Machine learning and feature selection methods for egfr mutation status prediction in lung cancer

The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.This work is financed by the ERDF—European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation—COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263

Comprehensive perspective for lung cancer characterisation based on AI solutions using CT images

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.This work is financed by the ERDF–European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation–COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT–Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263

EGFR Assessment in Lung Cancer CT Images: Analysis of Local and Holistic Regions of Interest Using Deep Unsupervised Transfer Learning

Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A Convolutional Autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification.The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health for the free publicly available LIDC-IDRI Database used in this work. They also acknowledge The Cancer Imaging Archive (TCIA) for the open-access NSCLC-Radiogenomics dataset publicly available. This work was supported in part by the European Regional Development Fund (ERDF) through the Operational Program for Competitiveness and Internationalization—COMPETE 2020 Program, and in part by the National Funds through the Portuguese Funding Agency, Fundação para a Ciência e a Tecnologia (FCT), under Project POCI-01-0145-FEDER-030263

Effectiveness, cost-effectiveness and cost-benefit of a single annual professional intervention for the prevention of childhood dental caries in a remote rural Indigenous community

Background The aim of the study is to reduce the high prevalence of tooth decay in children in a remote, rural Indigenous community in Australia, by application of a single annual dental preventive intervention. The study seeks to (1) assess the effectiveness of an annual oral health preventive intervention in slowing the incidence of dental caries in children in this community, (2) identify the mediating role of known risk factors for dental caries and (3) assess the cost-effectiveness and cost-benefit of the intervention. Methods/design The intervention is novel in that most dental preventive interventions require regular re-application, which is not possible in resource constrained communities. While tooth decay is preventable, self-care and healthy habits are lacking in these communities, placing more emphasis on health services to deliver an effective dental preventive intervention. Importantly, the study will assess cost-benefit and cost-effectiveness for broader implementation across similar communities in Australia and internationally. Discussion There is an urgent need to reduce the burden of dental decay in these communities, by implementing effective, cost-effective, feasible and sustainable dental prevention programs. Expected outcomes of this study include improved oral and general health of children within the community; an understanding of the costs associated with the intervention provided, and its comparison with the costs of allowing new lesions to develop, with associated treatment costs. Findings should be generalisable to similar communities around the world. The research is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR), registration number ACTRN12615000693527; date of registration: 3rd July 2015

Needle stick injuries among dental students: risk factors and recommendations for prevention

Aim: To evaluate the risk factors of needle stick injuries (NSIs) sustained by undergraduate dental students and nurse students at the King's College London (KCL) Dental Institute. Materials and methods: A retrospective study evaluated the incident reports relating to NSIs reported over a period of 2 years. Factors including the dental department, study year, and when the injury took place during administration of local anaesthesia (LA) and recapping conventional syringe or clearing work surface or during disposal. Results: This report showed that students are at the highest risk of NSIs at the fourth year of their 5-year BDS course. About one-third of injuries were reported among this group of students followed by year 5 students (25%). Oral surgery clinics were the major source of incident reporting when compared with other specialised dental clinics within the institute. The left hands of the students were the most frequently affected by such injuries and then the right hands of student dental nurses. The attempt of needle recapping of conventional syringes was the least reported mechanism of injuries and constituted only 15% of the total injuries and mainly occurred in third year students. The most frequent injuries among student nurses were during disposal of the needle. Conclusion: Less NSIs occur when using safety syringes. A non-recapping policy with immediate disposal of either the conventional or safety syringe systems after injection would prevent all clearance-related NSIs sustained by nurses. To avoid NSIs, education plays a vital role particularly with effective implementation of the change to safety syringes with appropriate training

An association between anti-platelet drug use and reduced cancer prevalence in diabetic patients: results from the Vermont Diabetes Information System Study

<p>Abstract</p> <p>Background</p> <p>Diabetes is associated with an increased risk of several malignancies. Both diabetic patients and patients with cancer have an increase in platelet reactivity and platelet activation has recently emerged as a potential mediator of cancer progression. Drug therapies, such as aspirin, that reduce platelet reactivity reduce both cardiovascular and cancer risk.</p> <p>Methods</p> <p>We performed a cross-sectional analysis to assess the association between history of cancer and current anti-platelet drug use in a primary care population of adults with diabetes enrolled in the Vermont Diabetes Information System.</p> <p>Results</p> <p>Self-reported characteristics, medical history, and a complete medication list were recorded on 1007 diabetic adults. Fifty percent of diabetic patients used an anti-platelet drug. In unadjusted analysis, no association was seen between anti-platelet drug use and cancer history (OR = 0.93; <it>P </it>= .70). Platelet inhibitor use was associated with a decreased patient-reported history of malignancy in a multivariate logistic regression adjusted for age, sex, body mass index, comorbidity, and number of medications (OR = 0.66; CI 0.44-0.99; <it>P </it>= .045). Similar odds of association were seen in both males and females, and for aspirin and non-aspirin platelet inhibitor therapy.</p> <p>Conclusions</p> <p>Our data suggest an association between anti-platelet drug use and reduced cancer prevalence in patients with diabetes. Given the potentially large implications of our observations in the diabetic population, further studies are required to determine if this association is causal.</p

Consultations with complementary and alternative medicine practitioners amongst wider care options for back pain: a study of a nationally representative sample of 1,310 Australian women aged 60–65 years

Back pain is a significant health service issue in Australia and internationally. Back pain sufferers can draw upon a range of health care providers including complementary and alternative medicine (CAM) practitioners. Women are higher users of health services than men and tend to use CAM frequently for musculoskeletal conditions. However, there remain important gaps in our understanding of women's consultation patterns with CAM practitioners for back pain. The objective of this study is to examine the prevalence of use and characteristics of women who use CAM practitioners for back pain. The method used was a survey of a nationally representative sample of women aged 60-65 years from the Australian Longitudinal Study on Women's Health. Women consulted a massage therapist (44.1 %, n = 578) and a chiropractor (37.3 %, n = 488) more than other CAM practitioners for their back pain. Consultations with a chiropractor for back pain were lower for women who consulted a General Practitioner (GP) (OR, 0.56; 95 % CI 0.41, 0.76) or a physiotherapist (OR, 0.53; 95 % CI 0.39, 0.72) than for those who did not consult a GP or a physiotherapist. CAM practitioner consultations for back pain were greater for women who visited a pharmacist (OR, 1.99; 95 % CI 1.23, 3.32) than for women who did not visit a pharmacist. There is substantial use of CAM practitioners alongside conventional practitioners amongst women for back pain, and there is a need to provide detailed examination of the communication between patients and their providers as well as across the diverse range of health professionals involved in back pain care