Ensemble Support Vector Machine Models of Radiation-Induced Lung Injury Risk

Patients undergoing radiation therapy can develop a potentially fatal inflammation of the lungs known as radiation pneumonitis: RP). In practice, modeling RP factors is difficult because existing data are under-sampled and imbalanced. Support vector machines: SVMs), a class of statistical learning methods that implicitly maps data into a higher dimensional space, is one machine learning method that recently has been applied to the RP problem with encouraging results. In this thesis, we present and evaluate an ensemble SVM method of modeling radiation pneumonitis. The method internalizes kernel/model parameter selection into model building and enables feature scaling via Olivier Chapelle\u27s method. We show that the ensemble method provides statistically significant increases to the cross-folded area under the receiver operating characteristic curve while maintaining model parsimony. Finally, we extend our model with John C. Platt\u27s method to support non-binary outcomes in order to augment clinical relevancy

Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy

In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

Improving radiotherapy using image analysis and machine learning

With ever increasing advancements in imaging, there is an increasing abundance of images being acquired in the clinical environment. However, this increase in information can be a burden as well as a blessing as it may require significant amounts of time to interpret the information contained in these images. Computer assisted evaluation is one way in which better use could be made of these images. This thesis presents the combination of texture analysis of images acquired during the treatment of cancer with machine learning in order to improve radiotherapy. The first application is to the prediction of radiation induced pneumonitis. In 13- 37% of cases, lung cancer patients treated with radiotherapy develop radiation induced lung disease, such as radiation induced pneumonitis. Three dimensional texture analysis, combined with patient-specific clinical parameters, were used to compute unique features. On radiotherapy planning CT data of 57 patients, (14 symptomatic, 43 asymptomatic), a Support Vector Machine (SVM) obtained an area under the receiver operator curve (AUROC) of 0.873 with sensitivity, specificity and accuracy of 92%, 72% and 87% respectively. Furthermore, it was demonstrated that a Decision Tree classifier was capable of a similar level of performance using sub-regions of the lung volume. The second application is related to prostate cancer identification. T2 MRI scans are used in the diagnosis of prostate cancer and in the identification of the primary cancer within the prostate gland. The manual identification of the cancer relies on the assessment of multiple scans and the integration of clinical information by a clinician. This requires considerable experience and time. As MRI becomes more integrated within the radiotherapy work flow and as adaptive radiotherapy (where the treatment plan is modified based on multi-modality image information acquired during or between RT fractions) develops it is timely to develop automatic segmentation techniques for reliably identifying cancerous regions. In this work a number of texture features were coupled with a supervised learning model for the automatic segmentation of the main cancerous focus in the prostate - the focal lesion. A mean AUROC of 0.713 was demonstrated with 10-fold stratified cross validation strategy on an aggregate data set. On a leave one case out basis a mean AUROC of 0.60 was achieved which resulted in a mean DICE coefficient of 0.710. These results showed that is was possible to delineate the focal lesion in the majority (11) of the 14 cases used in the study

Radiomics for Response Assessment after Stereotactic Radiotherapy for Lung Cancer

Stereotactic ablative radiotherapy (SABR) is a guideline-specified treatment option for patients with early stage non-small cell lung cancer. After treatment, patients are followed up regularly with computed tomography (CT) imaging to determine treatment response. However, benign radiographic changes to the lung known as radiation-induced lung injury (RILI) frequently occur. Due to the large doses delivered with SABR, these changes can mimic the appearance of a recurring tumour and confound response assessment. The objective of this work was to evaluate the accuracy of radiomics, for prediction of eventual local recurrence based on CT images acquired within 6 months of treatment. A semi-automatic decision support system was developed to segment and sample regions of common post-SABR changes, extract radiomic features and classify images as local recurrence or benign injury. Physician ability to detect timely local recurrence was also measured on CT imaging, and compared with that of the radiomics tool. Within 6 months post-SABR, physicians assessed the majority of images as no recurrence and had an overall lower accuracy compared to the radiomics system. These results suggest that radiomics can detect early changes associated with local recurrence that are not typically considered by physicians. These appearances detected by radiomics may be early indicators of the promotion and progression to local recurrence. This has the potential to lead to a clinically useful computer-aided decision support tool based on routinely acquired CT imaging, which could lead to earlier salvage opportunities for patients with recurrence and fewer invasive investigations of patients with only benign injury

Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature

Quantitative Analysis of Radiation-Associated Parenchymal Lung Change

Radiation-induced lung damage (RILD) is a common consequence of thoracic radiotherapy (RT). We present here a novel classification of the parenchymal features of RILD. We developed a deep learning algorithm (DLA) to automate the delineation of 5 classes of parenchymal texture of increasing density. 200 scans were used to train and validate the network and the remaining 30 scans were used as a hold-out test set. The DLA automatically labelled the data with Dice Scores of 0.98, 0.43, 0.26, 0.47 and 0.92 for the 5 respective classes. Qualitative evaluation showed that the automated labels were acceptable in over 80% of cases for all tissue classes, and achieved similar ratings to the manual labels. Lung registration was performed and the effect of radiation dose on each tissue class and correlation with respiratory outcomes was assessed. The change in volume of each tissue class over time generated by manual and automated segmentation was calculated. The 5 parenchymal classes showed distinct temporal patterns We quantified the volumetric change in textures after radiotherapy and correlate these with radiotherapy dose and respiratory outcomes. The effect of local dose on tissue class revealed a strong dose-dependent relationship We have developed a novel classification of parenchymal changes associated with RILD that show a convincing dose relationship. The tissue classes are related to both global and local dose metrics, and have a distinct evolution over time. Although less strong, there is a relationship between the radiological texture changes we can measure and respiratory outcomes, particularly the MRC score which directly represents a patient’s functional status. We have demonstrated the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible

AI/ML advances in non-small cell lung cancer biomarker discovery

Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers

Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy

Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages