Optimized Swarm Enabled Deep Learning Technique for Bone Tumor Detection using Histopathological Image

Cancer subjugates a community that lacks proper care. It remains apparent that research studies enhance novel benchmarks in developing a computer-assisted tool for prognosis in radiology yet an indication of illness detection should be recognized by the pathologist. In bone cancer (BC), Identification of malignancy out of the BC’s histopathological image (HI) remains difficult because of the intricate structure of the bone tissue (BTe) specimen. This study proffers a new approach to diagnosing BC by feature extraction alongside classification employing deep learning frameworks. In this, the input is processed and segmented by Tsallis Entropy for noise elimination, image rescaling, and smoothening. The features are excerpted employing Efficient Net-based Convolutional Neural Network (CNN) Feature Extraction. ROI extraction will be employed to enhance the precise detection of atypical portions surrounding the affected area. Next, for classifying the accurate spotting and for grading the BTe as typical and a typical employing augmented XGBoost alongside Whale optimization (WOA). HIs gathering out of prevailing scales patients is acquired alongside texture characteristics of such images remaining employed for training and testing the Neural Network (NN). These classification outcomes exhibit that NN possesses a hit ratio of 99.48 percent while this occurs in BT classification

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

Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd

Development and validation of novel and quantitative MRI methods for cancer evaluation

Quantitative imaging biomarkers (QIB) offer the opportunity to further the evaluation of cancer at presentation as well as predict response to anti-cancer therapies before and early during treatment with the ultimate goal of truly personalised medical care and the mitigation of futile, often detrimental, therapy. Few QIBs are successfully translated into clinical practice and there is increasing recognition that rigorous methodologies and standardisation of research pipelines and techniques are required to move a theoretically useful biomarker into the clinic. To this end, I have aimed to give an overview of what I believe to be some of key elements within the research field beginning with the concept of imaging biomarkers, introducing concepts in development and validation, before providing a summary of the current and future utility of a range of quantitative MR imaging biomarkers techniques within the oncological imaging field. The original, prospective, research moves from the technical and analytical validation of a novel QIB use (T1 mapping in cancer), first in vivo qualification of this biomarker in cancer patient response assessment and prediction (sarcoma and breast cancer as well as prostate cancer separately), and then moving on to application of more established QIBs in cancer evaluation (R2*/BOLD imaging in head and neck cancer) as well as how existing MR data can be post-processed to improved cancer evaluation (further metrics derived from diffusion weighted imaging in head and neck cancer and textural analysis of existing clinical MR images utility in prostate cancer detection)

Patient-Specific Implants in Musculoskeletal (Orthopedic) Surgery

Most of the treatments in medicine are patient specific, aren’t they? So why should we bother with individualizing implants if we adapt our therapy to patients anyway? Looking at the neighboring field of oncologic treatment, you would not question the fact that individualization of tumor therapy with personalized antibodies has led to the thriving of this field in terms of success in patient survival and positive responses to alternatives for conventional treatments. Regarding the latest cutting-edge developments in orthopedic surgery and biotechnology, including new imaging techniques and 3D-printing of bone substitutes as well as implants, we do have an armamentarium available to stimulate the race for innovation in medicine. This Special Issue of Journal of Personalized Medicine will gather all relevant new and developed techniques already in clinical practice. Examples include the developments in revision arthroplasty and tumor (pelvic replacement) surgery to recreate individual defects, individualized implants for primary arthroplasty to establish physiological joint kinematics, and personalized implants in fracture treatment, to name but a few

Studying the origins of primary tumours and residual disease in breast cancer

Breast cancer is the leading cause of death in women worldwide and these deaths are mostly attributed to metastasis and tumour recurrence following initially successful therapy. Metastasis refers to the development of invasive disease, wherein malignant cells dissociate from primary tumours, infiltrating other organs and tissues to give rise to secondary outgrowths. Previously, metastasis was thought to be initiated in advanced tumours, but breast cancer cellsh with metastatic potential have now been shown to disseminate very early from the primary site via largely unknown mechanisms. These early interactions of tumour cells with their cellular micro-environment and normal neighbours also results in early tumour cell heterogeneity and must therefore be elucidated such that we can prevent metastatic spread in the patient situation and better treat the resulting heterogenous tumours. However, studying tumour initiation is not possible in patients because it happens on a cellular level not detectable by current technology. Tumour recurrence is another major cause of breast cancer related death and is believed to be caused by residual disease cells that survive initial therapy. These are a reservoir of refractory cells that can lay dormant for many years (sometimes decades) before resulting in relapse tumours. They are also difficult to obtain from human patients, since they are very few and cannot be detected easily, and thus their molecular mechanisms have not been fully explored. In addition to the unavailability of human tissue, mouse models of breast cancer also fall short in helping us study early cancer initiation, because they allow oncogenic expression in all cells of the tissue instead of initiating cancer like in the human situation|one neoplastic transformed cell proliferating unchecked in a normal epithelium. To address this issue, we used primary organoids from an inducible mouse model of breast cancer and lentivirally transduced single cells within these organoids to express oncogenes. We further optimized parameters for long term imaging using light sheet microscopy and developed big data analysis pipelines that lead us to discern that single transformed cells had a lower chance at establishing tumorigenic foci, when compared to clusters of cells. Thus, we postulate a proximity-controlled signalling that is imperative to tumour initiation within epithelial tissues using the first ever in vitro stochastic breast tumorigenesis model system. This new stochastic tumorigenesis system can be further used to identify the molecular interactions in the early breast cancer cells. Our group has already revealed distinct characteristics, such as dysregulated lipid metabolism, of the residual disease correlate obtained from an inducible mouse model. As survival mechanisms invoked by residual cells remain largely unknown, we analysed the dynamic transcriptome of regressing tumours at important timepoints during the establishment of residual disease. Key molecular players upregulated during regression {like c-Jun and BCL6 { were identified and the inflammatory arm of the Nf-kB cascade was found to be dysregulated among others. Further validation of these molecular targets as potentially synthetic lethal interactors remains to be performed so that they can be used to limit the residual disease reservoir and eventually tumour recurrence

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Purpose of Review This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging. Recent Findings Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases. Summary The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management

Diagnosis and prognosis of cardiovascular diseases by means of texture analysis in magnetic resonance imaging

Cardiovascular diseases constitute the leading global cause of morbidity and mortality. Magnetic resonance imaging (MRI) has become the gold standard technique for the assessment of patients with myocardial infarction. However, limitations still exist thus new alternatives are open to investigation. Texture analysis is a technique that aims to quantify the texture of the images that are not always perceptible by the human eye. It has been successfully applied in medical imaging but applications to cardiac MRI (CMR) are still scarce. Therefore, the purpose of this thesis was to apply texture analysis in conventional CMR images for the assessment of patients with myocardial infarction, as an alternative to current methods. Three applications of texture analysis and machine learning techniques were studied: i) Detection of infarcted myocardium in late gadolinium enhancement (LGE) CMR. Segmentation of the infarcted myocardium is routinely performed using image intensity thresholds. The inclusion of texture features to aid the segmentation was analyzed obtaining overall good results. The method was developed using 10 LGE CMR datasets and tested on a separate dataset comprising 5 cases that were acquired with a completely different scanner than that used for training. Therefore, this preliminary study showed the transferability of texture analysis which is important for clinical applicability. ii) Differentiation of acute and chronic myocardial infarction using LGE CMR and standard pre-contrast cine CMR. In this study, two different feature selection techniques and six different machine learning classifiers were studied and compared. The best classification was achieved using a polynomial SVM obtaining an overall AUC of 0.87 ± 0.06 in LGE CMR. Interestingly, results on cine CMR in which infarctions are visually imperceptible in most cases were also good (AUC = 0.83 ± 0.08). iii) Detection of infarcted non-viable segments in cine CMR. This study was motivated by the findings of the previous one. It demonstrated that texture analysis can be used to distinguish non-viable, viable and remote segments using standard pre-contrast cine CMR solely. This was the most relevant contribution of this thesis as it can be used as hypothesis for future work aiming to accurately delineate the infarcted myocardium as a gadolinium-free alternative that will have potential advantages. The three proposed applications were successfully performed obtaining promising results. In conclusion, texture analysis can be successfully applied to conventional CMR images and provides a potential quantitative alternative to existing methods