition's heterogeneity directly impacts the quality of the predictor features and, subsequently, the model performance. (2) The models based on automatic extracted features methods (CNN models) of multi-leaf-collimator modulation maps (MM) presented a more independent and transferable prediction performance. Furthermore, (3) ML algorithms incorporated in radiotherapy workflows for virtual plan verification are required to retrieve treatment plan parameters associated with the prediction to support themodel's reliability and stability. Finally, this thesis presents how the most relevant automatically extracted features from the activation maps were considered to suggest an alternative decision support tool to comprehensively evaluate the causes of the predicted dose deliverability

Improving nuclear medicine with deep learning and explainability: two real-world use cases in parkinsonian syndrome and safety dosimetry

Computer vision in the area of medical imaging has rapidly improved during recent years as a consequence of developments in deep learning and explainability algorithms. In addition, imaging in nuclear medicine is becoming increasingly sophisticated, with the emergence of targeted radiotherapies that enable treatment and imaging on a molecular level (“theranostics”) where radiolabeled targeted molecules are directly injected into the bloodstream. Based on our recent work, we present two use-cases in nuclear medicine as follows: first, the impact of automated organ segmentation required for personalized dosimetry in patients with neuroendocrine tumors and second, purely data-driven identification and verification of brain regions for diagnosis of Parkinson’s disease. Convolutional neural network was used for automated organ segmentation on computed tomography images. The segmented organs were used for calculation of the energy deposited into the organ-at-risk for patients treated with a radiopharmaceutical. Our method resulted in faster and cheaper dosimetry and only differed by 7% from dosimetry performed by two medical physicists. The identification of brain regions, however was analyzed on dopamine-transporter single positron emission tomography images using convolutional neural network and explainability, i.e., layer-wise relevance propagation algorithm. Our findings confirm that the extra-striatal brain regions, i.e., insula, amygdala, ventromedial prefrontal cortex, thalamus, anterior temporal cortex, superior frontal lobe, and pons contribute to the interpretation of images beyond the striatal regions. In current common diagnostic practice, however, only the striatum is the reference region, while extra-striatal regions are neglected. We further demonstrate that deep learning-based diagnosis combined with explainability algorithm can be recommended to support interpretation of this image modality in clinical routine for parkinsonian syndromes, with a total computation time of three seconds which is compatible with busy clinical workflow. Overall, this thesis shows for the first time that deep learning with explainability can achieve results competitive with human performance and generate novel hypotheses, thus paving the way towards improved diagnosis and treatment in nuclear medicine

New approaches to improving the accuracy and outcome of Intensity Modulated Radiotherapy

Several aspects of the IMRT treatment chain are evaluated with a view to improving the accuracy with which the treatment can be delivered and also the radiobiological outcome for the patient. A safe, non-invasive way of evaluating patient repositioning for multi-fraction treatments is developed and presented, with results highlighting the head-and-neck immobilisation system that provides the best results at Addenbrooke's radiotherapy centre. Previously published dose evaluation metrics are studied in depth, identifying ways in which they differ. A new method of calculating the popular Gamma index is proposed, along with a new Kappa index which can highlight areas of dose mismatch more readily than the Gamma index. Using a specially developed dose calculation system, systematic errors found with the IMRT treatment machines at Addenbrooke's are simulated to determine the dose differences created by these errors, which are then evaluated using the Kappa index with typical tolerances used by previous authors. The concept of equivalent uniform dose (EUD) is then used to assess whether the tolerances for distance and dose are equivalent to each other. They are found not to be equivalent, and better relative values of the two quantities are suggested for use in composite indices. The use of the EUD is extended further into analysing the equivalent dose differences of the treatment delivery errors previously calculated. It is found that the equivalent dose to the target volume is changed by more than the composite indices indicate. The EUD is then used to calculate normal tissue complication probabilities, the probability of uncomplicated control and therapeutic gain. It is found that the dose errors predicted can actually be beneficial to the patient outcome, although not always. It is concluded that dose evaluation for complex treatments such as IMRT should be more radiobiologically-based in order to assess patient outcomes properly

Grid Analysis of Radiological Data

IGI-Global Medical Information Science Discoveries Research Award 2009International audienceGrid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services

Texture Analysis Platform for Imaging Biomarker Research

abstract: The rate of progress in improving survival of patients with solid tumors is slow due to late stage diagnosis and poor tumor characterization processes that fail to effectively reflect the nature of tumor before treatment or the subsequent change in its dynamics because of treatment. Further advancement of targeted therapies relies on advancements in biomarker research. In the context of solid tumors, bio-specimen samples such as biopsies serve as the main source of biomarkers used in the treatment and monitoring of cancer, even though biopsy samples are susceptible to sampling error and more importantly, are local and offer a narrow temporal scope. Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers. This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Contrôle en temps réel de la précision du suivi indirect de tumeurs mobiles en radiothérapie

Le but de la radiothérapie est d’irradier les cellules cancéreuses tout en préservant au maximum les tissus sains environnants. Or, dans le cas du cancer du poumon, la respiration du patient engendre des mouvements de la tumeur pendant le traitement. Une solution possible est de repositionner continuellement le faisceau d’irradiation sur la cible tumorale en mouvement. L’e cacité et la sûreté de cette approche reposent sur la localisation précise en temps réel de la tumeur. Le suivi indirect consiste à inférer la position de la cible tumorale à partir de l’observation d’un signal substitut, visible en continu sans nécessiter de rayonnement ionisant. Un modèle de corrélation spatial doit donc être établi. Par ailleurs, pour compenser la latence du système, l’algorithme de suivi doit pouvoir également anticiper la position future de la cible. Parce que la respiration du patient varie dans le temps, les modèles de prédiction et de corrélation peuvent devenir imprécis. La prédiction de la position de la tumeur devrait alors idéalement être complétée par l’estimation des incertitudes associées aux prédictions. Dans la pratique clinique actuelle, ces incertitudes de positionnement en temps réel ne sont pas explicitement prédites. Cette thèse de doctorat s’intéresse au contrôle en temps réel de la précision du suivi indirect de tumeurs mobiles en radiothérapie. Dans un premier temps, une méthode bayésienne pour le suivi indirect en radiothérapie est développée. Cette approche, basée sur le filtre de Kalman, permet de prédire non seulement la position future de la tumeur à partir d’un signal substitut, mais aussi les incertitudes associées. Ce travail o re une première preuve de concept, et montre également le potentiel du foie comme substitut interne, qui apparait plus robuste et fiable que les marqueurs externes communément utilisés dans la pratique clinique. Dans un deuxième temps, une adaptation de la méthode est proposée afin d’améliorer sa robustesse face aux changements de respiration. Cette innovation permet de prédire des régions de confiance adaptatives, capables de détecter les erreurs de prédiction élevées, en se basant exclusivement sur l’observation du signal substitut. Les résultats révèlent qu’à sensibilité élevée (90%), une spécificité d’environ 50% est obtenue. Un processus de validation innovant basé sur ces régions de confiance adaptatives est ensuite évalué et comparé au processus conventionnel qui consiste en des mesures de la cible à intervalles de temps fixes et prédéterminés. Une version adaptative de la méthode bayésienne est donc développée afin d’intégrer des mesures occasionnelles de la position de la cible. Les résultats confirment que les incertitudes prédites par la méthode bayésienne permettent de détecter les erreurs de prédictions élevées, et démontrent que le processus de validation basé sur ces incertitudes a le potentiel d’être plus e cace que les validations régulières. Ces approches bayésiennes sont validées sur des séquences respiratoires de volontaires, acquises par imagerie par résonance magnétique (IRM) dynamique et interpolées à haute fréquence. Afin de compléter l’évaluation de la méthode bayésienne pour le suivi indirect, une validation expérimentale préliminaire est conduite sur des données cliniques de patients atteints de cancer du poumon. Les travaux de ce projet doctoral promettent une amélioration du contrôle en temps réel de la précision des prédictions lors des traitements de radiothérapie. Finalement, puisque l’imagerie ultrasonore pourrait être employée pour visualiser les substituts internes, une étude préliminaire sur l’évaluation automatique de la qualité des images ultrasonores est présentée. Ces résultats pourront être utilisés ultérieurement pour le suivi indirect en radiothérapie en vue d’optimiser les acquisitions ultrasonores pendant les traitements et faciliter l’extraction automatique du mouvement du substitut.The goal of radiotherapy is to irradiate cancer cells while maintaining a low dose of radiation to the surrounding healthy tissue. In the case of lung cancer, the patient’s breathing causes the tumor to move during treatment. One possible solution is to continuously reposition the irradiation beam on the moving target. The e ectiveness and safety of this approach rely on accurate real-time localization of the tumor. Indirect strategies derive the target positions from a correlation model with a surrogate signal, which is continuously monitored without the need for radiation-based imaging. In addition, to compensate for system latency, the tracking algorithm must also be able to anticipate the future position of the target. Because the patient’s breathing varies over time, prediction and correlation models can become inaccurate. Ideally, the prediction of the tumor location would also include an estimation of the uncertainty associated with the prediction. However, in current clinical practice, these real-time positioning uncertainties are not explicitly predicted. This doctoral thesis focuses on real-time control of the accuracy of indirect tracking of mobile tumors in radiotherapy. First, a Bayesian method is developed. This approach, based on Kalman filter theory, allows predicting both future target motion in real-time from a surrogate signal and associated uncertainty. This work o ers a first proof of concept, and also shows the potential of the liver as an internal substitute as it appears more robust and reliable than the external markers commonly used in clinical practice. Second, an adaptation of the method is proposed to improve its robustness against changes in breathing. This innovation enables the prediction of adaptive confidence regions that can be used to detect significant prediction errors, based exclusively on the observation of the surrogate signal. The results show that at high sensitivity (90%), a specificity of about 50% is obtained. A new validation process based on these adaptive confidence regions is then evaluated and compared to the conventional validation process (i.e., target measurements at fixed and predetermined time intervals). An adaptive version of the Bayesian method is therefore developed to valuably incorporate occasional measurements of the target position. The results confirm that the uncertainties predicted by the Bayesian method can detect high prediction errors, and demonstrate that the validation process based on these uncertainties has the potential to be more e cient and e ective than regular validations. For these studies, the proposed Bayesian methods are validated on respiratory sequences of volunteers, acquired by dynamic MRI and interpolated at high frequency. In order to complete the evaluation of the Bayesian method for indirect tracking, experimental validation is conducted on clinical data of patients with lung cancer. The work of this doctoral project promises to improve the real-time control of the accuracy of predictions during radiotherapy treatments. Finally, since ultrasound imaging could be used to visualize internal surrogates, a preliminary study on automatic ultrasound image quality assessment is presented. These results can later be used for indirect tracking in radiotherapy to optimize ultrasound acquisitions during treatments and facilitate the automatic estimation of surrogate motion

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems