Claudin-18 status and its correlation with HER2 and PD-L1 expression in gastric cancer with peritoneal dissemination.

BACKGROUND: Gastric cancer with peritoneal dissemination (PD) has a dismal prognosis, and current treatments have shown little efficacy. CLDN18.2-targeted therapies have shown promising efficacy against gastric cancers that express high levels of CLDN18. Because of the limited information regarding CLDN18.2 status in PD, we analyzed PD-positive gastric cancers for CLDN18 status in both primary and PD, along with HER2 and PD-L1 combined positive score (CPS). METHODS: Immunohistochemical analyses were performed on 84 gastric cancer cases using paired primary and PD tissue samples. RESULTS: At 40% cut-off, CLDN18 was positive in 57% (48/84) primary tumors and in 44% (37/84) PDs. At 75% cut-off, 28.6% (24/84) primary tumors and 20.2% (17/84) PDs were CLDN18-positive. The concordance rate between primary tumors and PD was 79.8% at 40% cut-off and 75% at 75% cut-off. When comparing biopsy and surgical specimens, the concordance rates were 87.5% at 40% cut-off and 81.3% at 75% cut-off. Within a tumor, the superficial area tended to have a higher CLDN18-positive rate than the invasive front (P = 0.001). Although HER2 -positivity was only 11.9% in this cohort, CLDN18 positivity in HER2-negative tumors (n = 74) was relatively high: 60.8% at 40% cut-off and 28.4% at 75% cut-off. Among double-negative (HER2 - and PD-L1 CPS < 1) tumors, CLDN18 positivity was 67.6% at 40% cut-off and 26.5% at 75% cut-off. CONCLUSIONS: CLDN18 expression is generally maintained in PD and is relatively high even in double-negative tumors, making it a promising therapeutic target for PD-positive gastric cancer.

Integrated enhancer regulatory network by enhancer-promoter looping in gastric cancer.

Enhancer cis-regulatory elements play critical roles in gene regulation at many stages of cell growth. Enhancers in cancer cells also regulate the transcription of oncogenes. In this study, we performed a comprehensive analysis of long-range chromatin interactions, histone modifications, chromatin accessibility and expression in two gastric cancer (GC) cell lines compared to normal gastric epithelial cells. We found that GC-specific enhancers marked by histone modifications can activate a population of genes, including some oncogenes, by interacting with their proximal promoters. In addition, motif analysis of enhancer-promoter interacting enhancers showed that GC-specific transcription factors are enriched. Among them, we found that MYB is crucial for GC cell growth and activated by the enhancer with an enhancer-promoter loop and TCF7 upregulation. Clinical GC samples showed epigenetic activation of enhancers at the MYB locus and significant upregulation of TCF7 and MYB, regardless of molecular GC subtype and clinicopathological factors. Single-cell RNA sequencing of gastric mucosa with intestinal metaplasia showed high expression of TCF7 and MYB in intestinal stem cells. When we inactivated the loop-forming enhancer at the MYB locus using CRISPR interference (dCas9-KRAB), GC cell growth was significantly inhibited. In conclusion, we identified MYB as an oncogene activated by a loop-forming enhancer and contributing to GC cell growth.

Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal.

Purpose: Current clinical assessment qualitatively describes background parenchymal enhancement (BPE) as minimal, mild, moderate, or marked based on the visually perceived volume and intensity of enhancement in normal fibroglandular breast tissue in dynamic contrast-enhanced (DCE)-MRI. Tumor enhancement may be included within the visual assessment of BPE, thus inflating BPE estimation due to angiogenesis within the tumor. Using a dataset of 426 MRIs, we developed an automated method to segment breasts, electronically remove lesions, and calculate scores to estimate BPE levels. Approach: A U-Net was trained for breast segmentation from DCE-MRI maximum intensity projection (MIP) images. Fuzzy c-means clustering was used to segment lesions; the lesion volume was removed prior to creating projections. U-Net outputs were applied to create projection images of both, affected, and unaffected breasts before and after lesion removal. BPE scores were calculated from various projection images, including MIPs or average intensity projections of first- or second postcontrast subtraction MRIs, to evaluate the effect of varying image parameters on automatic BPE assessment. Receiver operating characteristic analysis was performed to determine the predictive value of computed scores in BPE level classification tasks relative to radiologist ratings. Results: Statistically significant trends were found between radiologist BPE ratings and calculated BPE scores for all breast regions (Kendall correlation, p<0.001). Scores from all breast regions performed significantly better than guessing (p<0.025 from the z-test). Results failed to show a statistically significant difference in performance with and without lesion removal. BPE scores of the affected breast in the second postcontrast subtraction MIP after lesion removal performed statistically greater than random guessing across various viewing projections and DCE time points. Conclusions: Results demonstrate the potential for automatic BPE scoring to serve as a quantitative value for objective BPE level classification from breast DCE-MR without the influence of lesion enhancement.

Changes in SARS-CoV-2 viral load and titers over time in SARS-CoV-2-infected human corpses.

High viral titers of infectious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been detected in human corpses long after death. However, little is known about the kinetics of infectious SARS-CoV-2 in corpses. In this case series study, we investigated the postmortem kinetics of infectious SARS-CoV-2 in human corpses by collecting nasopharyngeal swab samples at multiple time points from six SARS-CoV-2-infected patients after their death. SARS-CoV-2 RNA was detected by quantitative reverse transcription-polymerase chain reaction from nasopharyngeal swab samples collected from all six deceased patients. A viral culture showed the presence of infectious virus in one deceased patient up to 12 days after death. Notably, this patient had a shorter time from symptom onset to death than the other patients, and autopsy samples showed pathological findings consistent with viral replication in the upper respiratory tract. Therefore, this patient died during the viral shedding phase, and the amount of infectious virus in the corpse did not decrease over time up to the date of autopsy (12 days after death). The findings of this study indicate that the persistence of SARS-CoV-2 in corpses can vary among individuals and may be associated with the stage of the disease at the time of death. These important results complement many previously reported findings on the infectivity of SARS-CoV-2 at postmortem.

Role of sureness in evaluating AI/CADx: Lesion-based repeatability of machine learning classification performance on breast MRI.

BACKGROUND: Artificial intelligence/computer-aided diagnosis (AI/CADx) and its use of radiomics have shown potential in diagnosis and prognosis of breast cancer. Performance metrics such as the area under the receiver operating characteristic (ROC) curve (AUC) are frequently used as figures of merit for the evaluation of CADx. Methods for evaluating lesion-based measures of performance may enhance the assessment of AI/CADx pipelines, particularly in the situation of comparing performances by classifier. PURPOSE: The purpose of this study was to investigate the use case of two standard classifiers to (1) compare overall classification performance of the classifiers in the task of distinguishing between benign and malignant breast lesions using radiomic features extracted from dynamic contrast-enhanced magnetic resonance (DCE-MR) images, (2) define a new repeatability metric (termed sureness), and (3) use sureness to examine if one classifier provides an advantage in AI diagnostic performance by lesion when using radiomic features. METHODS: Images of 1052 breast lesions (201 benign, 851 cancers) had been retrospectively collected under HIPAA/IRB compliance. The lesions had been segmented automatically using a fuzzy c-means method and thirty-two radiomic features had been extracted. Classification was investigated for the task of malignant lesions (81% of the dataset) versus benign lesions (19%). Two classifiers (linear discriminant analysis, LDA and support vector machines, SVM) were trained and tested within 0.632 bootstrap analyses (2000 iterations). Whole-set classification performance was evaluated at two levels: (1) the 0.632+ bias-corrected area under the ROC curve (AUC) and (2) performance metric curves which give variability in operating sensitivity and specificity at a target operating point (95% target sensitivity). Sureness was defined as 1-95% confidence interval of the classifier output for each lesion for each classifier. Lesion-based repeatability was evaluated at two levels: (1) repeatability profiles, which represent the distribution of sureness across the decision threshold and (2) sureness of each lesion. The latter was used to identify lesions with better sureness with one classifier over another while maintaining lesion-based performance across the bootstrap iterations. RESULTS: In classification performance assessment, the median and 95% CI of difference in AUC between the two classifiers did not show evidence of difference (ΔAUC = -0.003 [-0.031, 0.018]). Both classifiers achieved the target sensitivity. Sureness was more consistent across the classifier output range for the SVM classifier than the LDA classifier. The SVM resulted in a net gain of 33 benign lesions and 307 cancers with higher sureness and maintained lesion-based performance. However, with the LDA there was a notable percentage of benign lesions (42%) with better sureness but lower lesion-based performance. CONCLUSIONS: When there is no evidence for difference in performance between classifiers using AUC or other performance summary measures, a lesion-based sureness metric may provide additional insight into AI pipeline design. These findings present and emphasize the utility of lesion-based repeatability via sureness in AI/CADx as a complementary enhancement to other evaluation measures.

Development and validation of machine learning model for predicting treatment responders in patients with primary biliary cholangitis.

AIMS: Ursodeoxycholic acid is the first-line treatment for primary biliary cholangitis, and treatment response is one of the factors predicting the outcome. To prescribe alternative therapies, clinicians might need additional information before deciphering the treatment response to ursodeoxycholic acid, contributing to a better patient prognosis. In this study, we developed and validated machine learning (ML) algorithms to predict treatment responses using pretreatment data. METHODS: This multicenter cohort study included collecting datasets from two data samples. Data 1 included 245 patients from 18 hospitals for ML development, and was divided into (i) training and (ii) development sets. Data 2 (iii: test set) included 51 patients from our hospital for validation. An extreme gradient boosted tree predicted the treatment response in the ML model. The area under the curve was used to evaluate the efficacy of the algorithm. RESULTS: Data 1 showed that patients complying with the Paris II treatment response had significantly lower serum alkaline phosphatase and total bilirubin levels than those who did not respond. Three factors, total bilirubin, total protein, and alanine aminotransferase levels were selected as essential variables for prediction. Data 2 showed that patients complying with the Paris II criteria had significantly high prothrombin time and low total bilirubin levels. The area under the curve of extreme gradient boosted tree was good for (ii) (0.811) and (iii) (0.856). CONCLUSIONS: We demonstrated the efficacy of ML in predicting the treatment response for patients with primary biliary cholangitis. Early identification of cases requiring additional treatment with our novel ML model may improve prognosis.

External Evaluation of a Mammography-based Deep Learning Model for Predicting Breast Cancer in an Ethnically Diverse Population.

Purpose: To externally evaluate a mammography-based deep learning (DL) model (Mirai) in a high-risk racially diverse population and compare its performance with other mammographic measures. Materials and Methods: A total of 6435 screening mammograms in 2096 female patients (median age, 56.4 years ± 11.2 [SD]) enrolled in a hospital-based case-control study from 2006 to 2020 were retrospectively evaluated. Pathologically confirmed breast cancer was the primary outcome. Mirai scores were the primary predictors. Breast density and Breast Imaging Reporting and Data System (BI-RADS) assessment categories were comparative predictors. Performance was evaluated using area under the receiver operating characteristic curve (AUC) and concordance index analyses. Results: Mirai achieved 1- and 5-year AUCs of 0.71 (95% CI: 0.68, 0.74) and 0.65 (95% CI: 0.64, 0.67), respectively. One-year AUCs for nondense versus dense breasts were 0.72 versus 0.58 (P = .10). There was no evidence of a difference in near-term discrimination performance between BI-RADS and Mirai (1-year AUC, 0.73 vs 0.68; P = .34). For longer-term prediction (2-5 years), Mirai outperformed BI-RADS assessment (5-year AUC, 0.63 vs 0.54; P < .001). Using only images of the unaffected breast reduced the discriminatory performance of the DL model (P < .001 at all time points), suggesting that its predictions are likely dependent on the detection of ipsilateral premalignant patterns. Conclusion: A mammography DL model showed good performance in a high-risk external dataset enriched for African American patients, benign breast disease, and BRCA mutation carriers, and study findings suggest that the model performance is likely driven by the detection of precancerous changes.Keywords: Breast, Cancer, Computer Applications, Convolutional Neural Network, Deep Learning Algorithms, Informatics, Epidemiology, Machine Learning, Mammography, Oncology, Radiomics Supplemental material is available for this article. © RSNA, 2023See also commentary by Kontos and Kalpathy-Cramer in this issue.

Stomach encyclopedia: Combined single-cell and spatial transcriptomics reveal cell diversity and homeostatic regulation of human stomach.

The stomach is an important digestive organ with various biological functions. However, because of the complexity of its cellular and glandular composition, its precise cellular biology has yet to be elucidated. In this study, we conducted single-cell RNA sequencing (scRNA-seq) and subcellular-level spatial transcriptomics analysis of the human stomach and constructed the largest dataset to date: a stomach encyclopedia. This dataset consists of approximately 380,000 cells from scRNA-seq and the spatial transcriptome, enabling integrated analyses of transcriptional and spatial information of gastric and metaplastic cells. This analysis identified LEFTY1 as an uncharacterized stem cell marker, which was confirmed through lineage tracing analysis. A wide variety of cell-cell interactions between epithelial and stromal cells, including PDGFRA+BMP4+WNT5A+ fibroblasts, was highlighted in the developmental switch of intestinal metaplasia. Our extensive dataset will function as a fundamental resource in investigations of the stomach, including studies of development, aging, and carcinogenesis.

Analysis of distribution, collection, and confirmation of capacity dependency of small extracellular vesicles toward a therapy for liver cirrhosis.

BACKGROUND: The progression of liver fibrosis leads to portal hypertension and liver dysfunction. However, no antifibrotic agents have been approved for cirrhosis to date, making them an unmet medical need. Small extracellular vesicles (sEVs) of mesenchymal stem cells (MSCs) are among these candidate agents. In this study, we investigated the effects of sEVs of MSCs, analyzed their distribution in the liver post-administration, whether their effect was dose-dependent, and whether it was possible to collect a large number of sEVs. METHODS: sEVs expressing tdTomato were generated, and their uptake into constituent liver cells was observed in vitro, as well as their sites of uptake and cells in the liver using a mouse model of liver cirrhosis. The efficiency of sEV collection using tangential flow filtration (TFF) and changes in the therapeutic effects of sEVs in a volume-dependent manner were examined. RESULTS: The sEVs of MSCs accumulated mostly in macrophages in damaged areas of the liver. In addition, the therapeutic effect of sEVs was not necessarily dose-dependent, and it reached a plateau when the dosage exceeded a certain level. Furthermore, although ultracentrifugation was commonly used to collect sEVs for research purposes, we verified that TFF could be used for efficient sEV collection and that their effectiveness is not reduced. CONCLUSION: In this study, we identified some unknown aspects regarding the dynamics, collection, and capacity dependence of sEVs. Our results provide important fundamentals for the development of therapies using sEVs and hold potential implications for the therapeutic applications of sEV-based therapies for liver cirrhosis.

Solid pseudopapillary neoplasm in a woman presenting with acute pancreatitis: a case report and review of literature.

Solid pseudopapillary neoplasm (SPN) is a rare pancreatic tumor that typically affects young women in the body and tail of the pancreas. SPN is often asymptomatic in the early stages, so it is initially discovered as a large tumor. In this report, we experienced a case of a relatively small SPN discovered in the setting of acute pancreatitis. Because there have been few reports of SPN being discovered in the situation like our case, we report this case based on a review of the literature.

Bilateral asymmetry of quantitative parenchymal kinetics at ultrafast DCE-MRI predict response to neoadjuvant chemotherapy in patients with HER2+ breast cancer.

PURPOSE: To assess whether measurement of the bilateral asymmetry of semiquantitative and quantitative perfusion parameters from ultrafast dynamic contrast-enhanced MRI (DCE-MRI), allows early prediction of pathologic response after neoadjuvant chemotherapy (NAC) in patients with HER2+ breast cancer. MATERIALS AND METHODS: Twenty-eight female patients with HER2+ breast cancer treated with NAC who underwent pre-NAC ultrafast DCE-MRI (3-9 s/phase) were enrolled for this study. Four semiquantitative and two quantitative parenchymal parameters were calculated for each patient. Ipsilateral/contralateral (I/C) ratio (for four parameters) and the difference between (for two parameters) ipsi- and contra-lateral parenchymal kinetic parameters (kBPE) were compared for patients with pathologic complete response (pCR) and those having residual disease. Lasso regression with leave-one-out cross validation was used to determine the optimal combination of parameters for a regression model and multivariable logistic regression was used to identify independent predictors for pCR. Chi-squared test, two-sided t-test and Kruskal-Wallis test were used. RESULTS: The Ktrans I/C ratio cutoff value of 1.11 had a sensitivity of 83.3% and specificity of 75% for pCR. The ve I/C ratio cutoff value of 1.1 had a sensitivity of 75% and specificity of 81.3% for pCR. The area under the receiver operating characteristic curve of the three-kBPE parameter model, including initial area under the enhancement curve (AUC30) I/C ratio, KtransI/C ratio and ve I/C ratio, was 0.89 with sensitivity of 91.7% at specificity of 81.3%. CONCLUSION: Quantitative assessment of bilateral asymmetry kBPE from pre-NAC ultrafast DCE-MRI can predict pCR in patients with HER2+ breast cancer.

Apoptosis-related Prognostic Factors in Advanced Colorectal Cancer Determined Using Tissue Microarrays.

BACKGROUND/AIM: Cancer cells evade apoptosis in colorectal cancer (CRC); however, overlap between apoptosis and poor prognosis marker proteins in the invasive front of tumors has not been reported. Here, we aimed to clarify the relationship between apoptosis, apoptosis-related protein expression, and prognosis in the central and invasive front regions of CRC using tissue microarrays. PATIENTS AND METHODS: Data of 207 patients with pathological stage 3 CRC, who underwent radical surgery between October 2010 and November 2014, were retrospectively reviewed. We assessed apoptosis using M30 CytoDEATH, CD163, and p53 immunostaining in tumor sections in the center and invasive front using tissue microarrays and correlated the results with the survival outcomes. RESULTS: M30 CytoDEATH staining was negative; 134 cases (64.7%) were apoptosis-negative in the center and 103 (49.8%) were apoptosis-negative at the invasive front. CD163 positivity was observed in 16 cases (7.8%) in the center and in 36 cases (17.6%) at the invasive front; p53 positivity was observed in 33 (15.9%) and 64 (30.9%) cases in the center and invasive front, respectively. CD163 and p53 expression was not associated with survival outcomes; however, the apoptosis-negative group at the invasive front had significantly poorer survival outcomes (overall survival: p=0.044, relapse-free survival: p=0.001). We identified cases with a poor prognosis by combining apoptosis and CD163 expression. CONCLUSION: A lower apoptosis percentage at the invasive front is associated with a poorer prognosis. CRC cases with a poor prognosis can be identified by evaluating apoptosis and CD163 expression in the invasive front.

COVID-19 Analysis in Tissue Samples Acquired by Minimally Invasive Autopsy in Out-of-Hospital Deaths with Postmortem Degeneration.

Minimally invasive autopsy (MIA) is an alternative to a full autopsy for the collection of tissue samples from patients' bodies using instruments such as a biopsy needle. MIA has been conducted in many cases of coronavirus disease 2019 (COVID-19) and has contributed to the elucidation of the disease pathogenesis. However, most cases analyzed are hospital deaths, and there are few reports on the application of MIA in out-of-hospital deaths with varying extents of post-mortem changes. In this study, MIA and autopsies were performed in 15 patients with COVID-19 2-30 days after death, including 11 out-of-hospital deaths. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome detection by reverse transcriptase quantitative polymerase chain reaction using MIA samples was mostly consistent with autopsy samples, particularly lung tissue, even in out-of-hospital cases. MIA had high sensitivity and specificity (> 0.80). Histological examination of lung tissue obtained by MIA showed characteristics of COVID-19 pneumonia, with 91% agreement with autopsy samples, whereas localization of SARS-CoV-2 protein in lung tissue was indicated by immunohistochemistry, with 75% agreement. In conclusion, these results suggest that MIA is applicable to out-of-hospital deaths due to COVID-19 with various postmortem changes, especially when autopsies are not available.

Elucidation of the mechanism of amyloid A and transthyretin formation using mass spectrometry-based absolute quantification.

Amyloidosis is triggered by the truncation of amyloid precursor proteins, causing organ damages. While previous studies found the truncation of amyloid A (AA) and amyloid transthyretin (ATTR) occurs in C- and N-terminal, respectively, the detailed mechanism of the fibril formation remains unclear. Liquid chromatography mass spectrometry is usually applied for a qualitative purpose, and thus quantification of tryptic peptide residue is difficult. We therefore employed a mass spectrometry-based quantification by isotope-labeled cell-free (MS-QBIC) to analyze the truncation processes in amyloid fibrillogenesis of AA and ATTR using the formalin-fixed paraffin-embedded tissues of autopsy cases. In this study, the process of transthyretin from an 'early fibril state' consisting of full-length ATTR to a 'mature ATTR amyloid fibril' with a truncated low-amyloidogenic segment has been mathematically revealed. The amount of full-length ATTR was nine times higher than in mature fibers. Large cohort studies using MS-QBIC may shed light on the clinical significance of amyloid fibrils.

BCL11B expression in hepatocellular carcinoma relates to chemosensitivity and clinical prognosis.

INTRODUCTION: B-cell lymphoma/leukemia 11B (BCL11B) is a subunit of SWI/SNF chromatin remodeling complexes and functions in cell cycle regulation and apoptosis upon DNA replication stress and damages via transcription. Many malignancies were reported to exhibit changes in BCL11B gene expression; however, no study has focused on the relationship between BCL11B and hepatocellular carcinoma, which potentially exhibits DNA replication stress and damages upon its oncogenesis. Thus, in this study, we examined the molecular characterization of BCL11B expression in hepatocellular carcinoma. METHODS AND RESULTS: The cumulative progression-free survival and overall survival were significantly longer in the clinical cases of BCL11B-negative hepatocellular carcinoma than BCL11B-positve cases. Microarray and real-time PCR analyses in hepatocellular carcinoma cell lines indicated a correlation between BCL11B and GATA6, a gene reported to be correlated with oncogenic activities and resistance to anthracycline, which is often used for hepatocellular carcinoma chemotherapy. Consequently, BCL11B-overexpressing cell lines exhibited resistance to anthracycline in cell growth assays and the resistance has been evidenced by the increased expression of BCL-xL in cell lines. The results were supported by the analyses of human HCC samples showing the correlation between BCL11B and GATA6 expressions. DISCUSSIONS AND CONCLUSION: Our results indicated that overexpression of BCL11B amplifies GATA6 expression in hepatocellular carcinoma in vitro and in vivo that leads to anti-apoptotic signal activation, and induces resistance to chemotherapy, which influenced the postoperative prognosis.

Similarity of oncogenic protein expression in KRASG12D gene delivery-based rat pancreatic cancer model to that of human pancreatic cancer.

BACKGROUND: The development of effective therapies and biomarkers for pancreatic cancer is an unmet clinical need. To address this, we have developed an easy-to-use pancreatic cancer rat animal model via pancreas-targeted hydrodynamic gene delivery of human pancreatic cancer-related genes. Our study aimed to determine the molecular similarity between the pancreatic tumor in the rat model and human pancreatic cancer. METHODS: KRASG12D gene-expressing plasmid was delivered to the pancreas of wild type rats via pancreas-targeted hydrodynamic gene delivery as previously reported. Tissue samples were collected at 5 weeks after the first gene delivery. The tumors developed in the rats were assessed for the expression of oncogenic proteins that are involved in human pancreatic cancer development. RESULTS: The development of a tumor mimicking pancreatic ductal adenocarcinoma was confirmed. The expression levels of Cyclin D1, c-Jun, IL-33, and Zip4 proteins in the tumor were immunohistochemically assessed and the correlation of the proteins was confirmed. The expression pattern showed similarity to that of surgically resected human pancreatic cancer tissues. CONCLUSIONS: Our study findings showing a similar pattern of oncogenic protein expression in novel KRASG12D gene-induced rat pancreatic cancer model and human pancreatic cancer will be useful for establishing novel tumor markers and therapeutic options for pancreatic cancer.

Machine learning prediction model for treatment responders in patients with primary biliary cholangitis.

Background and Aim: Treatment response to ursodeoxycholic acid may predict the prognosis of patients with primary biliary cholangitis (PBC). Recent studies have suggested the benefits of using machine learning (ML) to forecast complex medical predictions. We aimed to predict treatment response in patients with PBC using ML and pretreatment data. Methods: We conducted a single-center retrospective study and collected data from 194 patients with PBC who were followed up for at least 12 months after treatment initiation. Patient data were analyzed with five ML models, namely random forest, extreme gradient boosting (XGB), decision tree, naïve Bayes, or logistic regression, to predict treatment response using the Paris II criteria. The established models were assessed using an out-of-sample validation. The area under the curve (AUC) was used to evaluate the efficacy of each algorithm. Overall survival and liver-related deaths were analyzed using Kaplan-Meier analysis. Results: Compared to logistic regression (AUC = 0.595, P = 0.0219, 0.031 models), ML analyses showed significantly high AUC in the random forest (AUC = 0.84) and XGB (AUC = 0.83) models; however, the AUC was not significantly high for decision tree (AUC = 0.633) or naïve Bayes (AUC = 0.584) models. Kaplan-Meier analysis showed significantly improved prognoses in patients predicted to achieve the Paris II criteria by XGB (log-rank = 0.005 and 0.007). Conclusion: ML algorithms could improve treatment response prediction using pretreatment data, which could lead to better prognoses. In addition, the ML model using XGB could predict the prognosis of patients before treatment initiation.

Essence of postmortem computed tomography for in-hospital deaths: what clinical radiologists should know.

Postmortem computed tomography (CT) is an essential tool for investigating the causes of death. Postmortem CT has characteristic imaging findings and should not be interpreted in the same manner as clinical antemortem images. In analyzing the cause of death in in-hospital death cases using postmortem images, it is crucial to understand early postmortem and post-resuscitation changes. In addition, it is essential to understand the limitations of diagnosing the cause of death or significant pathology relating to death on non-contrast-enhanced postmortem CT. In Japan, there has also been social demand to establish a system for postmortem imaging at the time of death. To facilitate such a system, clinical radiologists should be prepared to interpret postmortem images and assess the cause of death. This review article provides comprehensive information regarding unenhanced postmortem CT for in-hospital death cases in daily clinical practice in Japan.

Lost expression of AT-rich interaction domain 1A in the gastric mucosa-A constituent of field cancerization in the stomach.

Abnormalities of the AT-rich interaction domain 1A (ARID1A) occur in cancer tissues and precursors or premalignant lesions in various organs. To investigate the significance of ARID1A abnormalities in the early phase of cancer development in the stomach, we screened for ARID1A loss and p53 overexpression in glands in non-neoplastic gastric mucosa using immunohistochemistry. We tested 230 tissue blocks of 77 patients with gastric carcinoma, and in 10% of non-neoplastic mucosa we detected ARID1A-lost and in 3.7% p53-overexpressed foci. Loss of ARID1A expression occurred in the scales of several glands, which were morphologically characterized as authentic, pseudo-pyloric, or intestinal metaplastic glands devoid of dysplastic changes. In contrast, p53-overexpressed foci were detected in dysplastic intestinal metaplasia. In early gastric cancer cases (n = 46), ARID1A-lost foci were frequent in samples of patients with Epstein-Barr virus-associated gastric carcinoma (p = 0.037). Ultra-deep DNA sequencing of ARID1A-lost foci revealed frameshift and nonsense mutations in ARID1A. Mapping abnormal glands in the entire resected stomach of the three selected patients demonstrated that ARID1A-lost foci clustered with p53 abnormal glands. ARID1A-lost epithelial cells may develop clonal growth through the pathway, different from p53-abnormal intestinal metaplasia, and require one or more events to develop into an overt carcinoma, such as EBV infection.