OncoSpineSeg: A Software Tool for a Manual Segmentation of Computed Tomography of the Spine on Cancer Patients

The organ most commonly affected by metastatic cancer is the skeleton, and spine is the site where it causes the highest morbidity. Computer-aided diagnosis (CAD) for detecting and assessing metastatic disease in bone or other spine disorders can assist physicians to perform their decision-making tasks. A precise segmentation of the spine is important as a first stage in any automatic diagnosis task. However, it is a challenging problem to segment correctly an affected spine, and it is a crucial step to assess quantitatively the results of segmentation by comparing them with the results of a manual segmentation, reviewed by one experienced radiologist. This chapter presents the design of a MATLAB-based software for the manual segmentation of the spine. The software tool has a simple and easy to use interface, and it works with either computed tomography or magnetic resonance imaging (MRI). A typical workflow includes loading the image volume, creating multi-planar reconstructions, manually contouring the vertebrae, spinal lesions, intervertebral discs and spinal canal with availability of different segmentation tools, classification of the bone into healthy bone, osteolytic metastases, osteoblastic metastases or mixed lesions, being also possible to classify an object as a false-positive and a 3D reconstruction of the segmented objects

A Novel Approach for the Visualisation and Progression Tracking of Metastatic Bone Disease

Metastatic bone disease (MBD) is a common secondary feature of cancer that can cause significant complications, including severe pain and death. Current methods of diagnosis require a highly trained radiologist capable of interpreting medical images and recognising the sites of MBD. These medical images are often noisy, two dimensional, greyscale and usually have a poor resolution. In order to help assist with these issues, several studies have shown that computer aided methods can locate MBD within medical images. However these methods are limited in scope, accuracy, sensitivity, explainability and do not improve upon the poor visualisations of the underlying medical imaging data. To address these limitations, I have developed a novel method of automatic MBD assessment and visualisation using computed tomography (CT) imaging data as the input. The method is fully automated and does not require any human interaction -- although users can interact with a viewer that visualises the results. This method has been tested on CT data from prostate cancer patients as prostate cancer is one of the most common sources of MBD. The method described in this thesis has a sensitivity of 0.871 when detecting sclerotic and lytic lesions within a single data set. This sensitivity is comparable to existing methods, however the scope in detecting these lesions was limited to the vertebrae in previous studies. My method significantly expands this scope to include the ribs, vertebrae, pelvis and proximal femurs. The work in this thesis also provides novel visualisations of the disease and does not suffer from explainability issues that plague modern machine learning algorithms. In addition, I developed a novel method of tracking the spread of MBD at multiple time points using longitudinal CT data. This method is capable of calculating the change in lesion volume size across multiple time points, providing a novel numerical assessment.The Armstrong Trus

Computer-aided detection in musculoskeletal projection radiography: A systematic review

This is the author accepted manuscript. The final version is available from WB Saunders via the DOI in this record.Objectives To investigated the accuracy of computer-aided detection (CAD) software in musculoskeletal projection radiography via a systematic review. Key findings Following selection screening, eligible studies were assessed for bias, and had their study characteristics extracted resulting in 22 studies being included. Of these 22 three studies had tested their CAD software in a clinical setting; the first study investigated vertebral fractures, reporting a sensitivity score of 69.3% with CAD, compared to 59.8% sensitivity without CAD. The second study tested dental caries diagnosis producing a sensitivity score of 68.8% and specificity of 94.1% with CAD, compared to sensitivity of 39.3% and specificity of 96.7% without CAD. The third indicated osteoporotic cases based on CAD, resulting in 100% sensitivity and 81.3% specificity. Conclusion The current evidence reported shows a lack of development into the clinical testing phase; however the research does show future promise in the variation of different CAD systems

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed

Radiomics and Artificial Intelligence for Outcome Prediction in Multiple Myeloma Patients

The significant clinical heterogeneity of Multiple Myeloma (MM) patients implies that a set of consolidated biomarkers is currently missing. Radiomics is an advanced, quantitative feature-based methodology for image analysis. We assess the feasibility of an AI-based approach for the automatic stratification of MM patients from CT data, and for the automatic identification of radiological biomarkers with a possible prognostic value. A retrospective analysis of n = 33 transplanted MM with focal lesion were performed via an open-source toolbox that extracted 109 radiomics features. The redundancy reduction was realized via correlation and principal component analysis. The highest sensitivity and critical success index (CSI) were obtained representing each patient, with 17 focal features selected via correlation with the 24 features describing the overall skeletal asset. The Mann\u2013 Whitney U-test showed that three among the 17 imaging descriptors passed the null hypothesis. This computational approach to the interpretation of radiomics features shows the potential for the stratification of relapsed and non-relapsed MM patients, and could represent a prognostic image-based procedure for determining the disease follow-up and therapy

Advances in murine models of breast cancer bone disease

Bone is the most prevalent metastatic site for breast cancer affecting ~70% of patients with late-stage disease. Treatments for this condition currently focus on controlling disease progression and limiting tumour-induced damage to bone, thereby playing a valuable role in increasing quality of life. However, limited understanding of the interplay between tumour cells and their environment during bone metastasis has impeded the development of curative treatments. To unravel the complex genetic and phenotypic alterations that occur during this process, it would be helpful to have a model in which tumours develop spontaneously at the primary site, spread to bone, undergo a dormancy phase and then, after a fixed timeframe, become re-activated to form osteolytic/mixed lesions in the skeleton. Unlike humans, spontaneous metastasis of primary mammary tumours to bone is rare in mice and no syngeneic models of oestrogen receptor positive disease have been reported. As there is no single model that authentically reproduces all of the genetic and phenotypic changes representative of human bone metastasis, this review discusses the traditional and novel mouse models that are used to study bone metastasis from breast cancer. Additionally, this review focuses on advances that have been made towards making these models more closely related to human disease in an attempt to help researchers select the correct model(s) for their experimental needs with the aim of improving translational efficacy between the laboratory and the clinic