Redox dynamics and surface structures of an active palladium catalyst during methane oxidation.

Catalysts based on palladium are among the most effective in the complete oxidation of methane. Despite extensive studies and notable advances, the nature of their catalytically active species and conceivable structural dynamics remains only partially understood. Here, we combine operando transmission electron microscopy (TEM) with near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) and density functional theory (DFT) calculations to investigate the active state and catalytic function of Pd nanoparticles (NPs) under methane oxidation conditions. We show that the particle size, phase composition and dynamics respond appreciably to changes in the gas-phase chemical potential. In combination with mass spectrometry (MS) conducted simultaneously with in situ observations, we uncover that the catalytically active state exhibits phase coexistence and oscillatory phase transitions between Pd and PdO. Aided by DFT calculations, we provide a rationale for the observed redox dynamics and demonstrate that the emergence of catalytic activity is related to the dynamic interplay between coexisting phases, with the resulting strained PdO having more favorable energetics for methane oxidation.

A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.

Images obtained from single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) contain noises and artifacts, making cardiovascular disease diagnosis difficult. We developed a deep learning-based diagnosis support system using MPI SPECT images. Single-center datasets of MPI SPECT images (n = 5443) were obtained and labeled as healthy or coronary artery disease based on diagnosis reports. Three axes of four-dimensional datasets, resting, and stress conditions of three-dimensional reconstruction data, were reconstructed, and an AI model was trained to classify them. The trained convolutional neural network showed high performance [area under the curve (AUC) of the ROC curve: approximately 0.91; area under the recall precision curve: 0.87]. Additionally, using unsupervised learning and the Grad-CAM method, diseased lesions were successfully visualized. The AI-based automated diagnosis system had the highest performance (88%), followed by cardiologists with AI-guided diagnosis (80%) and cardiologists alone (65%). Furthermore, diagnosis time was shorter for AI-guided diagnosis (12 min) than for cardiologists alone (31 min). Our high-quality deep learning-based diagnosis support system may benefit cardiologists by improving diagnostic accuracy and reducing working hours.

Ensemble detection of hand joint ankylosis and subluxation in radiographic images using deep neural networks.

The modified total Sharp score (mTSS) is often used as an evaluation index for joint destruction caused by rheumatoid arthritis. In this study, special findings (ankylosis, subluxation, and dislocation) are detected to estimate the efficacy of mTSS by using deep neural networks (DNNs). The proposed method detects and classifies finger joint regions using an ensemble mechanism. This integrates multiple DNN detection models, specifically single shot multibox detectors, using different training data for each special finding. For the learning phase, we prepared a total of 260 hand X-ray images, in which proximal interphalangeal (PIP) and metacarpophalangeal (MP) joints were annotated with mTSS by skilled rheumatologists and radiologists. We evaluated our model using five-fold cross-validation. The proposed model produced a higher detection accuracy, recall, precision, specificity, F-value, and intersection over union than individual detection models for both ankylosis and subluxation detection, with a detection rate above 99.8% for the MP and PIP joint regions. Our future research will aim at the development of an automatic diagnosis system that uses the proposed mTSS model to estimate the erosion and joint space narrowing score.

Spatial and single-cell colocalisation analysis reveals MDK-mediated immunosuppressive environment with regulatory T cells in colorectal carcinogenesis.

BACKGROUND: Cell-cell interaction factors that facilitate the progression of adenoma to sporadic colorectal cancer (CRC) remain unclear, thereby hindering patient survival. METHODS: We performed spatial transcriptomics on five early CRC cases, which included adenoma and carcinoma, and one advanced CRC. To elucidate cell-cell interactions within the tumour microenvironment (TME), we investigated the colocalisation network at single-cell resolution using a deep generative model for colocalisation analysis, combined with a single-cell transcriptome, and assessed the clinical significance in CRC patients. FINDINGS: CRC cells colocalised with regulatory T cells (Tregs) at the adenoma-carcinoma interface. At early-stage carcinogenesis, cell-cell interaction inference between colocalised adenoma and cancer epithelial cells and Tregs based on the spatial distribution of single cells highlighted midkine (MDK) as a prominent signalling molecule sent from tumour epithelial cells to Tregs. Interaction between MDK-high CRC cells and SPP1+ macrophages and stromal cells proved to be the mechanism underlying immunosuppression in the TME. Additionally, we identified syndecan4 (SDC4) as a receptor for MDK associated with Treg colocalisation. Finally, clinical analysis using CRC datasets indicated that increased MDK/SDC4 levels correlated with poor overall survival in CRC patients. INTERPRETATION: MDK is involved in the immune tolerance shown by Tregs to tumour growth. MDK-mediated formation of the TME could be a potential target for early diagnosis and treatment of CRC. FUNDING: Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Science Research; OITA Cancer Research Foundation; AMED under Grant Number; Japan Science and Technology Agency (JST); Takeda Science Foundation; The Princess Takamatsu Cancer Research Fund.

Tumor suppressive role of the epigenetic master regulator BRD3 in colorectal cancer.

Bromodomain and extraterminal domain (BET) family proteins are epigenetic master regulators of gene expression via recognition of acetylated histones and recruitment of transcription factors and co-activators to chromatin. Hence, BET family proteins have emerged as promising therapeutic targets in cancer. In this study, we examined the functional role of bromodomain containing 3 (BRD3), a BET family protein, in colorectal cancer (CRC). In vitro and vivo analyses using BRD3-knockdown or BRD3-overexpressing CRC cells showed that BRD3 suppressed tumor growth and cell cycle G1/S transition and induced p21 expression. Clinical analysis of CRC datasets from our hospital or The Cancer Genome Atlas revealed that BET family genes, including BRD3, were overexpressed in tumor tissues. In immunohistochemical analyses, BRD3 was observed mainly in the nucleus of CRC cells. According to single-cell RNA sequencing in untreated CRC tissues, BRD3 was highly expressed in malignant epithelial cells, and cell cycle checkpoint-related pathways were enriched in the epithelial cells with high BRD3 expression. Spatial transcriptomic and single-cell RNA sequencing analyses of CRC tissues showed that BRD3 expression was positively associated with high p21 expression. Furthermore, overexpression of BRD3 combined with knockdown of, a driver gene in the BRD family, showed strong inhibition of CRC cells in vitro. In conclusion, we demonstrated a novel tumor suppressive role of BRD3 that inhibits tumor growth by cell cycle inhibition in part via induction of p21 expression. BRD3 activation might be a novel therapeutic approach for CRC.

Implementable assay for monitoring minimum residual disease after radical treatment for colorectal cancer.

Considering the cost and invasiveness of monitoring postoperative minimal residual disease (MRD) of colorectal cancer (CRC) after adjuvant chemoradiotherapy (ACT), we developed a favorable approach based on methylated circulating tumor DNA to detect MRD after radical resection. Analyzing the public database, we identified the methylated promoter regions of the genes FGD5, GPC6, and MSC. Using digital polymerase chain reaction (dPCR), we termed the "amplicon of methylated sites using a specific enzyme" assay as "AMUSE." We examined 180 and 114 pre- and postoperative serial plasma samples from 28 recurrent and 19 recurrence-free pathological stage III CRC patients, respectively. The results showed 22 AMUSE-positive of 28 recurrent patients (sensitivity, 78.6%) and 17 AMUSE-negative of 19 recurrence-free patients (specificity, 89.5%). AMUSE predicted recurrence 208 days before conventional diagnosis using radiological imaging. Regarding ACT evaluation by the reactive response, 19 AMUSE-positive patients during their second or third blood samples showed a significantly poorer prognosis than the other patients (p = 9E-04). The AMUSE assay stratified four groups by the altered patterns of tumor burden postoperatively. Interestingly, only 34.8% of cases tested AMUSE-negative during ACT treatment, indicating eligibility for ACT. The AMUSE assay addresses the clinical need for accurate MRD monitoring with universal applicability, minimal invasiveness, and cost-effectiveness, thereby enabling the timely detection of recurrences. This assay can effectively evaluate the efficacy of ACT in patients with stage III CRC following curative resection. Our study strongly recommends reevaluating the clinical application of ACT using the AMUSE assay.

Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms.

Anthracyclines can cause cancer therapy-related cardiac dysfunction (CTRCD) that adversely affects prognosis. Despite guideline recommendations, only half of the patients undergo surveillance echocardiograms. An AI model detecting reduced left ventricular ejection fraction from 12-lead electrocardiograms (ECG) (AI-EF model) suggests ECG features reflect left ventricular pathophysiology. We hypothesized that AI could predict CTRCD from baseline ECG, leveraging the AI-EF model's insights, and developed the AI-CTRCD model using transfer learning on the AI-EF model. In 1011 anthracycline-treated patients, 8.7% experienced CTRCD. High AI-CTRCD scores indicated elevated CTRCD risk (hazard ratio (HR), 2.66; 95% CI 1.73-4.10; log-rank p < 0.001). This remained consistent after adjusting for risk factors (adjusted HR, 2.57; 95% CI 1.62-4.10; p < 0.001). AI-CTRCD score enhanced prediction beyond known factors (time-dependent AUC for 2 years: 0.78 with AI-CTRCD score vs. 0.74 without; p = 0.005). In conclusion, the AI model robustly stratified CTRCD risk from baseline ECG.

Pre-existing Interstitial Lung Abnormalities and Immune Checkpoint Inhibitor-Related Pneumonitis in Solid Tumors: A Retrospective Analysis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have demonstrated efficacy over previous cytotoxic chemotherapies in clinical trials among various tumors. Despite their favorable outcomes, they are associated with a unique set of toxicities termed as immune-related adverse events (irAEs). Among the toxicities, ICI-related pneumonitis has poor outcomes with little understanding of its risk factors. This retrospective study aimed to investigate whether pre-existing interstitial lung abnormality (ILA) is a potential risk factor for ICI-related pneumonitis. MATERIALS AND METHODS: Patients with non-small cell lung cancer, malignant melanoma, renal cell carcinoma, and gastric cancer, who was administered either nivolumab, pembrolizumab, or atezolizumab between September 2014 and January 2019 were retrospectively reviewed. Information on baseline characteristics, computed tomography findings before administration of ICIs, clinical outcomes, and irAEs were collected from their medical records. Pre-existing ILA was categorized based on previous studies. RESULTS: Two-hundred-nine patients with a median age of 68 years were included and 23 (11.0%) developed ICI-related pneumonitis. While smoking history and ICI agents were associated with ICI-related pneumonitis (P = .005 and .044, respectively), the categories of ILA were not associated with ICI-related pneumonitis (P = .428). None of the features of lung abnormalities were also associated with ICI-related pneumonitis. Multivariate logistic analysis indicated that smoking history was the only significant predictor of ICI-related pneumonitis (P = .028). CONCLUSION: This retrospective study did not demonstrate statistically significant association between pre-existing ILA and ICI-related pneumonitis, nor an association between radiologic features of ILA and ICI-related pneumonitis. Smoking history was independently associated with ICI-related pneumonitis. Further research is warranted for further understanding of the risk factors of ICI-related pneumonitis.

Proximity labeling and identification of endogenous client proteins recruited to Y15-based artificial granules tethering a bait protein.

Protein clustering is a ubiquitous event in diverse cellular processes. Self-association of proteins triggers recruitment of downstream proteins to regulate cellular signaling. To investigate the interactions in detail, chemical biology tools to identify proteins recruited to defined assemblies are required. Here, we exploit an identification of proteins recruited in artificial granules (IPRAG) platform that combines intracellular Y15-based supramolecule construction with a proximity labeling method. We validated the IPRAG tool using Nck1 as a target bait protein. We constructed Nck1-tethering granules, labeled the recruited proteins with biotin, and analyzed them by LC-MS/MS. As a result, we successfully identified proteins that directly or indirectly interact with Nck1.

Improvement of Ionization Efficiency and Application of Structural Analysis for MALDI-TOFMS by Derivatization of Polyacrylic Acid.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) is a suitable method for polymer analysis. MALDI is a soft ionization technique that can generate mainly singly charged ions. Therefore, the polymer's molecular weight distribution is easy to analyze, facilitating the calculation of the number average molecular weight and weight average molecular weight and polydispersity. However, there are polymers that are difficult to detect by MALDI-TOFMS. For example, polyacrylic acid includes carboxylic acid in the main chain, which is difficult to measure due to its low ionization efficiency. As a solution, the ionization efficiency was improved by methylation. In this technical report, we introduce a method to utilize derivatization to determine the degree of polymerization by accurate mass spectrometry (MS). Furthermore, the structures of both ends of the polymers were estimated by tandem time-of-flight MS.

Deep learning-based model detects atrial septal defects from electrocardiography: a cross-sectional multicenter hospital-based study.

Background: Atrial septal defect (ASD) increases the risk of adverse cardiovascular outcomes. Despite the potential for risk mitigation through minimally invasive percutaneous closure, ASD remains underdiagnosed due to subtle symptoms and examination findings. To bridge this diagnostic gap, we propose a novel screening strategy aimed at early detection and enhanced diagnosis through the implementation of a convolutional neural network (CNN) to identify ASD from 12-lead electrocardiography (ECG). Methods: ECGs were collected from patients with at least one recorded echocardiogram at 3 hospitals from 2 continents (Keio University Hospital from July 2011 to December 2020, Brigham and Women's Hospital from January 2015 to December 2020, and Dokkyo Medical University Saitama Medical Center from January 2010 and December 2021). ECGs from patients with a diagnosis of ASD were labeled as positive cases while the remainder were labeled as negative. ECGs after the closure of ASD were excluded. After randomly splitting the ECGs into 3 datasets (50% derivation, 20% validation, and 30% test) with no patient overlap, a CNN-based model was trained using the derivation datasets from 2 hospitals and was tested on held-out datasets along with an external validation on the 3rd hospital. All eligible ECGs were used for derivation and validation whereas the earliest ECG for each patient was used for the test and external validation. The discrimination of ASD was assessed by the area under the receiver operating characteristic curve (AUROC). Multiple subgroups were examined to identify any heterogeneity. Findings: A total of 671,201 ECGs from 80,947 patients were collected from the 3 institutions. The AUROC for detecting ASD was 0.85-0.90 across the 3 hospitals. The subgroup analysis showed excellent performance across various characteristics Screening simulation using the model greatly increased sensitivity from 80.6% to 93.7% at specificity 33.6% when compared to using overt ECG abnormalities. Interpretation: A CNN-based model using 12-lead ECG successfully identified the presence of ASD with excellent generalizability across institutions from 2 separate continents. Funding: This work was supported by research grants from JST (JPMJPF2101), JSR corporation, Taiju Life Social Welfare Foundation, Kondou Kinen Medical Foundation, Research fund of Mitsukoshi health and welfare foundation, Tokai University School of Medicine Project Research and Internal Medicine Project Research, Secom Science and Technology Foundation, and Grants from AMED (JP23hma922012 and JP23ym0126813). This work was partially supported by One Brave Idea, co-funded by the American Heart Association and Verily with significant support from AstraZeneca and pillar support from Quest Diagnostics.

Successful multidisciplinary treatment with complete response to atezolizumab plus bevacizumab in a 90-year-old patient with hepatocellular carcinoma recurrence.

Atezolizumab plus bevacizumab is the first-line regimen in Japan for hepatocellular carcinoma following the results of the IMbrave 150 trial. However, the safety and efficiency of atezolizumab plus bevacizumab in older patients, especially in the oldest-old patients aged over 80 years, have not been thoroughly studied and is still controversial. Eighteen months ago, a 90-year-old woman underwent a laparoscopic hepatectomy (S6) for her primary hepatocellular carcinoma (S6, 2 cm). Nine months after the first surgery, she received transcatheter arterial chemoembolization treatment for solitary hepatocellular carcinoma recurrence (S8, 2 cm). The subsequent recurrence (S3, 1 cm; S5, 2 cm; S8, 1 cm) was uncovered by radiological assessment 1 year after transcatheter arterial chemoembolization treatment. We then initiated chemotherapy treatment with lenvatinib at 8 mg daily. Despite reducing the lenvatinib dosage, the adverse event of severe fatigue and asitia did not resolve; therefore, the regimen of atezolizumab + bevacizumab combination therapy was changed to be started. After the first 2 months, tumor regression was observed on computed tomography; the patient tolerated the atezolizumab + bevacizumab combination regimen over 8 months for 10 cycles without any adverse effects. She finally showed a complete response; no recurrence developed 1 year after the complete response. Therefore, older adult patients may benefit highly from atezolizumab plus bevacizumab with appropriate patient selection.

Increasing the siRNA knockdown efficiency of lipid nanoparticles by morphological transformation with the use of dihydrosphingomyelin as a helper lipid.

Lipid nanoparticles (LNPs), comprising ionizable lipids, helper lipids, cholesterol, and PEG lipids, can act as delivery carriers for nucleic acids and have achieved clinical success in the delivery of siRNA and mRNA. It has been shown that the morphology of LNPs varies depending on their lipid composition, but the influence of their morphology on nucleic acid efficacy has not been fully elucidated. In this study, we used our previously developed novel lipid, dioleoylglycerophosphate-diethylenediamine conjugate (DOP-DEDA), to create pH-responsive LNPs (DOP-DEDA LNPs). We evaluated the morphology of DOP-DEDA LNPs composed of different helper lipids and the knockdown efficiency of small interfering RNA (siRNA). A distinctive difference in morphology was observed between DOP-DEDA LNPs of different helper lipids. Significant differences were also observed in the apparent pKa of DOP-DEDA LNPs and the knockdown efficiency of siRNA, which may be due to the difference in the localization of DOP-DEDA molecules in DOP-DEDA LNPs. These findings suggest that changing helper lipids alters the morphology of the DOP-DEDA LNP system, which affects the apparent pKa and knockdown efficiency of siRNA.

Automatic Detection and Measurement of Renal Cysts in Ultrasound Images: A Deep Learning Approach.

Ultrasonography is widely used for diagnosis of diseases in internal organs because it is nonradioactive, noninvasive, real-time, and inexpensive. In ultrasonography, a set of measurement markers is placed at two points to measure organs and tumors, then the position and size of the target finding are measured on this basis. Among the measurement targets of abdominal ultrasonography, renal cysts occur in 20-50% of the population regardless of age. Therefore, the frequency of measurement of renal cysts in ultrasound images is high, and the effect of automating measurement would be high as well. The aim of this study was to develop a deep learning model that can automatically detect renal cysts in ultrasound images and predict the appropriate position of a pair of salient anatomical landmarks to measure their size. The deep learning model adopted fine-tuned YOLOv5 for detection of renal cysts and fine-tuned UNet++ for prediction of saliency maps, representing the position of salient landmarks. Ultrasound images were input to YOLOv5, and images cropped inside the bounding box and detected from the input image by YOLOv5 were input to UNet++. For comparison with human performance, three sonographers manually placed salient landmarks on 100 unseen items of the test data. These salient landmark positions annotated by a board-certified radiologist were used as the ground truth. We then evaluated and compared the accuracy of the sonographers and the deep learning model. Their performances were evaluated using precision-recall metrics and the measurement error. The evaluation results show that the precision and recall of our deep learning model for detection of renal cysts are comparable to standard radiologists; the positions of the salient landmarks were predicted with an accuracy close to that of the radiologists, and in a shorter time.

Detecting hand joint ankylosis and subluxation in radiographic images using deep learning: A step in the development of an automatic radiographic scoring system for joint destruction.

We propose a wrist joint subluxation/ankylosis classification model for an automatic radiographic scoring system for X-ray images. In managing rheumatoid arthritis, the evaluation of joint destruction is important. The modified total Sharp score (mTSS), which is conventionally used to evaluate joint destruction of the hands and feet, should ideally be automated because the required time depends on the skill of the evaluator, and there is variability between evaluators. Since joint subluxation and ankylosis are given a large score in mTSS, we aimed to estimate subluxation and ankylosis using a deep neural network as a first step in developing an automatic radiographic scoring system for joint destruction. We randomly extracted 216 hand X-ray images from an electronic medical record system for the learning experiments. These images were acquired from patients who visited the rheumatology department of Keio University Hospital in 2015. Using our newly developed annotation tool, well-trained rheumatologists and radiologists labeled the mTSS to the wrist, metacarpal phalangeal joints, and proximal interphalangeal joints included in the images. We identified 21 X-ray images containing one or more subluxation joints and 42 X-ray images with ankylosis. To predict subluxation/ankylosis, we conducted five-fold cross-validation with deep neural network models: AlexNet, ResNet, DenseNet, and Vision Transformer. The best performance on wrist subluxation/ankylosis classification was as follows: accuracy, precision, recall, F1 value, and AUC were 0.97±0.01/0.89±0.04, 0.92±0.12/0.77±0.15, 0.77±0.16/0.71±0.13, 0.82±0.11/0.72±0.09, and 0.92±0.08/0.85±0.07, respectively. The classification model based on a deep neural network was trained with a relatively small dataset; however, it showed good accuracy. In conclusion, we provided data collection and model training schemes for mTSS prediction and showed an important contribution to building an automated scoring system.

Diagnostic Value of the Vesical Imaging-Reporting and Data System in Bladder Urothelial Carcinoma with Variant Histology.

The value of the Vesicle Imaging-Reporting and Data System (VI-RADS) in the diagnosis of muscle-invasive bladder cancer (MIBC) for urothelial carcinoma with variant histology (VUC) remains unknown. We retrospectively evaluated 360 consecutive patients with bladder cancer (255 pure urothelial carcinoma [PUC] and 69 VUC) who underwent multiparametric magnetic resonance imaging between 2011 and 2019. VI-RADS scores assigned by four readers were significantly higher for the VUC group than for the PUC group (p < 0.05). In the cohort of 122 pair-matched patients, there was no significant difference in VI-RADS score distribution between the PUC and VUC groups for all readers (p > 0.05). The area under the receiver operating characteristic curve for MIBC diagnosis via overall VI-RADS score was 0.93-0.94 for PUC and 0.89-0.92 for VUC, with no significant difference between the PUC and VUC groups (p = 0.32-0.60). These data suggests that VI-RADS scores achieved high diagnostic performance for detection of muscle invasion in both PUC and VUC. PATIENT SUMMARY: The Vesical Imaging-Reporting and Data System (VI-RADS) is a standardized system for reporting on detection of muscle-invasive bladder cancer via magnetic resonance imaging (MRI) scans. Our study shows that VI-RADS is also highly accurate for diagnosis for different variants of muscle-invasive bladder cancer, with good inter-reader agreement.

Attention induction for a CT volume classification of COVID-19.

PURPOSE: This study proposes a method to draw attention toward the specific radiological findings of coronavirus disease 2019 (COVID-19) in CT images, such as bilaterality of ground glass opacity (GGO) and/or consolidation, in order to improve the classification accuracy of input CT images. METHODS: We propose an induction mask that combines a similarity and a bilateral mask. A similarity mask guides attention to regions with similar appearances, and a bilateral mask induces attention to the opposite side of the lung to capture bilaterally distributed lesions. An induction mask for pleural effusion is also proposed in this study. ResNet18 with nonlocal blocks was trained by minimizing the loss function defined by the induction mask. RESULTS: The four-class classification accuracy of the CT images of 1504 cases was 0.6443, where class 1 was the typical appearance of COVID-19 pneumonia, class 2 was the indeterminate appearance of COVID-19 pneumonia, class 3 was the atypical appearance of COVID-19 pneumonia, and class 4 was negative for pneumonia. The four classes were divided into two subgroups. The accuracy of COVID-19 and pneumonia classifications was evaluated, which were 0.8205 and 0.8604, respectively. The accuracy of the four-class and COVID-19 classifications improved when attention was paid to pleural effusion. CONCLUSION: The proposed attention induction method was effective for the classification of CT images of COVID-19 patients. Improvement of the classification accuracy of class 3 by focusing on features specific to the class remains a topic for future work.

Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers' performance and final diagnosis.

PURPOSE: To evaluate the performance of a deep learning-based computer-aided detection (CAD) software for detecting pulmonary nodules, masses, and consolidation on chest radiographs (CRs) and to examine the effect of readers' experience and data characteristics on the sensitivity and final diagnosis. MATERIALS AND METHODS: The CRs of 453 patients were retrospectively selected from two institutions. Among these CRs, 60 images with abnormal findings (pulmonary nodules, masses, and consolidation) and 140 without abnormal findings were randomly selected for sequential observer-performance testing. In the test, 12 readers (three radiologists, three pulmonologists, three non-pulmonology physicians, and three junior residents) interpreted 200 images with and without CAD, and the findings were compared. Weighted alternative free-response receiver operating characteristic (wAFROC) figure of merit (FOM) was used to analyze observer performance. The lesions that readers initially missed but CAD detected were stratified by anatomic location and degree of subtlety, and the adoption rate was calculated. Fisher's exact test was used for comparison. RESULTS: The mean wAFROC FOM score of the 12 readers significantly improved from 0.746 to 0.810 with software assistance (P = 0.007). In the reader group with < 6 years of experience, the mean FOM score significantly improved from 0.680 to 0.779 (P = 0.011), while that in the reader group with ≥ 6 years of experience increased from 0.811 to 0.841 (P = 0.12). The sensitivity of the CAD software and the adoption rate for the lesions with subtlety level 2 or 3 (obscure) lesions were significantly lower than for level 4 or 5 (distinct) lesions (50% vs. 93%, P < 0.001; and 55% vs. 74%, P = 0.04, respectively). CONCLUSION: CAD software use improved doctors' performance in detecting nodules/masses and consolidation on CRs, particularly for non-expert doctors, by preventing doctors from missing distinct lesions rather than helping them to detect obscure lesions.

Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning.

This study presents a novel framework for classifying and visualizing pneumonia induced by COVID-19 from CT images. Although many image classification methods using deep learning have been proposed, in the case of medical image fields, standard classification methods are unable to be used in some cases because the medical images that belong to the same category vary depending on the progression of the symptoms and the size of the inflamed area. In addition, it is essential that the models used be transparent and explainable, allowing health care providers to trust the models and avoid mistakes. In this study, we propose a classification method using contrastive learning and an attention mechanism. Contrastive learning is able to close the distance for images of the same category and generate a better feature space for classification. An attention mechanism is able to emphasize an important area in the image and visualize the location related to classification. Through experiments conducted on two-types of classification using a three-fold cross validation, we confirmed that the classification accuracy was significantly improved; in addition, a detailed visual explanation was achieved comparison with conventional methods.

An autopsy case of ovarian mucinous cystic tumor complicated by ovarian abscess and a review of the English literature.

Ovarian tumors are rarely associated with abscesses. Herein, an autopsy case of an ovarian mucinous cystic tumor complicated by an abscess, along with a review of previous cases, suggests the necessity of considering ovarian abscess as a cause of inflammation in patients with the ovarian tumors.