Long-term trends in sex difference in bladder cancer survival 1975-2009: A population-based study in Osaka, Japan.

In Japan, a study using population-based cancer registry data from six prefectures revealed a difference in bladder cancer survival between men and women. However, the period of the study was limited to 1993-2006. The recent introduction of immune checkpoint inhibitors, which have proved to be effective for the treatment for bladder cancer, has led to a rising demand for analysis of long-term trends in net survival in order to accurately assess the effect of the new treatment. The aim of the present study was to examine long-term trends in sex difference in bladder cancer net survival using large-scale population-based cancer registry data from Osaka, Japan (17,500 cases from 1975 to 2009). We also evaluated sex difference in bladder cancer survival after adjustment for stage, histologic type, and other prognostic factors. We showed the long-term trend of five-year net survival for each stage and found that women had poorer five-year net survival than men for the whole study period. The risk of death from bladder cancer was higher among men than women even after adjusting for period at diagnosis, histologic type, stage, age group, and treatment (Excess hazard ratios: 1.17; 95% Confidence interval: 1.10-1.25).

Automatic diagnosis of neurological diseases using MEG signals with a deep neural network.

The application of deep learning to neuroimaging big data will help develop computer-aided diagnosis of neurological diseases. Pattern recognition using deep learning can extract features of neuroimaging signals unique to various neurological diseases, leading to better diagnoses. In this study, we developed MNet, a novel deep neural network to classify multiple neurological diseases using resting-state magnetoencephalography (MEG) signals. We used the MEG signals of 67 healthy subjects, 26 patients with spinal cord injury, and 140 patients with epilepsy to train and test the network using 10-fold cross-validation. The trained MNet succeeded in classifying the healthy subjects and those with the two neurological diseases with an accuracy of 70.7 ± 10.6%, which significantly exceeded the accuracy of 63.4 ± 12.7% calculated from relative powers of six frequency bands (δ: 1-4 Hz; θ: 4-8 Hz; low-α: 8-10 Hz; high-α: 10-13 Hz; ß: 13-30 Hz; low-γ: 30-50 Hz) for each channel using a support vector machine as a classifier (p = 4.2 × 10-2). The specificity of classification for each disease ranged from 86-94%. Our results suggest that this technique would be useful for developing a classifier that will improve neurological diagnoses and allow high specificity in identifying diseases.

Evaluation of the default-mode network by quantitative 15O-PET: comparative study between cerebral blood flow and oxygen consumption.

OBJECTIVE: Resting-state functional MRI (rs-fMRI) has revealed the existence of a default-mode network (DMN) based on spontaneous oscillations of the blood oxygenation level-dependent (BOLD) signal. The BOLD signal reflects the deoxyhemoglobin concentration, which depends on the relationship between the regional cerebral blood flow (CBF) and the cerebral metabolic rate of oxygen (CMRO2). However, these two factors cannot be separated in BOLD rs-fMRI. In this study, we attempted to estimate the functional correlations in the DMN by means of quantitative 15O-labeled gases and water PET, and to compare the contribution of the CBF and CMRO2 to the DMN. METHODS: Nine healthy volunteers (5 men and 4 women; mean age, 47.0 ± 1.2 years) were studied by means of 15O-O2, 15O-CO gases and 15O-water PET. Quantitative CBF and CMRO2 images were generated by an autoradiographic method and transformed into MNI standardized brain template. Regions of interest were placed on normalized PET images according to the previous rs-fMRI study. For the functional correlation analysis, the intersubject Pearson's correlation coefficients (r) were calculated for all pairs in the brain regions and correlation matrices were obtained for CBF and CMRO2, respectively. We defined r > 0.7 as a significant positive correlation and compared the correlation matrices of CBF and CMRO2. RESULTS: Significant positive correlations (r > 0.7) were observed in 24 pairs of brain regions for the CBF and 22 pairs of brain regions for the CMRO2. Among them, 12 overlapping networks were observed between CBF and CMRO2. Correlation analysis of CBF led to the detection of more brain networks as compared to that of CMRO2, indicating that the CBF can capture the state of the spontaneous activity with a higher sensitivity. CONCLUSIONS: We estimated the functional correlations in the DMN by means of quantitative PET using 15O-labeled gases and water. The correlation matrix derived from the CBF revealed a larger number of brain networks as compared to that derived from the CMRO2, indicating that contribution to the functional correlation in the DMN is higher in the blood flow more than the oxygen consumption.