Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence.

The prediction of patient disease risk via computed tomography (CT) images and artificial intelligence techniques shows great potential. However, training a robust artificial intelligence model typically requires large-scale data support. In practice, the collection of medical data faces obstacles related to privacy protection. Therefore, the present study aims to establish a robust federated learning model to overcome the data island problem and identify high-risk patients with postoperative gastric cancer recurrence in a multicentre, cross-institution setting, thereby enabling robust treatment with significant value. In the present study, we collect data from four independent medical institutions for experimentation. The robust federated learning model algorithm yields area under the receiver operating characteristic curve (AUC) values of 0.710, 0.798, 0.809, and 0.869 across four data centres. Additionally, the effectiveness of the algorithm is evaluated, and both adaptive and common features are identified through analysis.

Development of a CT image analysis-based scoring system to differentiate gastric schwannomas from gastrointestinal stromal tumors.

Purpose: To develop a point-based scoring system (PSS) based on contrast-enhanced computed tomography (CT) qualitative and quantitative features to differentiate gastric schwannomas (GSs) from gastrointestinal stromal tumors (GISTs). Methods: This retrospective study included 51 consecutive GS patients and 147 GIST patients. Clinical and CT features of the tumors were collected and compared. Univariate and multivariate logistic regression analyses using the stepwise forward method were used to determine the risk factors for GSs and create a PSS. Area under the receiver operating characteristic curve (AUC) analysis was performed to evaluate the diagnostic efficiency of PSS. Results: The CT attenuation value of tumors in venous phase images, tumor-to-spleen ratio in venous phase images, tumor location, growth pattern, and tumor surface ulceration were identified as predictors for GSs and were assigned scores based on the PSS. Within the PSS, GS prediction probability ranged from 0.60% to 100% and increased as the total risk scores increased. The AUC of PSS in differentiating GSs from GISTs was 0.915 (95% CI: 0.874-0.957) with a total cutoff score of 3.0, accuracy of 0.848, sensitivity of 0.843, and specificity of 0.850. Conclusions: The PSS of both qualitative and quantitative CT features can provide an easy tool for radiologists to successfully differentiate GS from GIST prior to surgery.

Clinical and imaging characteristics of pulmonary cryptococcosis: a comparative analysis of 118 non-AIDS patients in China.

The accurate diagnosis of pulmonary cryptococcosis (PC) is an important guarantee for the selection of reasonable treatment methods. In this paper, the clinical and imaging manifestations of PC in non-AIDS patients were retrospectively analyzed, and according to whether there was an underlying disease, a comparative analysis was carried out to deepen the understanding of PC, and improve the accuracy of its diagnosis. Both clinical and CT imaging data of 118 PC patients were analyzed retrospectively. The clinical manifestations of PC patients were not specific, and 61 patients had no apparent symptoms. A total of 49 patients (49/118) were treated with antifungal agents alone, 46 of them had follow-up records after treatment, and 91.3% (42/46) of them achieved a good outcome. The most common imaging sign was the subpleural nodule or mass. Other main imaging signs include bronchial air sign (50/118), halo sign (32/118), ring target sign (65/118), lobulation sign (72/118), and necrosis (76/118). In terms of age, halo sign, and ring target sign, there were significant differences between the group with underlying disease and the group without underlying disease (P < .05). The CT manifestations of PC have some characteristics, and using antifungal agents can achieve good outcomes.

The CT manifestations of PC were characteristic. The subpleural lesions combined with bronchial air sign, ring target sign, and necrosis were important for the accurate diagnosis of PC. In addition, antifungal therapy for PC patients can achieve good results.

Computed Tomography-Based Radiomics Nomogram: Potential to Predict Local Recurrence of Gastric Cancer After Radical Resection.

OBJECTIVE: Accurate prediction of postoperative recurrence risk of gastric cancer (GC) is critical for individualized precision therapy. We aimed to investigate whether a computed tomography (CT)-based radiomics nomogram can be used as a tool for predicting the local recurrence (LR) of GC after radical resection. MATERIALS AND METHODS: 342 patients (194 in the training cohort, 78 in the internal validation cohort, and 70 in the external validation cohort) with pathologically proven GC from two centers were included. Radiomics features were extracted from the preoperative CT imaging. The clinical model, radiomics signature, and radiomics nomogram, which incorporated the radiomics signature and independent clinical risk factors, were developed and verified. Furthermore, the performance of these three models was assessed by using the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). RESULTS: The radiomics signature, which was comprised of two selected radiomics features, namely, contrast_GLCM and dissimilarity_GLCM, showed better performance than the clinical model in predicting the LR of GC, with AUC values of 0.83 in the training cohort, 0.84 in the internal validation cohort, and 0.73 in the external cohort, respectively. By integrating the independent clinical risk factors (N stage, bile acid duodenogastric reflux and nodular or irregular outer layer of the gastric wall) into the radiomics signature, the radiomics nomogram achieved the highest accuracy in predicting LR, with AUC values of 0.89, 0.89 and 0.80 in the three cohorts, respectively. DCA in the validation cohort showed that radiomics nomogram added more net benefit than the clinical model within the range of 0.01-0.98. CONCLUSION: The CT-based radiomics nomogram has the potential to predict the LR of GC after radical resection.

A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer From Primary Gastric Lymphoma.

OBJECTIVE: This study aims to differentiate preoperative Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL) by transfer learning radiomics nomogram (TLRN) with whole slide images of GC as source domain data. MATERIALS AND METHODS: This study retrospectively enrolled 438 patients with histopathologic diagnoses of Borrmann type IV GC and PGL. They received CT examinations from three hospitals. Quantitative transfer learning features were extracted by the proposed transfer learning radiopathomic network and used to construct transfer learning radiomics signatures (TLRS). A TLRN, which integrates TLRS, clinical factors, and CT subjective findings, was developed by multivariate logistic regression. The diagnostic TLRN performance was assessed by clinical usefulness in the independent validation set. RESULTS: The TLRN was built by TLRS and a high enhanced serosa sign, which showed good agreement by the calibration curve. The TLRN performance was superior to the clinical model and TLRS. Its areas under the curve (AUC) were 0.958 (95% confidence interval [CI], 0.883-0.991), 0.867 (95% CI, 0.794-0.922), and 0.921 (95% CI, 0.860-0.960) in the internal and two external validation cohorts, respectively. Decision curve analysis (DCA) showed that the TLRN was better than any other model. TLRN has potential generalization ability, as shown in the stratification analysis. CONCLUSIONS: The proposed TLRN based on gastric WSIs may help preoperatively differentiate PGL from Borrmann type IV GC.Borrmann type IV gastric cancer, primary gastric lymphoma, transfer learning, whole slide image, deep learning.

A Heterogeneity Radiomic Nomogram for Preoperative Differentiation of Primary Gastric Lymphoma From Borrmann Type IV Gastric Cancer.

OBJECTIVE: This study aimed to preoperatively differentiate primary gastric lymphoma from Borrmann type IV gastric cancer by heterogeneity nomogram based on routine contrast-enhanced computed tomographic images. METHODS: We enrolled 189 patients from 2 hospitals (90 in the training cohort and 99 in the validation cohort). Subjective findings, including high-enhanced mucosal sign, high-enhanced serosa sign, nodular or an irregular outer layer of the gastric wall, and perigastric fat infiltration, were assessed to construct a subjective finding model. A deep learning model was developed to segment tumor areas, from which 1680 three-dimensional heterogeneity radiomic parameters, including first-order entropy, second-order entropy, and texture complexity, were extracted to build a heterogeneity signature by least absolute shrinkage and selection operator logistic regression. A nomogram that integrates heterogeneity signature and subjective findings was developed by multivariate logistic regression. The diagnostic performance of the nomogram was assessed by discrimination and clinical usefulness. RESULTS: High-enhanced serosa sign and nodular or an irregular outer layer of the gastric wall were identified as independent predictors for building the subjective finding model. High-enhanced serosa sign and heterogeneity signature were significant predictors for differentiating the 2 groups (all, P < 0.05). The area under the curve with heterogeneity nomogram was 0.932 (95% confidence interval, 0.863-0.973) in the validation cohort. Decision curve analysis and stratified analysis confirmed the clinical utility of the heterogeneity nomogram. CONCLUSIONS: The proposed heterogeneity radiomic nomogram on contrast-enhanced computed tomographic images may help differentiate primary gastric lymphoma from Borrmann type IV gastric cancer preoperatively.

Development and validation of a predictor of insufficient enhancement during the hepatobiliary phase of Gd-EOB-DTPA-enhanced magnetic resonance imaging.

Background Insufficient enhancement of liver parenchyma negatively affects diagnostic accuracy of Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI). Currently, there is no reliable method for predicting insufficient enhancement during the hepatobiliary phase (HBP) in Gd-EOB-DTPA-enhanced MRI. Purpose To develop a predictor for insufficient enhancement of liver parenchyma during HBP in Gd-EOB-DTPA-enhanced MRI. Material and Methods In order to formulate a HBP enhancement test (HBP-ET), clinical factors associated with relative enhancement ratio (RER) of liver parenchyma were retrospectively determined from the datasets of 156 patients (Development group) who underwent Gd-EOB-DTPA-enhanced MRI between November 2012 and May 2015. The independent clinical factors were identified by Pearson's correlation and multiple stepwise regression analysis; the performance of HBP-ET was compared to Child-Pugh score (CPS), Model for End-stage Liver Disease score (MELD), and total bilirubin (TBIL) using receiver operating characteristic (ROC) curve analysis. The datasets of 52 patients (Validation group), which were examined between June 2015 and Oct 2015, were applied to validate the HBP-ET. Results Six biochemical parameters independently influenced RER and were used to develop HBP-ET. The mean HBP-ET score of patients with insufficient enhancement was significantly higher than that of patients with sufficient enhancement ( P < 0.001) in both the Development and Validation groups. HBP-ET (area under the curve [AUC] = 0.895) had better performance in predicting insufficient enhancement than CPS (AUC = 0.707), MELD (AUC = 0.798), and TBIL (AUC = 0.729). Conclusion The HBP-ET is more accurate than routine indicators in predicting insufficient enhancement during HBP, which is valuable to aid clinical decisions.