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.

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.

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.

Influence of masticatory fatigue on the fracture resistance of the pulpless teeth restored with quartz-fiber post-core and crown.

To investigate whether masticatory fatigue affects the fracture resistance and pattern of lower premolars restored with quartz-fiber post-core and full crown, 44 single rooted lower premolars recently extracted from orthodontic patients were divided into two groups of 22 each. The crowns of all teeth were removed and endodontically treated and then restored with quartz-fiber post-core and full crown. Twenty-two teeth in one group were selected randomly and circularly loaded at 45° to the long axis of the teeth of 127.4 N at a 6 Hz frequency, and the other group was not delivered to cyclic loading and considered as control. Subsequently, all teeth in two groups were continually loaded to fail at 45° to the long axis of the teeth at a crosshead speed of 1 mm⋅min(-1). The mean destructive force values were (733.88±254.99) and (869.14±280.26) N for the experimental and the control group, respectively, and no statistically significant differences were found between two groups (P>0.05). Bevel fracture and horizontal fracture in the neck of root were the major fracture mode of the specimens. Under the circumstances of this study, it seems that cyclic loading does not affect the fracture strength and pattern of the quartz-fiber post-core-crown complex.