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

Deformable models for adaptive radiotherapy planning

Radiotherapy is the most widely used treatment for cancer, with 4 out of 10 cancer patients receiving radiotherapy as part of their treatment. The delineation of gross tumour volume (GTV) is crucial in the treatment of radiotherapy. An automatic contouring system would be beneficial in radiotherapy planning in order to generate objective, accurate and reproducible GTV contours. Image guided radiotherapy (IGRT) acquires patient images just before treatment delivery to allow any necessary positional correction. Consequently, real-time contouring system provides an opportunity to adopt radiotherapy on the treatment day. In this thesis, freely deformable models (FDM) and shape constrained deformable models (SCDMs) were used to automatically delineate the GTV for brain cancer and prostate cancer. Level set method (LSM) is a typical FDM which was used to contour glioma on brain MRI. A series of low level image segmentation methodologies are cascaded to form a case-wise fully automatic initialisation pipeline for the level set function. Dice similarity coefficients (DSCs) were used to evaluate the contours. Results shown a good agreement between clinical contours and LSM contours, in 93% of cases the DSCs was found to be between 60% and 80%. The second significant contribution is a novel development to the active shape model (ASM), a profile feature was selected from pre-computed texture features by minimising the Mahalanobis distance (MD) to obtain the most distinct feature for each landmark, instead of conventional image intensity. A new group-wise registration scheme was applied to solve the correspondence definition within the training data. This ASM model was used to delineated prostate GTV on CT. DSCs for this case was found between 0.75 and 0.91 with the mean DSC 0.81. The last contribution is a fully automatic active appearance model (AAM) which captures image appearance near the GTV boundary. The image appearance of inner GTV was discarded to spare the potential disruption caused by brachytherapy seeds or gold markers. This model outperforms conventional AAM at the prostate base and apex region by involving surround organs. The overall mean DSC for this case is 0.85

Image Processing and Analysis for Preclinical and Clinical Applications

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis

Study of serial markers of biological response in rectal cancer patients receiving preoperative chemoradiotherapy with or without biological agents

The key to understanding the heterogeneous behaviour of similar stage locally advance rectal cancer lies in the understanding of tumour biology. The aim of this project was to investigate the biological behaviour of rectal cancers and its alterations in response to neoadjuvant chemoradiotherapy, by studying the intrinsic radiosensitivity, pathophysiology and angiogenesis of rectal cancers. It was intended to provide information that may help risk-stratify patients for individualised treatments including optimal timing of surgery after chemoradiotherapy. Consecutive patients with locally advanced, non-metastatic rectal cancer, who were considered suitable for long-course neoadjuvant chemoradiotherapy, were prospectively recruited. Radiosensitivity was studied by investigating the timing of DNA repair analysis with single cell gel electrophoresis (comet assay). The tumour pathophysiology and angiogenesis was investigated in vivo by novel functional imaging techniques (multiparametric magnetic resonance imaging and dynamic contrast enhanced computed tomography). It is demonstrated that rectal cancer tissue consists of cells with heterogeneous radiosensitivities and functional microvascularity. Until six weeks after NCRT, the DNA repair remains inhibited with progressive devascularisation and increasing hypoxic blood volume resulting in loss of tumour cells. Thereafter, variable fractions of cancer cell may continue to perish or survive with corresponding changes in vascularity. Therefore, the period between the sixth and eleventh weeks after neoadjuvant therapy is a critical time when surviving cells from rectal cancers may develop aggressive traits with long-term consequences. Hence, biological assessment of locally advance rectal cancers after six weeks post-NCRT may help risk-stratify patients for individualised therapy

Novel Magnetic Resonance Imaging-Compatible Mechatronic Needle Guidance System for Prostate Focal Laser Ablation Therapy

Advances in prostate cancer (PCa) screening techniques have led to diagnosis of many cases of low-grade and highly localized disease. Conventional whole-gland therapies often result in overtreatment in such cases and debate still surrounds the optimal method of oncologic control. MRI-guided prostate focal laser ablation (FLA) is a minimally invasive treatment option, which has demonstrated potential to destroy localized lesions while sparing healthy prostatic tissue, thereby reducing treatment-related side effects. Many challenges still exist in the development of FLA, including patient selection; tumour localization, visualization, and characterization; needle guidance; and evaluation of treatment efficacy. The objective of this thesis work was to advance and enhance techniques for needle guidance in MRI-guided focal laser ablation (FLA) therapy of PCa. Several steps were taken in achieving this goal. Firstly, we evaluated the overlap between identified lesions and MRI-confirmed ablation regions using conventional needle guidance. Non-rigid thin-plate spline registration of pre-operative and intra-operative images was performed to align lesions with ablation boundaries and quantify the degree of coverage. Complete coverage of the lesion with the ablation zone is a clinically important metric of success for FLA therapy and we found it was not achieved in many cases. Therefore, our next step was to develop an MRI-compatible, remotely actuated mechatronic system for transperineal FLA of prostate cancer. The system allows physicians in the MRI scanner control room to accurately target lesions through 4 degrees of freedom while the patient remains in the scanner bore. To maintain compatibility with the MRI environment, piezoelectric motors were used to actuate the needle guidance templates, the device was constructed from non-ferromagnetic materials, and all cables were shielded from electromagnetic interference. The MR compatibility and needle placement accuracy of the device were evaluated with virtual and phantom targets. The system should next be validated for accuracy and usefulness in a clinical trial where more complex tissue properties and potential patient motion will be encountered. Future advances in modeling the tissue properties and compensating for deformation of the prostate, as well as predicting needle deflection, will further bolster the potential of FLA as option for the management of PCa

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world