Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis.

Chest X-ray (CXR) imaging is a low-cost, easy-to-use imaging alternative that can be used to diagnose/screen pulmonary abnormalities due to infectious diseaseX: Covid-19, Pneumonia and Tuberculosis (TB). Not limited to binary decisions (with respect to healthy cases) that are reported in the state-of-the-art literature, we also consider non-healthy CXR screening using a lightweight deep neural network (DNN) with a reduced number of epochs and parameters. On three diverse publicly accessible and fully categorized datasets, for non-healthy versus healthy CXR screening, the proposed DNN produced the following accuracies: 99.87% on Covid-19 versus healthy, 99.55% on Pneumonia versus healthy, and 99.76% on TB versus healthy datasets. On the other hand, when considering non-healthy CXR screening, we received the following accuracies: 98.89% on Covid-19 versus Pneumonia, 98.99% on Covid-19 versus TB, and 100% on Pneumonia versus TB. To further precisely analyze how well the proposed DNN worked, we considered well-known DNNs such as ResNet50, ResNet152V2, MobileNetV2, and InceptionV3. Our results are comparable with the current state-of-the-art, and as the proposed CNN is light, it could potentially be used for mass screening in resource-constraint regions.

Phytomodulatory proteins isolated from Calotropis procera latex promote glycemic control by improving hepatic mitochondrial function in HepG2 cells.

The medicinal uses of Calotropis procera are diverse, yet some of them are based on effects that still lack scientific support. Control of diabetes is one of them. Recently, latex proteins from C. procera latex (LP) have been shown to promote in vivo glycemic control by the inhibition of hepatic glucose production via AMP-activated protein kinase (AMPK). Glycemic control has been attributed to an isolated fraction of LP (CpPII), which is composed of cysteine peptidases (95%) and osmotin (5%) isoforms. Those proteins are extensively characterized in terms of chemistry, biochemistry and structural aspects. Furthermore, we evaluated some aspects of the mitochondrial function and cellular mechanisms involved in CpPII activity. The effect of CpPII on glycemic control was evaluated in fasting mice by glycemic curve and glucose and pyruvate tolerance tests. HepG2 cells was treated with CpPII, and cell viability, oxygen consumption, PPAR activity, production of lactate and reactive oxygen species, mitochondrial density and protein and gene expression were analyzed. CpPII reduced fasting glycemia, improved glucose tolerance and inhibited hepatic glucose production in control animals. Additionally, CpPII increased the consumption of ATP-linked oxygen and mitochondrial uncoupling, reduced lactate concentration, increased protein expression of mitochondrial complexes I, III and V, and activity of peroxisome-proliferator-responsive elements (PPRE), reduced the presence of reactive oxygen species (ROS) and increased mitochondrial density in HepG2 cells by activation of AMPK/PPAR. Our findings strongly support the medicinal use of the plant and suggest that CpPII is a potential therapy for prevention and/or treatment of type-2 diabetes. A common epitope sequence shared among the proteases and osmotin is possibly the responsible for the beneficial effects of CpPII.

Accuracy of salivary biomarkers in the diagnosis of periodontal status and coronary heart disease.

Inflammatory illnesses, such as periodontitis and atherosclerotic coronary heart disease (ASCHD), trigger the production of pro-inflammatory mediators. The aim of this study was to assess the accuracy of using salivary interleukin-1ß (IL-1ß), interleukin-18 (IL-18), and gasdermin D (GSDMD) in discerning patients with periodontitis with and without ASCHD from healthy individuals, and to assess their correlation with clinical periodontal parameters and low-density lipoprotein (LDL) levels. The study involved 120 participants: 30 were healthy subjects (control group, C), 30 had generalized periodontitis (group P), 30 had ASCHD and clinically healthy periodontium (group AS-C), and 30 had ASCHD and generalized periodontitis (group AS-P). Saliva and blood samples were collected, and periodontal characteristics such as plaque index, bleeding on probing, probing pocket depth, and clinical attachment loss were examined. IL-1ß, IL-18, and GSDMD levels from saliva were determined using ELISA. LDL levels were determined from the blood samples. Groups P, AS-C, and AS-P had higher levels of salivary IL-1ß, IL-18, and GSDMD than group C. The receiver operating characteristic (ROC) curves of all biomarkers showed high diagnostic accuracy, with a significant positive correlation with the clinical parameters and LDL levels. The observed correlations between the studied pro-inflammatory mediators and disease severity suggest that these biomarkers could serve as indicators of disease progression in conditions such as periodontitis and ASCHD.

Predictive model for preoperative risk calculation of cerebrospinal fluid leak after resection of midline craniofacial mass lesions.

Background: Complex anterior skull base defects produced by resection of mass lesions vary in size and configuration and may be extensive. We analyzed the largest single-center series of midline craniofacial lesions extending intra- and extracranially. The study aims at the development of a predictive model for preoperative measurement of the risk of the postoperative cerebrospinal fluid (CSF) leak based on patients' characteristics and surgical plans. Methods: 166 male and 149 female patients with mean age 40,5 years (1 year and - 81 years) operated for benign and tumor-like midline craniofacial mass lesions were retrospectively analyzed using logistic regression method (Ridge regression algorithm was selected). The overall CSF leak rate was 9.6%. The ROSE algorithm and 'glmnet' software suite in R were used to overcome the cohort's disbalance and avoid overtraining the model. Results: The most influential modifiable negative predictor of the postoperative CSF leak was the use of extracranial and combined approaches. Use of transbasal approaches, gross total resection, utilization of one or two vascularized flaps for skull base reconstruction were the foremost modifiable predictors of a good outcome. Criterium of elevated risk was established at 50% with a specificity of the model as high as 0.83. Conclusions: The performed study has allowed for identifying the most significant predictors of postoperative CSF leak and developing an effective formula to estimate the risk of this complication using data known for each patient. We believe that the suggested web-based online calculator can be helpful for decision making support in off-pattern clinical situations.

Photobac derived from bacteriochlorophyll-a shows potential for treating brain tumor in animal models by photodynamic therapy with desired pharmacokinetics and limited toxicity in rats and dogs.

Photobac is a near infrared photosensitizer (PS) derived from naturally occurring bacteriochlorophyll- a, with a potential for treating a variety of cancer types (U87, F98 and C6 tumor cells in vitro). The main objective of the studies presented herein was to evaluate the efficacy, toxicity and pharmacokinetic profile of Photobac in animals (mice, rats and dogs) and submit these results to the United States Food and Drug Administration (US FDA) for its approval to initiate Phase I human clinical trials of glioblastoma, a deadly cancer disease with no long term cure. The photodynamic therapy (PDT) efficacy of Photobac was evaluated in mice subcutaneously implanted with U87 tumors, and in rats bearing C6 tumors implanted in brain. In both tumor types, the Photobac-PDT was quite effective. The long-term cure in rats was monitored by magnetic resonance imaging (MRI) and histopathology analysis. A detailed pharmacology, pharmacokinetics and toxicokinetic study of Photobac was investigated in both non-GLP and GLP facilities at variable doses following the US FDA parameters. Safety Pharmacology studies suggest that there is no phototoxicity, cerebral or retinal toxicity with Photobac. No metabolites of Photobac were observed following incubation in rat, dog, mini-pig and human hepatocytes. Based on current biological data, Photobac-IND received the approval for Phase-I human clinical trials to treat Glioblastoma (brain cancer), which is currently underway at our institute. Photobac has also received an orphan drug status from the US FDA, because of its potential for treating Glioblastoma as no effective treatment is currently available for this deadly disease.

Development and validation of a nomogram to predict cancer-specific survival in middle-aged patients with papillary thyroid cancer: A SEER database study.

Background: Thyroid cancer (TC) accounts for more than 90% of endocrine tumours and is a typical head and neck tumour in adults. The aim of this study was to develop a predictive tool to predict cancer-specific survival (CSS) in middle-aged patients with papillary thyroid carcinoma (PTC). Methods: The patients from 2004 to 2015 were randomly divided into a training cohort (n = 25,342) and a internal validation cohort (n = 10,725). The patients from 2016 to 2018 were treated as an external validation cohort (n = 11353). COX proportional hazard model was used to screen meaningful independent risk factors. These factors were constructed into a nomogram to predict CSS in middle-aged patients with PTC. The performance and accuracy of the nomogram were then evaluated using the concordance index (C-index), calibration curve and the area under the curve (AUC). The clinical value of nomogram was evaluated by decision curve analysis (DCA). Results: Age, gender, marriage, tumour grade, T stage, N stage, M stage, surgery, chemotherapy, and tumour size were independent prognostic factors. The C-indexes of the training, internal validation, and external validation cohorts were 0.906, 0.887, and 0.962, respectively. The AUC and calibration curves show good accuracy. DCA shows that the clinical value of the nomogram is higher than that of Tumour, Node and Metastasis (TNM) staging. Conclusion: We developed a new prediction tool to predict CSS in middle-aged patients with PTC. The model has good performance after internal and external validation, which can be friendly to help doctors and patients predict CSS.

Automated detection and delineation of lymph nodes in haematoxylin & eosin stained digitised slides.

Treatment of patients with oesophageal and gastric cancer (OeGC) is guided by disease stage, patient performance status and preferences. Lymph node (LN) status is one of the strongest prognostic factors for OeGC patients. However, survival varies between patients with the same disease stage and LN status. We recently showed that LN size from patients with OeGC might also have prognostic value, thus making delineations of LNs essential for size estimation and the extraction of other imaging biomarkers. We hypothesized that a machine learning workflow is able to: (1) find digital H&E stained slides containing LNs, (2) create a scoring system providing degrees of certainty for the results, and (3) delineate LNs in those images. To train and validate the pipeline, we used 1695 H&E slides from the OE02 trial. The dataset was divided into training (80%) and validation (20%). The model was tested on an external dataset of 826 H&E slides from the OE05 trial. U-Net architecture was used to generate prediction maps from which predefined features were extracted. These features were subsequently used to train an XGBoost model to determine if a region truly contained a LN. With our innovative method, the balanced accuracies of the LN detection were 0.93 on the validation dataset (0.83 on the test dataset) compared to 0.81 (0.81) on the validation (test) datasets when using the standard method of thresholding U-Net predictions to arrive at a binary mask. Our method allowed for the creation of an "uncertain" category, and partly limited false-positive predictions on the external dataset. The mean Dice score was 0.73 (0.60) per-image and 0.66 (0.48) per-LN for the validation (test) datasets. Our pipeline detects images with LNs more accurately than conventional methods, and high-throughput delineation of LNs can facilitate future LN content analyses of large datasets.

Antibody titer levels and the effect on subsequent SARS-CoV-2 infection in a large US-based cohort.

Despite a growing amount of data around the kinetics and durability of the antibody response induced by vaccination and previous infection, there is little understanding of whether or not a given quantitative level of antibodies correlates to protection against SARS-CoV-2 infection or reinfection. In this study, we examine SARS-CoV-2 anti-spike receptor binding domain (RBD) antibody titers and subsequent SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) tests in a large cohort of US-based patients. We analyzed antibody test results in a cohort of 22,204 individuals, 6.8% (n = 1,509) of whom eventually tested positive for SARS-CoV-2 RNA, suggesting infection or reinfection. Kaplan-Meier curves were plotted to understand the effect of various levels of anti-spike RBD antibody titers (classified into discrete ranges) on subsequent RT-PCR positivity rates. Statistical analyses included fitting a Cox proportional hazards model to estimate the age-, sex- and exposure-adjusted hazard ratios for S antibody titer, using zip-code positivity rates by week as a proxy for COVID-19 exposure. It was found that the best models of the temporally associated infection risk were those based on log antibody titer level (HR = 0.836 (p < 0.05)). When titers were binned, the hazard ratio associated with antibody titer >250 Binding Antibody Units (BAU) was 0.27 (p < 0.05, 95% CI [0.18, 0.41]), while the hazard ratio associated with previous infection was 0.20 (p < 0.05, 95% CI [0.10, 0.39]). Fisher exact odds ratio (OR) for Ab titers <250 BAU showed OR = 2.84 (p < 0.05; 95% CI: [2.30, 3.53]) for predicting the outcome of a subsequent PCR test. Antibody titer levels correlate with protection against subsequent SARS-CoV-2 infection or reinfection when examining a cohort of real-world patients who had the spike RBD antibody assay performed.

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes.

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

Deep-Learning-Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT.

Purpose: Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies. Design: Cross sectional study. Participants: Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma. Methods: The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis. Main Outcome Measures: The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework. Results: For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02. Conclusions: Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Time to sputum conversion in patients with pulmonary tuberculosis: A score to estimate the infectious period.

Introduction: Patients with pulmonary tuberculosis (PTB) disease and positive sputum cultures are the main source of infection. Culture conversion time is inconsistent and defining the length of respiratory isolation is challenging. The objective of this study is to develop a score to predict the length of isolation period. Methods: A retrospective study was carried out to evaluated risk factors associated with persistent positive sputum cultures after 4 weeks of treatment in 229 patients with PTB. A multivariable logistic regression model was used to determinate predictors for positive culture and a scoring system was created based on the coefficients of the final model. Results: Sputum culture was persistently positive in 40.6%. Fever at consultation (1.87, 95% CI:1.02-3.41), smoking (2.44, 95% CI:1.36-4.37), >2 affected lung lobes (1.95, 95% CI:1.08-3.54), and neutrophil-to-lymphocyte ratio > 3.5 (2.22, 95% CI:1.24-3.99), were significantly associated with delayed culture conversion. Therefore, we assembled a severity score that achieved an area under the curve of 0.71 (95% CI:0.64-0.78). Conclusions: In patients with smear positive PTB, a score with clinical, radiological and analytical parameters can be used as a supplemental tool to assist clinical decisions in isolation period.

Comparative Analysis of 10-2 Test on Advanced Vision Analyzer and Humphrey Perimeter in Glaucoma.

Purpose: To evaluate diagnostic precision and prove equivalence of 2 devices, Advanced vision analyzer (AVA, Elisar Vision Technology) and Humphrey field analyzer (HFA, Zeiss) for the detection of glaucoma on 10-2 program. Design: Prospective, cross-sectional, observational study. Participants: Threshold estimates of 1 eye each of 66 patients with glaucoma, 36 control participants, and 10 glaucoma suspects were analyzed on 10-2 test with AVA and HFA. Methods: Mean sensitivity (MS) values of 68 points and central 16 test points were calculated and compared. Intraclass correlation (ICC), Bland-Altman (BA) plots, linear regression of MS, mean deviation (MD), and pattern standard deviation (PSD) were computed to assess the 10-2 threshold estimate of the devices. Receiver operating characteristic curves were generated for MS and MD values, and the area under the curve (AUC) was compared with assessing diagnostic precision. Main Outcome Measures: Mean sensitivity values of 68 points and central 16 points, AUC for MS and MD values, ICC values, BA plots, and linear-regression analysis. Results: Bland-Altman plot showed significant correlation for MS, MD, and PSD values for both devices. For MS, the overall ICC value was 0.96 (P < 0.001) with a mean bias of 0.0 dB and limits of agreement range of 7.59. The difference in MS values between both devices was -0.4760 ± 1.95 (P > 0.05). The AUC for MS values for AVA was 0.89 and for HFA was 0.92 (P = 0.188); whereas it was similar at 0.88 for MD values (P = 0.799). Advanced vision analyzer and HFA identically discriminated between healthy and patients with glaucoma (P < 0.001), although HFA denoted marginally greater ability (P > 0.05). Conclusions: Statistical results denote adequate equivalence between AVA and HFA because threshold estimates of AVA strongly correlate with HFA for 10-2 program. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Emerging Biomarkers in Alcohol-associated Hepatitis.

Alcohol-associated hepatitis (AH) is a clinical syndrome of jaundice, abdominal pain, and anorexia due to prolonged heavy alcohol intake. AH is associated with changes in gene expression, cytokines, immune response, and the gut microbiome. There are limited biomarkers to diagnose and prognosticate in AH, but several non-invasive biomarkers are emerging. In this review, clinical risk-stratifying algorithms, promising AH biomarkers like cytokeratin-18 fragments, genetic polymorphisms, and microRNAs will be reviewed.

Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis.

Purpose: To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis. Design: Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965). Participants: Patients with type 1 DM and controls included in the progenitor study. Methods: Radiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types. Results: A dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection, the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3 × 3 mm superficial capillary plexus OCTA scan (0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets. Conclusions: Radiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for DR screening in patients with type 1 DM. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Prevalence of and Associated Factors for Eyelid Cancer in the American Academy of Ophthalmology Intelligent Research in Sight Registry.

Purpose: To estimate the prevalence of eyelid cancers in the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) Registry and evaluate the associated factors. Design: Retrospective IRIS Registry database study. Participants: All patients in the IRIS Registry between December 1, 2010, and December 1, 2018, with International Classification of Disease, ninth and 10th revisions, codes for eyelid cancers (basal cell carcinoma [BCC], squamous cell carcinoma [SCC], malignant melanoma [MM], sebaceous carcinoma/other specified malignant neoplasm [SBC], melanoma in situ [MIS], and unspecified malignant neoplasm [UMN]). Methods: The prevalence of each eyelid cancer type was estimated overall and by age group, sex, race, ethnicity, and smoking status. The associations between any eyelid cancer (AEC) or each cancer type and possible risk factors were examined using univariate and multivariate logistic regression models. Main Outcome Measures: Prevalence of and associated factors for each eyelid cancer type. Results: There were 82 136 patients with eyelid cancer identified. The prevalence of AEC was 145.1 per 100 000 population. The cancer-specific prevalence ranged from 87.9 (BCC) to 25.6 (UMN), 11.1 (SCC), 5.0 (SBC), 4.1 (MM), and 0.4 (MIS) per 100 000 population. The prevalence of AEC and each cancer type increased with increasing age (all P < 0.0001), and the prevalence of AEC, BCC, SCC, and MM was higher in males (all P < 0.0001), MIS (P = 0.02). The prevalence of BCC, SCC, MM, SBC, and AEC was highest in Whites versus that in patients of any other race (all P < 0.0001). In the multivariate logistic regression model with associated risk factors (age, sex, race, ethnicity, and smoking status), AEC was associated with older age groups ([< 20 years reference {ref.}]; odds ratio [95% confidence interval]: 20-39 years: 3.35 [1.96-5.72]; 40-65 years: 24.21 [14.80-39.59]; and > 65 years: 42.78 [26.18-69.90]), male sex (female [ref.]; 1.40 [1.33-1.48]), White race (inverse associations with African Americans [0.12 {0.09-0.16}], Asians [0.19 {0.13-0.26}], others [0.59 {0.40-0.89}]), and ethnicity (non-Hispanic [ref.]; Hispanic: 0.38 [0.33-0.45]; unknown: 0.81 [0.75-0.88]). Active smoking (never smoker [ref.]) was associated with AEC (1.11 [1.01-1.21]), BCC (1.27 [1.23-1.31]), SCC (1.59 [1.46-1.73]), and MM (1.26 [1.08-1.46]). Conclusions: This study reports the overall and cancer-specific prevalence of eyelid cancers using a large national clinical eye disease database. Smoking was found to be associated with AEC, BCC, SCC, and MM, which is a new observation. This epidemiologic profile of on-eyelid cancers is valuable for identifying patients at a higher risk of malignancy, allocating medical resources, and improving cancer care.

Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study.

Background: Tongue images (the colour, size and shape of the tongue and the colour, thickness and moisture content of the tongue coating), reflecting the health state of the whole body according to the theory of traditional Chinese medicine (TCM), have been widely used in China for thousands of years. Herein, we investigated the value of tongue images and the tongue coating microbiome in the diagnosis of gastric cancer (GC). Methods: From May 2020 to January 2021, we simultaneously collected tongue images and tongue coating samples from 328 patients with GC (all newly diagnosed with GC) and 304 non-gastric cancer (NGC) participants in China, and 16 S rDNA was used to characterize the microbiome of the tongue coating samples. Then, artificial intelligence (AI) deep learning models were established to evaluate the value of tongue images and the tongue coating microbiome in the diagnosis of GC. Considering that tongue imaging is more convenient and economical as a diagnostic tool, we further conducted a prospective multicentre clinical study from May 2020 to March 2022 in China and recruited 937 patients with GC and 1911 participants with NGC from 10 centres across China to further evaluate the role of tongue images in the diagnosis of GC. Moreover, we verified this approach in another independent external validation cohort that included 294 patients with GC and 521 participants with NGC from 7 centres. This study is registered at ClinicalTrials.gov, NCT01090362. Findings: For the first time, we found that both tongue images and the tongue coating microbiome can be used as tools for the diagnosis of GC, and the area under the curve (AUC) value of the tongue image-based diagnostic model was 0.89. The AUC values of the tongue coating microbiome-based model reached 0.94 using genus data and 0.95 using species data. The results of the prospective multicentre clinical study showed that the AUC values of the three tongue image-based models for GCs reached 0.88-0.92 in the internal verification and 0.83-0.88 in the independent external verification, which were significantly superior to the combination of eight blood biomarkers. Interpretation: Our results suggest that tongue images can be used as a stable method for GC diagnosis and are significantly superior to conventional blood biomarkers. The three kinds of tongue image-based AI deep learning diagnostic models that we developed can be used to adequately distinguish patients with GC from participants with NGC, even early GC and precancerous lesions, such as atrophic gastritis (AG). Funding: The National Key R&D Program of China (2021YFA0910100), Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Science and Technology Projects of Zhejiang Province (2019C03049), National Natural Science Foundation of China (82074245, 81973634, 82204828), and Chinese Postdoctoral Science Foundation (2022M713203).

MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study.

Background and purpose: Predicting tumour response would be useful for selecting patients with locally advanced rectal cancer (LARC) for organ preservation strategies. We aimed to develop and validate a prediction model for T downstaging (ypT0-2) in LARC patients after neoadjuvant chemoradiotherapy and to identify those who may benefit from consolidation chemotherapy. Materials and methods: cT3-4 LARC patients at three tertiary medical centers from January 2012 to January 2019 were retrospectively included, while a prospective cohort was recruited from June 2021 to March 2022. Eight filter (principal component analysis, least absolute shrinkage and selection operator, partial least-squares discriminant analysis, random forest)-classifier (support vector machine, logistic regression) models were established to select radiomic features. A nomogram combining radiomics and significant clinical features was developed and validated by calibration curve and decision curve analysis. Interaction test was conducted to investigate the consolidation chemotherapy benefits. Results: A total of 634 patients were included (426 in training cohort, 174 in testing cohort and 34 in prospective cohort). A radiomic prediction model using partial least-squares discriminant analysis and a support vector machine showed the best performance (AUC: 0.832 [training]; 0.763 [testing]). A nomogram combining radiomics and clinical features showed significantly better prognostic performance (AUC: 0.842 [training]; 0.809 [testing]) than the radiomic model. The model was also tested in the prospective cohort with AUC 0.727. High-probability group (score > 81.82) may have potential benefits from ≥ 4 cycles consolidation chemotherapy (OR: 4.173, 95 % CI: 0.953-18.276, p = 0.058, pinteraction = 0.021). Conclusion: We identified and validated a model based on multicenter pre-treatment radiomics to predict ypT0-2 in cT3-4 LARC patients, which may facilitate individualised treatment decision-making for organ-preservation strategies and consolidation chemotherapy.

Prediction of short-term treatment outcome of nasopharyngeal carcinoma based on voxel incoherent motion imaging and arterial spin labeling quantitative parameters.

Purpose: To evaluate the early response of chemoradiotherapy (CRT) in nasopharyngeal carcinoma (NPC) based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and three-dimensional pseudo-continuous arterial spin labeling (3D pCASL). Materials and methods: Forty patients diagnosed with NPC were recruited and divided into complete remission (CR) and partial remission (PR) group after CRT. All patients underwent IVIM and ASL and the related parameters was obtained. These parameters include pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), average blood flow ( BFavg), minimum blood flow (BFmin), and maximum blood flow (BFmax). Student's t test was used to compare the difference in ASL and IVIM derived parameters between CR and PR. The Areas under curve (AUC) of the receiver operating characteristic (ROC) was used to analyze the diagnostic performance of each parameter of ASL and IVIM to the treatment outcome. Results: the D value of IVIM in CR group was lower than that of the PR group ( P = 0.014),. Among the parameters of ASL, the BFavg and BFmax of the CR group were higher than those of the PR group(p = 0.004,0.013), but the BFmin had no statistical significance in the two groups（P = 0.54）. AUC of D, BFavg, and BFmax is about 0.731, 0.753, and 0.724, respectively, all of their combined AUC diagnosis was 0.812. Conclusion: The early response of NPC after CRT can predict by IVIM's diffusion parameters and ASL-related blood flow parameters.

A novel transit-time flow metric, diastolic resistance index, detects subcritical anastomotic stenosis in coronary artery bypass grafting.

Objective: Transit time flow measurement (TTFM) can detect critical anastomotic stenosis during coronary artery bypass grafting. However, the identification of subcritical stenosis remains challenging. We hypothesized that diastolic resistance index (DRI), a novel TTFM metric, is more effective in evaluating subcritical stenosis than the currently available TTFM metrics. DRI is used to measure changes in the diastolic versus systolic resistance of distal anastomosis. Methods: A total of 123 coronary bypass anastomoses in 35 patients were prospectively analyzed. During coronary artery bypass grafting, the mean graft flow (Qmean), pulsatility index, and diastolic filling were obtained. DRI was calculated using the intraoperative recordings of TTFM and arterial pressure. Postoperatively, stenosis of anastomoses was categorized into successful (<50%), subcritical (50%-74%), and critical (≥75%) via multidetector computed tomography scan. Results: In total, 93 (76%), 13 (10%), and 17 (14%) anastomoses were graded as successful, subcritical, and critical, respectively. DRI and diastolic filling could distinguish subcritical from successful anastomoses (P < .01 and < .01, respectively), whereas Qmean and pulsatility index could not (P = .12 and .39, respectively). The receiver operating characteristic curves were established to evaluate the diagnostic ability for detecting ≥50% stenosis. In left anterior descending artery grafting (n = 55), DRI had the highest area under the curve (0.91), followed by diastolic filling (0.87), Qmean (0.74), and pulsatility index (0.65). Conclusions: DRI and diastolic filling had a reliable diagnostic ability for detecting ≥50% stenosis during coronary artery bypass grafting. In left anterior descending artery grafting, DRI had a more satisfactory detection capability than other TTFM metrics.

Imaging biomarkers to stratify lymph node metastases in abdominal CT - Is radiomics superior to dual-energy material decomposition?

Purpose: To assess the potential of radiomic features in comparison to dual-energy CT (DECT) material decomposition to objectively stratify abdominal lymph node metastases. Materials and methods: In this retrospective study, we included 81 patients (m, 57; median age, 65 (interquartile range, 58.7-73.3) years) with either lymph node metastases (n = 36) or benign lymph nodes (n = 45) who underwent contrast-enhanced abdominal DECT between 06/2015-07/2019. All malignant lymph nodes were classified as unequivocal according to RECIST criteria and confirmed by histopathology, PET-CT or follow-up imaging. Three investigators segmented lymph nodes to extract DECT and radiomics features. Intra-class correlation analysis was applied to stratify a robust feature subset with further feature reduction by Pearson correlation analysis and LASSO. Independent training and testing datasets were applied on four different machine learning models. We calculated the performance metrics and permutation-based feature importance values to increase interpretability of the models. DeLong test was used to compare the top performing models. Results: Distance matrices and t-SNE plots revealed clearer clusters using a combination of DECT and radiomic features compared to DECT features only. Feature reduction by LASSO excluded all DECT features of the combined feature cohort. The top performing radiomic features model (AUC = 1.000; F1 = 1.000; precision = 1.000; Random Forest) was significantly superior to the top performing DECT features model (AUC = 0.942; F1 = 0.762; precision = 0.800; Stochastic Gradient Boosting) (DeLong < 0.001). Conclusion: Imaging biomarkers have the potential to stratify unequivocal lymph node metastases. Radiomics models were superior to DECT material decomposition and may serve as a support tool to facilitate stratification of abdominal lymph node metastases.