Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer.

OBJECTIVE: This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). METHODS: This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). RESULTS: A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. CONCLUSION: The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

Factors influencing traumatic brain injuries in maxillofacial fractures: A 12-year retrospective analysis of 2841 patients.

BACKGROUND/AIM: Results of studies investigating the association between traumatic brain injury (TBI) and maxillofacial fractures (MFs) have varied considerably. The present study aimed to evaluate the correlation between TBIs and MFs, as well as the impact of age, sex, trauma mechanism, and season on TBIs. MATERIALS AND METHODS: This 12-year retrospective study of 2841 patients used univariate and multivariate logistic regression to assess the association between MFs and other factors impacting TBIs. RESULTS: Among 2841 patients, 1978 TBIs occurred in 829 (29.2%), with intracranial injuries (n = 828) is the most common. Of 829 patients with TBIs, 688 were male and 141 were female, corresponding to a male-to-female ratio of 4.9:1.0. The most common age group was 40-49 years (24.6%). Vehicles (including motor vehicles and electric vehicles) accidents were the primary causes of injuries. Multivariate regression analyses revealed an increased risk for TBIs among males (odds ratio [OR] 0.632, p < 0.001). Patients >40 years of age were at higher risk for TBIs, especially those ≥70 years (OR 3.966, p = 0.001). Vehicle accidents were a high-risk factor for TBIs (OR 6.894, p < 0.001), and winter was the most prevalent season for such injuries (OR 1.559, p = 0.002). Risk for TBI increased by 136.4% in combined midfacial and mandibular fractures (p = 0.016) and by 101.6% in multiple midfacial fractures (p = 0.045). TBIs were less common in single mandibular fractures, notably in single-angle fractures, with a risk of only 0.204-fold. CONCLUSION: TBIs in MFs were significantly correlated with sex, age, aetiology, season and fracture location. Maxillofacial surgeons and emergency physicians must be aware of the possible association between TBIs and MFs to assess and manage this complicated relationship in a timely manner.

Epidemiology of maxillofacial fractures in northwest China: an 11-year retrospective study of 2240 patients.

BACKGROUND: The aim of this study was to determine the epidemiological pattern of maxillofacial fractures in northwestern China by retrospectively analysing the demographics, aetiologies, concomitant injuries, fracture sites, and management. METHODS: A 10-year retrospective analysis of 2240 patients with maxillofacial fractures admitted to the General Hospital of Ningxia Medical University was conducted. The extracted data included sex, age, aetiology, fracture site, concomitant injuries, time of treatment, therapeutic approaches and complications. Statistical analyses were performed, including descriptive analysis and the chi-square test. Logistic regression was used to determine the impact factors of maxillofacial fractures and concomitant injuries. P values < 0.05 were considered statistically significant. RESULTS: The age of the included patients ranged from 1 to 85 years, and the mean age was 35.88 ± 15.69 years. The male-to-female ratio was 3.9:1. The most frequent aetiology of maxillofacial fractures was road traffic accidents (RTAs) (56.3%), and the most common fracture sites were the anterior wall of the maxillary sinus, arcus zygomaticus and mandibular body. A total of 1147 patients (51.2%) were affected by concomitant injuries, with craniocerebral injury being the most common. Logistic regression analyses revealed increased risks of mid-facial fractures in elderly individuals (odds ratio (OR) = 1.029, P < 0.001) and females (OR = 0.719, P = 0.005). Younger patients had a higher risk of mandibular fractures (OR = 0.973, P < 0.001). RTAs increased the risk for mid-facial fractures and high falls increased the risk for mandibular fractures. CONCLUSIONS: The maxillofacial fracture pattern is correlated with sex, age and aetiology. Patients were mainly young and middle-aged males, and the main cause of injury was RTAs, mostly causing compound fractures. Medical staff must be systematically educated to comprehensively examine patients with injuries resulting from RTAs. The management of patients with fractures requires thorough consideration of the patient's age, aetiology, fracture site, and concomitant injuries.

3D DCT Based Image Compression Method for the Medical Endoscopic Application.

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.

OBJECTIVE: This retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin-Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pathological staging of colorectal cancer. The goal is to enable AI-assisted accurate pathological staging, supporting healthcare professionals in making efficient and precise staging assessments. METHODS: This study included a total of 267 colorectal cancer patients (training cohort: n = 213; testing cohort: n = 54). Logistic regression algorithms were used to construct the models. The HE image features were used to build the HE model, the Ki67 image features were used for the Ki67 model, and the combined model included features from both the HE and Ki67 images, as well as tumor markers (CEA, CA724, CA125, and CA242). The predictive results of the HE model, Ki67 model, and tumor markers were visualized through a nomogram. The models were evaluated using ROC curve analysis, and their clinical value was estimated using decision curve analysis (DCA). RESULTS: A total of 260 deep learning features were extracted from HE or Ki67 images. The AUC for the HE model and Ki67 model in the training cohort was 0.885 and 0.890, and in the testing cohort, it was 0.703 and 0.767, respectively. The combined model and nomogram in the training cohort had AUC values of 0.907 and 0.926, and in the testing cohort, they had AUC values of 0.814 and 0.817. In clinical DCA, the net benefit of the Ki67 model was superior to the HE model. The combined model and nomogram showed significantly higher net benefits compared to the individual HE model or Ki67 model. CONCLUSION: The combined model and nomogram, which integrate pathomics multi-modal data and clinical-pathological variables, demonstrated superior performance in distinguishing between Stage I-II and Stage III colorectal cancer. This provides valuable support for clinical decision-making and may improve treatment strategies and patient prognosis. Furthermore, the use of immunohistochemistry (Ki67) slides for pathomics modeling outperformed HE slide, offering new insights for future pathomics research.

A comprehensive radiopathological nomogram for the prediction of pathological staging in gastric cancer using CT-derived and WSI-based features.

OBJECTIVE: This study aims to develop and validate an innovative radiopathomics model that combines radiomics and pathomics features to effectively differentiate between stages I-II and stage III gastric cancer (pathological staging). METHODS: Our study included 200 patients with well-defined stages of gastric cancer divided into a training cohort (n = 140) and a test cohort (n = 60). Radiomics features were extracted from contrast-enhanced CT images using PyRadiomics, while pathomics features were obtained from whole slide images of pathological specimens through a fine-tuned deep learning model (ResNet-18). After rigorous feature dimensionality reduction and selection, we constructed radiomics models (SVM_rad, LR_rad, and MLP_rad) and pathomics models (SVM_path, LR_path, and MLP_path) utilizing support vector machine (SVM), logistic regression (LR), and multilayer perceptron (MLP) algorithms. The optimal radiomics and pathomics models were chosen based on comprehensive evaluation criteria such as ROC curves, Hosmer‒Lemeshow tests, and calibration curve tests. Feature patterns extracted from the best-performing radiomics model (MLP_rad) and pathomics model (SVM_rad) were integrated to create a powerful radiopathomics nomogram. RESULTS: From a pool of 1834 radiomics features extracted from CT images, 14 were selected to construct radiomics models. Among these, the MLP_rad model exhibited the most robust predictive performance (AUC, training cohort: 0.843; test cohort: 0.797). Likewise, 10 pathomics features were chosen from 512 extracted from whole slide images to build pathomics models, with the SVM_path model demonstrating the highest predictive efficiency (AUC, training cohort: 0.937; test cohort: 0.792). The combined radiopathomics nomogram model exhibited optimal discriminative ability (AUC, training cohort: 0.951; test cohort: 0.837), as confirmed by decision curve analysis (DCA), which indicated superior clinical effectiveness. CONCLUSION: This study presents a cutting-edge radiopathomics nomogram model designed to predict pathological staging in gastric cancer, distinguishing between stages I-II and stage III. Our research leverages preoperative CT images and histopathological slides to forecast gastric cancer staging accurately, potentially facilitating the estimation of staging before radical gastric cancer surgery in the future.

Abdominal pain in an adolescent girl with acute phlegmonous gastritis: a case report.

Acute phlegmonous gastritis (APG), a rare clinical condition, is mainly characterized by bacterial invasion in the gastric lining and is associated with a high mortality rate. The symptoms of APG include abdominal pain, nausea, vomiting, fever, and infection. Notably, the lack of specificity in the clinical presentation presents challenges in the early diagnosis of the disease. APG is often prevalent in adults, with a higher incidence in men than women. However, patients of other ages may also be affected. We herein present a case report of a 12-year-old girl who was admitted to the hospital with gastrointestinal symptoms and fever. The patient's imaging findings were compatible with APG. Despite the requirement for surgical treatment in most cases of phlegmonous gastritis, our patient rapidly improved with imaging and antibiotic therapy. This case demonstrates the success of antibiotic therapy with early diagnosis.

Identification of a Novel ACE Inhibitory Hexapeptide from Camellia Seed Cake and Evaluation of Its Stability.

The camellia seed cake proteins (CP) used in this study were individually hydrolyzed with neutral protease, alkaline protease, papain, and trypsin. The results showed that the hydrolysate had the highest ACE inhibitory activity at 67.36 ± 0.80% after four hours of neutral protease hydrolysis. Val-Val-Val-Pro-Gln-Asn (VVVPQN) was then obtained through ultrafiltration, Sephadex G-25 gel chromatography separation, LC-MS/MS analysis, and in silico screening. VVVPQN had ACE inhibitory activity with an IC50 value of 0.13 mg/mL (198.66 µmol/L), and it inhibited ACE in a non-competitive manner. The molecular docking indicated that VVVPQN can combine with ACE to form eight hydrogen bonds. The results of the stability study showed that VVVPQN maintained high ACE-inhibitory activity in weakly acidic and neutral environments and that heat treatment (20-80 °C) and Na+, Mg2+, as well as Fe3+ metal ions had little effect on the activity of VVVPQN. Moreover, it remained relatively stable after in vitro simulated gastrointestinal digestion. These results revealed that VVVPQN identified in camellia seed cake has the potential to be applied in functional food or antihypertensive drugs.

Clinicopathological features and prognostic significance of pulmonary adenocarcinoma with signet ring cell components: meta-analysis and SEER analysis.

Pulmonary adenocarcinoma is a common type of lung cancer that has been on the rise in recent years. Signet ring cell components (SRCC) can be present in various patterns of pulmonary adenocarcinoma, including papillary, acinar, and solid patterns. "Signet ring cell carcinoma" is a distinct subtype in the 2014 WHO classification of lung neoplasms, subsequent WHO classifications in 2015 and 2021 have deemed signet ring cells as accompanying morphological features with no clinical significance. The prognostic and clinical implications of SRCC in pulmonary adenocarcinoma remain controversial. Therefore, we conducted a meta-analysis to investigate the clinicopathological features and prognostic factors of SRCC in pulmonary adenocarcinoma. We conducted a comprehensive search in PubMed, EMBASE, and Web of Science to identify studies that examined the clinicopathological features and prognostic implications of pulmonary adenocarcinoma with SRCC. We used both fixed- and random-effects models to analyze the data and calculate the pooled hazard ratio (HR) and odds ratio (OR) with 95% confidence intervals (CIs). Additionally, we explored the prognostic significance of SRCC in pulmonary adenocarcinoma using the Surveillance, Epidemiology, and End Results (SEER) database. Our meta-analysis included 29 studies with pulmonary adenocarcinoma and SRCC components. The results showed that pulmonary adenocarcinoma with SRCC was associated with larger tumor size (OR = 1.99; 95% CI, 1.62-2.44, p < 0.001), advanced overall stage (OR = 5.18, 95% CI, 3.28-8.17, p < 0.00001) and lymph node stage (OR = 5.79, 95% CI, 1.96-17.09, p = 0.001), and worse overall survival (OS) compared to those without SRCC (HR = 1.80, 95% CI, 1.50-2.16, p < 0.00001). Analysis using the SEER dataset confirmed these findings. Our meta-analysis provides evidence that pulmonary adenocarcinoma with SRCC is associated with distinct clinicopathological features and a poorer prognosis. These findings have important implications for the management and treatment of patients. However, further studies are needed to validate these findings and explore the significance of SRCC in various subtypes of pulmonary adenocarcinoma.