Role of deep learning techniques in non-invasive diagnosis of human diseases.

Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than ever due to the increase in medical data being acquired, the presence of novel modalities being developed and the complexity of medical data. In all of these scenarios, machine learning can come up with new tools for interpreting the complex datasets that confront clinicians. Much of the excitement for the application of machine learning to biomedical research comes from the development of deep learning which is modeled after computation in the brain. Deep learning can help in attaining insights that would be impossible to obtain through manual analysis. Deep learning algorithms and in particular convolutional neural networks are different from traditional machine learning approaches. Deep learning algorithms are known by their ability to learn complex representations to enhance pattern recognition from raw data. On the other hand, traditional machine learning requires human engineering and domain expertise to design feature extractors and structure data. With increasing demands upon current radiologists, there are growing needs for automating the diagnosis. This is a concern that deep learning is able to address. In this dissertation, we present four different successful applications of deep learning for diseases diagnosis. All the work presented in the dissertation utilizes medical images. In the first application, we introduce a deep-learning based computer-aided diagnostic system for the early detection of acute renal transplant rejection. The system is based on the fusion of both imaging markers (apparent diffusion coefficients derived from diffusion-weighted magnetic resonance imaging) and clinical biomarkers (creatinine clearance and serum plasma creatinine). The fused data is then used as an input to train and test a convolutional neural network based classifier. The proposed system is tested on scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. In the second application, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aimed at achieving lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Using fully convolutional neural networks, we proposed novel methods for the extraction of a region of interest that contains the left ventricle, and the segmentation of the left ventricle. Following myocardial segmentation, functional and mass parameters of the left ventricle are estimated. Automated Cardiac Diagnosis Challenge dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. In the third application, we propose a novel deep learning approach for automated quantification of strain from cardiac cine MR images of mice. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. In the fourth application, we demonstrate how a deep learning approach can be utilized for the automated classification of kidney histopathological images. Our approach can classify four classes: the fat, the parenchyma, the clear cell renal cell carcinoma, and the unusual cancer which has been discovered recently, called clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole-slide kidney images were divided into patches with three different sizes to be inputted to the networks. Our approach can provide patch-wise and pixel-wise classification. Our approach classified the four classes accurately and surpassed other state-of-the-art methods such as ResNet (pixel accuracy: 0.89 Resnet18, 0.93 proposed). In conclusion, the results of our proposed systems demonstrate the potential of deep learning for the efficient, reproducible, fast, and affordable disease diagnosis

Oxycodone for cancer‐related pain

Background: Many patients with cancer experience moderate to severe pain that requires treatment with strong opioids, of which oxycodone and morphine are examples. Strong opioids are, however, not effective for pain in all patients, nor are they well-tolerated by all patients. The aim of this review was to assess whether oxycodone is associated with better pain relief and tolerability than other analgesic options for patients with cancer pain. Objectives: To assess the effectiveness and tolerability of oxycodone for pain in adults with cancer. Search methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library, MEDLINE and MEDLINE In-Process (Ovid), EMBASE (Ovid), Science Citation Index, Conference Proceedings Citation Index - Science (ISI Web of Science), BIOSIS (ISI), PsycINFO (Ovid) and PubMed to March 2014. We also searched Clinicaltrials.gov, metaRegister of Controlled Trials (mRCT), EU Clinical Trials Register and World Health Organization International Clinical Trials Registry Platform (ICTRP). We checked the bibliographic references of relevant identified studies and contacted the authors of the included studies to find additional trials not identified by the electronic searches. No language, date or publication status restrictions were applied to the search. Selection criteria: We included randomised controlled trials (parallel-group or cross-over) comparing oxycodone (any formulation or route of administration) with placebo or an active drug (including oxycodone) for cancer background pain in adults. Data collection and analysis: Two authors independently extracted study data (study design, participant details, interventions and outcomes) and independently assessed the quality of the included studies according to standard Cochrane methodology. Where possible, we meta-analysed the pain intensity data using the generic inverse variance method, otherwise these data were summarised narratively along with the adverse event and patient preference data. The overall quality of the evidence for each outcome was assessed according to the GRADE approach. Main results: We included 17 studies which enrolled/randomised 1390 patients with 1110 of these analysed for efficacy and 1170 for safety. The studies examined a number of different drug comparisons. Four studies compared controlled release (CR) oxycodone to immediate release (IR) oxycodone and pooled analysis of three of these studies showed that the effects of CR and IR oxycodone on pain intensity after treatment were similar (standardised mean difference (SMD) 0.1, 95% confidence interval (CI) -0.06 to 0.26; low quality evidence). This was in line with the finding that none of the included studies reported differences in pain intensity between the treatment groups. Three of the four studies also found similar results for treatment acceptability and adverse events in the IR and CR groups; but one study reported that, compared to IR oxycodone, CR oxycodone was associated with significantly fewer adverse events. Six studies compared CR oxycodone to CR morphine and pooled analysis of five of these studies indicated that pain intensity did not differ significantly between the treatments (SMD 0.14, 95% CI -0.04 to 0.32; low quality evidence). There were no marked differences in adverse event rates, treatment acceptability or quality of life ratings. The remaining seven studies either compared oxycodone in various formulations or compared oxycodone to different alternative opioids. None of them found any clear superiority or inferiority of oxycodone for cancer pain, neither as an analgesic agent nor in terms of adverse event rates and treatment acceptability. The quality of this evidence base was limited by the risk of bias of the studies and by small sample sizes for many outcomes. Random sequence generation and allocation concealment were under-reported, and the results were substantially compromised by attrition with data missing from more than 20% of the enrolled/randomised patients for efficacy and from more than 15% for safety. Authors' conclusions: Overall, the data included within this review suggest that oxycodone offers similar levels of pain relief and adverse events to other strong opioids including morphine, which is commonly considered the gold standard strong opioid. Our conclusions are consistent with other recent reviews and suggest that while the reliability of the evidence base is low, given the absence of important differences within this analysis it seems unlikely that larger head to head studies of oxycodone versus morphine will be justified. This means that for clinical purposes oxycodone or morphine can be used as first line oral opioids for relief of cancer pain

Integration of oncology and palliative care : a Lancet Oncology Commission

Full integration of oncology and palliative care relies on the specific knowledge and skills of two modes of care: the tumour-directed approach, the main focus of which is on treating the disease; and the host-directed approach, which focuses on the patient with the disease. This Commission addresses how to combine these two paradigms to achieve the best outcome of patient care. Randomised clinical trials on integration of oncology and palliative care point to health gains: improved survival and symptom control, less anxiety and depression, reduced use of futile chemotherapy at the end of life, improved family satisfaction and quality of life, and improved use of health-care resources. Early delivery of patient-directed care by specialist palliative care teams alongside tumour-directed treatment promotes patient-centred care. Systematic assessment and use of patient-reported outcomes and active patient involvement in the decisions about cancer care result in better symptom control, improved physical and mental health, and better use of health-care resources. The absence of international agreements on the content and standards of the organisation, education, and research of palliative care in oncology are major barriers to successful integration. Other barriers include the common misconception that palliative care is end-of-life care only, stigmatisation of death and dying, and insufficient infrastructure and funding. The absence of established priorities might also hinder integration more widely. This Commission proposes the use of standardised care pathways and multidisciplinary teams to promote integration of oncology and palliative care, and calls for changes at the system level to coordinate the activities of professionals, and for the development and implementation of new and improved education programmes, with the overall goal of improving patient care. Integration raises new research questions, all of which contribute to improved clinical care. When and how should palliative care be delivered? What is the optimal model for integrated care? What is the biological and clinical effect of living with advanced cancer for years after diagnosis? Successful integration must challenge the dualistic perspective of either the tumour or the host, and instead focus on a merged approach that places the patient's perspective at the centre. To succeed, integration must be anchored by management and policy makers at all levels of health care, followed by adequate resource allocation, a willingness to prioritise goals and needs, and sustained enthusiasm to help generate support for better integration. This integrated model must be reflected in international and national cancer plans, and be followed by developments of new care models, education and research programmes, all of which should be adapted to the specific cultural contexts within which they are situated. Patient-centred care should be an integrated part of oncology care independent of patient prognosis and treatment intention. To achieve this goal it must be based on changes in professional cultures and priorities in health care

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Impact of Early Do Not Resuscitate Order (DNR) on Length of Hospitalization, Intensive Care Unit Admission, and Hospital Mortality for Advanced Lung and Gastrointestinal Cancers

Background National guidelines recommend documenting goals of care for patients with terminal conditions in their medical records within 3 days of hospitalization. However, no studies have evaluated the impact of such a measure. We examined the association of early Do Not Resuscitate (DNR) within 3 days of presentation to an emergency department (ED) on hospital length of stay, intensive care unit (ICU) admission, death in the hospital, and hospice referral. Methods We searched MD Anderson patient databases for lung and gastrointestinal cancer patients who visited our ED in 2013, and had a DNR order written. Demographic information, cancer stage, ICU admissions, hospital and ICU length of stay, and hospital deaths were reviewed. Patients with early and late DNR were compared to the outcome variables of length of stay, ICU admission, hospital death, and hospice referral. Results Of the 645 patients with lung and GI cancer who visited the ED and had a DNR order, 613 (95%) were admitted to the hospital. The median time to DNR order was 1 day for early DNR patient compared to 7.7 days for late DNR (p\u3c0.001). More elderly patients had early DNR. Of all patients admitted, 164 (27%) were admitted from ED to ICU. Death in the hospital occurred in 41.5% of patients, and an equal number were discharged to hospice. Patients with early DNR had significantly shorter hospitalization compared to those with late DNR (5 vs 11; p \u3c0.00). In a linear regression, significant factors associated with prolonged length of stay include late DNR, total ICU days, and death in the palliative care unit. In a logistic regression, factors associated with increased hospital death include total days spent in ICU, death in the palliative care unit and lung cancer, and for hospice referral the only negative association were days spent in ICU. Conclusion Early DNR is associated with significantly shorter hospitalization. Days spent in ICU are strongly associated with all outcome variables. There were no differences in hospital death or hospice referral