METHODS: Subjects aged 55 years and above from the Malaysian Elders Longitudinal Research (MELoR) study with available information on vision and Montreal Cognitive Assessment (MoCA) scores were included. Data were obtained through a home-based interview and hospital-based health check by trained researchers. Visual acuity (VA) was assessed with logMAR score with vision impairment defined as VA 6/18 or worse in the better-seeing eye. Cognition was evaluated using the MoCA-Blind scoring procedure. Those with a MoCA-Blind score of <19/22 were considered to have cognitive impairment.
RESULTS: Data was available for 1144 participants, mean (SD) age = 68.57 (±7.23) years. Vision impairment was present in 143 (12.5 %) and 758 (66.3 %) had MoCA-Blind score of <19. Subjects with vision impairment were less likely to have a MoCA-Blind score of ≥19 (16.8 % vs 36.2 %, p < 0.001). Vision impairment was associated with poorer MoCA-Blind scores after adjustments for age, gender, and ethnicity (β = 2.064; 95 % CI, -1.282 to 3.320; P = 0.003). In those who had > 6 years of education attainment, vision impairment was associated with a significant reduction of cognitive function and remained so after adjustment for age and gender (β = 1.863; 95 % CI, 1.081-3.209; P = 0.025).
CONCLUSION: Our results suggest that vision impairment correlates with cognitive decline. Therefore, maintaining good vision is an important interventional strategy for preventing cognitive decline in older adults.
METHODS: A systematic electronic literature search (Scopus, MEDLINE, ProQuest, CINAHL, Cochrane and a grey literature specific site through Google Scholar) was undertaken between March and May 2018 (search updated in June 2019). Studies were selected based on predetermined criteria. Data relating to the contents and parameters of the SMEP were extracted and collated.
RESULTS: A total of 11 experimental studies met the inclusion criteria. Overall quality of the selected studies was good. The contents used for SMEP in older adults with KOA were information and management of KOA, healthy lifestyle and additional management strategies for KOA. The parameters used were face-to-face sessions led by health professionals and were chiefly group-based.
CONCLUSION: This review comprehensively summarises the structure of multifaceted SMEP for people with KOA, which could be used to inform clinical practice and future research.
PATIENTS AND METHODS: We established a multi-national, longitudinal, observational registry of patients with prostate cancer presenting to participating tertiary care hospitals in eight Asian countries. A total of 3636 eligible patients with existing or newly diagnosed high-risk localised prostate cancer (HRL), non-metastatic biochemically recurrent prostate cancer (M0), or metastatic prostate cancer (M1), were consecutively enrolled and are being followed-up for 5 years. Patient history, demographic and disease characteristics, treatment and treatment decisions, were collected at first prostate cancer diagnosis and at enrolment. Patient-reported quality of life was prospectively assessed using the European Quality of Life-five Dimensions, five Levels (EQ-5D-5L) and Functional Assessment of Cancer Therapy for Prostate Cancer questionnaires. In the present study, we report the first interim analysis of 2063 patients enrolled from study start (15 September 2015) until 18 May 2017.
RESULTS: Of the 2063 enrolled patients, 357 (17%), 378 (19%), and 1328 (64%) had HRL, M0 or M1 prostate cancer, respectively. The mean age at first diagnosis was similar in each group, 56% of all patients had extracapsular extension of their tumour, 28% had regional lymph node metastasis, and 53% had distant metastases. At enrolment, 62% of patients had at least one co-morbidity (mainly cardiovascular disease or diabetes), 91.8% of M1 patients had an Eastern Cooperative Oncology Group performance score of <2 and the mean EQ-5D-5L visual analogue score was 74.6-79.6 across cohorts. Treatment of M1 patients was primarily with combined androgen blockade (58%) or androgen-deprivation therapy (either orchidectomy or luteinising hormone-releasing hormone analogues) (32%). Decisions to start therapy were mainly driven by treatment guidelines and disease progression. Decision to discontinue therapy was most often due to disease progression (hormonal drug therapy) or completion of therapy (chemotherapy).
CONCLUSION: In the UFO registry of advanced prostate cancer in Asia, regional differences exist in prostate cancer treatment patterns that will be explored more deeply during the follow-up period; prospective follow-up is ongoing. The UFO registry will provide valuable descriptive data on current disease characteristics and treatment landscape amongst patients with prostate cancer in Asia.
METHODS AND ANALYSIS: The measurement challenge has been established as an international resource to offer a common set of anonymised mammogram images for measurement and analysis. To date, full field digital mammogram images and core data from 1650 cases and 1929 controls from five countries have been collated. The measurement challenge is an ongoing collaboration and we are continuing to expand the resource to include additional image sets across different populations (from contributors) and to compare additional measurement methods (by challengers). The intended use of the measurement challenge resource is for refinement and validation of new and existing mammographic measurement methods. The measurement challenge resource provides a standardised dataset of mammographic images and core data that enables investigators to directly compare methods of measuring mammographic density or other mammographic features in case/control sets of both raw and processed images, for the purposes of the comparing their predictions of breast cancer risk.
ETHICS AND DISSEMINATION: Challengers and contributors are required to enter a Research Collaboration Agreement with the University of Melbourne prior to participation in the measurement challenge. The Challenge database of collated data and images are stored in a secure data repository at the University of Melbourne. Ethics approval for the measurement challenge is held at University of Melbourne (HREC ID 0931343.3).
METHODS: We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method.
RESULTS: The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P
METHODS: We propose to use Residual Blocks with a 3 × 3 kernel size for local feature extraction and Non-Local Blocks to extract the global features. The Non-Local Block has the ability to extract global features without using a huge number of parameters. The key idea behind the Non-Local Block is to apply matrix multiplications between features on the same feature maps.
RESULTS: We trained and validated the proposed method on the LIDC-IDRI dataset which contains 1018 computed tomography scans. We followed a rigorous procedure for experimental setup, namely tenfold cross-validation, and ignored the nodules that had been annotated by
METHODS: We propose to use 3D Axial-Attention, which requires a fraction of the computing power of a regular Non-Local network (i.e., self-attention). Unlike a regular Non-Local network, the 3D Axial-Attention network applies the attention operation to each axis separately. Additionally, we solve the invariant position problem of the Non-Local network by proposing to add 3D positional encoding to shared embeddings.
RESULTS: We validated the proposed method on 442 benign nodules and 406 malignant nodules, extracted from the public LIDC-IDRI dataset by following a rigorous experimental setup using only nodules annotated by at least three radiologists. Our results show that the 3D Axial-Attention model achieves state-of-the-art performance on all evaluation metrics, including AUC and Accuracy.
CONCLUSIONS: The proposed model provides full 3D attention, whereby every element (i.e., pixel) in the 3D volume space attends to every other element in the nodule effectively. Thus, the 3D Axial-Attention network can be used in all layers without the need for local filters. The experimental results show the importance of full 3D attention for classifying lung nodules.