Resting-state networks representation of the global phenomena

Resting-state functional magnetic resonance imaging (rsfMRI) has been widely applied to investigate spontaneous neural activity, often based on its macroscopic organization that is termed resting-state networks (RSNs). Although the neurophysiological mechanisms underlying the RSN organization remain largely unknown, accumulating evidence points to a substantial contribution from the global signals to their structured synchronization. This study further explored the phenomenon by taking advantage of the inter- and intra-subject variations of the time delay and correlation coefficient of the signal timeseries in each region using the global mean signal as the reference signal. Consistent with the hypothesis based on the empirical and theoretical findings, the time lag and correlation, which have consistently been proven to represent local hemodynamic status, were shown to organize networks equivalent to RSNs. The results not only provide further evidence that the local hemodynamic status could be the direct source of the RSNs’ spatial patterns but also explain how the regional variations in the hemodynamics, combined with the changes in the global events’ power spectrum, lead to the observations. While the findings pose challenges to interpretations of rsfMRI studies, they further support the view that rsfMRI can offer detailed information related to global neurophysiological phenomena as well as local hemodynamics that would have great potential as biomarkers

Synthetic data generation method for hybrid image-tabular data using two generative adversarial networks

The generation of synthetic medical records using generative adversarial networks (GANs) has become increasingly important for addressing privacy concerns and promoting data sharing in the medical field. In this paper, we propose a novel method for generating synthetic hybrid medical records consisting of chest X-ray images (CXRs) and structured tabular data (including anthropometric data and laboratory tests) using an auto-encoding GAN ({\alpha}GAN) and a conditional tabular GAN (CTGAN). Our approach involves training a {\alpha}GAN model on a large public database (pDB) to reduce the dimensionality of CXRs. We then applied the trained encoder of the GAN model to the images in original database (oDB) to obtain the latent vectors. These latent vectors were combined with tabular data in oDB, and these joint data were used to train the CTGAN model. We successfully generated diverse synthetic records of hybrid CXR and tabular data, maintaining correspondence between them. We evaluated this synthetic database (sDB) through visual assessment, distribution of interrecord distances, and classification tasks. Our evaluation results showed that the sDB captured the features of the oDB while maintaining the correspondence between the images and tabular data. Although our approach relies on the availability of a large-scale pDB containing a substantial number of images with the same modality and imaging region as those in the oDB, this method has the potential for the public release of synthetic datasets without compromising the secondary use of data.Comment: 14 page

Cone-beam CT reconstruction for non-periodic organ motion using time-ordered chain graph model

Purpose: The purpose of this study is to introduce the new concept of a four-dimensional (4D) cone-beam computed tomography (CBCT) reconstruction approach for non-periodic organ motion in cooperation with the time-ordered chain graph model (TCGM) and to compare it with previously developed methods such as total variation-based compressed sensing (TVCS) and prior-image constrained compressed sensing (PICCS). Materials and Methods: Our proposed reconstruction is based on a model including the constraint originating from the images of neighboring time phases. Namely, the reconstructed time-series images depend on each other in this TCGM scheme, and the time-ordered images are concurrently reconstructed in the iterative reconstruction approach. In this study, iterative reconstruction with the TCGM was carried out with 90◦ projection ranges. The images reconstructed by the TCGM were compared with the images reconstructed by TVCS (200◦ projection ranges) and PICCS (90◦ projection ranges). Two kinds of projection data sets–an elliptic-cylindrical digital phantom and two clinical patients’ data–were used. For the digital phantom, an air sphere was contained and virtually moved along the longitudinal axis by 3 cm/30 s and 3 cm/60 s; the temporal resolution was evaluated by measuring the penumbral width of the air sphere. The clinical feasibility of the non-periodic time-ordered 4D CBCT image reconstruction was examined with the patient data in the pelvic region. Results: In the evaluation of the digital-phantom reconstruction, the penumbral widths of the TCGM yielded the narrowest result; the results obtained by PICCS and TCGM using 90◦ projection ranges were 2.8% and 18.2% for 3 cm/30 s, and 5.0% and 23.1% for 3 cm/60 s narrower than that of TVCS using 200◦ projection ranges. This suggests that the TCGM has a better temporal resolution, whereas PICCS seems similar to TVCS. These reconstruction methods were also compared using patients’ projection data sets. Although all three reconstruction results showed motion related to rectal gas or stool, the result obtained by the TCGM was visibly clearer with less blurring. Conclusion: The TCGM is a feasible approach to visualize non-periodic organ motion. The digital-phantom results demonstrated that the proposed method provides 4D image series with a better temporal resolution compared to TVCS and PICCS. The clinical patients’ results also showed that the present method enables us to visualize motion related to rectal gas and flatus in the rectum

イヨウガゾウニオケルカイボウガクテキランドマークノケンシュツ、テイギトソノオウヨウ

博士（工学）東京農工大

Predicting Breast Cancer Risk Using Radiomics Features of Mammography Images

Mammography images contain a lot of information about not only the mammary glands but also the skin, adipose tissue, and stroma, which may reflect the risk of developing breast cancer. We aimed to establish a method to predict breast cancer risk using radiomics features of mammography images and to enable further examinations and prophylactic treatment to reduce breast cancer mortality. We used mammography images of 4000 women with breast cancer and 1000 healthy women from the ‘starting point set’ of the OPTIMAM dataset, a public dataset. We trained a Light Gradient Boosting Machine using radiomics features extracted from mammography images of women with breast cancer (only the healthy side) and healthy women. This model was a binary classifier that could discriminate whether a given mammography image was of the contralateral side of women with breast cancer or not, and its performance was evaluated using five-fold cross-validation. The average area under the curve for five folds was 0.60122. Some radiomics features, such as ‘wavelet-H_glcm_Correlation’ and ‘wavelet-H_firstorder_Maximum’, showed distribution differences between the malignant and normal groups. Therefore, a single radiomics feature might reflect the breast cancer risk. The odds ratio of breast cancer incidence was 7.38 in women whose estimated malignancy probability was ≥0.95. Radiomics features from mammography images can help predict breast cancer risk

NTCIR-17 MedNLP-SC Radiology Report Subtask Overview: Dataset and Solutions for Automated Lung Cancer Staging

This paper describes the Radiology Report TNM staging (RR-TNM) subtask as a part of NTCIR-17 Medical Natural Language Processing for Social Media and Clinical Texts (MedNLP-SC) shared task in 2023. This subtask focused on automated lung cancer staging based on radiology reports. We created a dataset of 243 Japanese radiology reports containing no personal health information. A total of three teams with 16 members participated and submitted seven solutions. The best accuracy scores for the T, N, and M categories reached 67%, 80%, and 93%, respectively. Through the RR- TNM subtask, we have provided a valuable open Japanese clinical corpus and useful insights to apply natural language processing for secondary usage of staging information

Impact of CT-determined low kidney volume on renal function decline: a propensity score-matched analysis

Abstract Objectives To investigate the relationship between low kidney volume and subsequent estimated glomerular filtration rate (eGFR) decline in eGFR category G2 (60–89 mL/min/1.73 m2) population. Methods In this retrospective study, we evaluated 5531 individuals with eGFR category G2 who underwent medical checkups at our institution between November 2006 and October 2017. Exclusion criteria were absent for follow-up visit, missing data, prior renal surgery, current renal disease under treatment, large renal masses, and horseshoe kidney. We developed a 3D U-net-based automated system for renal volumetry on CT images. Participants were grouped by sex-specific kidney volume deviations set at mean minus one standard deviation. After 1:1 propensity score matching, we obtained 397 pairs of individuals in the low kidney volume (LKV) and control groups. The primary endpoint was progression of eGFR categories within 5 years, assessed using Cox regression analysis. Results This study included 3220 individuals (mean age, 60.0 ± 9.7 years; men, n = 2209). The kidney volume was 404.6 ± 67.1 and 376.8 ± 68.0 cm3 in men and women, respectively. The low kidney volume (LKV) cutoff was 337.5 and 308.8 cm3 for men and women, respectively. LKV was a significant risk factor for the endpoint with an adjusted hazard ratio of 1.64 (95% confidence interval: 1.09–2.45; p = 0.02). Conclusion Low kidney volume may adversely affect subsequent eGFR maintenance; hence, the use of imaging metrics may help predict eGFR decline. Critical relevance statement Low kidney volume is a significant predictor of reduced kidney function over time; thus, kidney volume measurements could aid in early identification of individuals at risk for declining kidney health. Key points • This study explores how kidney volume affects subsequent kidney function maintenance. • Low kidney volume was associated with estimated glomerular filtration rate decreases. • Low kidney volume is a prognostic indicator of estimated glomerular filtration rate decline. Graphical Abstrac

Data_Sheet_1_Resting-state networks representation of the global phenomena.docx

Resting-state functional magnetic resonance imaging (rsfMRI) has been widely applied to investigate spontaneous neural activity, often based on its macroscopic organization that is termed resting-state networks (RSNs). Although the neurophysiological mechanisms underlying the RSN organization remain largely unknown, accumulating evidence points to a substantial contribution from the global signals to their structured synchronization. This study further explored the phenomenon by taking advantage of the inter- and intra-subject variations of the time delay and correlation coefficient of the signal timeseries in each region using the global mean signal as the reference signal. Consistent with the hypothesis based on the empirical and theoretical findings, the time lag and correlation, which have consistently been proven to represent local hemodynamic status, were shown to organize networks equivalent to RSNs. The results not only provide further evidence that the local hemodynamic status could be the direct source of the RSNs’ spatial patterns but also explain how the regional variations in the hemodynamics, combined with the changes in the global events’ power spectrum, lead to the observations. While the findings pose challenges to interpretations of rsfMRI studies, they further support the view that rsfMRI can offer detailed information related to global neurophysiological phenomena as well as local hemodynamics that would have great potential as biomarkers.</p