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In this study, we investigated whether radiomic features of CT image data can accurately predict HMGA2 and C-MYC gene expression status and identify the patient survival time using a machine learning approach in pancreatic ductal adenocarcinoma (PDAC). A cohort of 111 patients with PDAC was enrolled in our study. Radiomic features were extracted using conventional (shape and texture analysis) and deep learning approaches following to segmentation of preoperative CT data. To predict patient survival time, significant radiomic features were identified using a log-rank test. After surgical resection, level of HMGA2 and C-MYC gene expressions of PDAC tumor regions were classified using a support vector machines method. The model was evaluated in terms of accuracy, sensitivity, specificity, and area under the curve (AUC). Besides, inter-reader reliability analysis was used to demonstrate the robustness of the proposed features. The identified features consistently achieved good performance in survival prediction and classification of gene expression status, on images segmented by different radiologists. Using CT data from 111 patients, six features in the segmented region of images were highly correlated with survival time. Using extracted deep features of excised lesions from 47 patients, we observed an average AUC score of 0.90 with an accuracy of 95% in C-MYC prediction (sensitivity: 92% and specificity: 98%). In HGMA2 group, using shape features, the average AUC score was measured as 0.91 with an accuracy of 88% (sensitivity: 89% and specificity: 88%). In conclusion, the radiomic features of CT image can accurately predict the expression status of HMGA2 and C-MYC genes and identify the survival time of PDAC patients.

Objective To construct an XGBoost prediction model to predict disease severity of COVID-19 based on clinical characteristics dataset of COVID-19 patients. Methods A total of 347 laboratory-confirmed COVID-19 patients with complete medical information admitted from Feb 10 to April 5, 2020 were screened from the medical record system of Huoshenshan Hospital. Firstly, 21 features with significant differences were screened out as input features for the training model. Bayesian optimization was performed on the constructed XGBoost model to adjust the parameters, and the optimal combination of features was filtered based on feature importance. To further analyze the positive and negative effects of the numerical size of each feature on the prediction results, each feature importance was quantified and attributed by using SHapley Additive exPlanations (SHAP). Finally, the performance of the XGBoost prediction model was evaluated, and the model was compared and discussed with other machine learning methods, including support vector machine (SVM), naïve Bayes (NB), logical regression (LR), and k-nearest neighbors (KNN). Results In this study, 21 features with significant differences between the severe and non-severe groups were selected for training and validation. The optimal subset with 10 features in the k-nearest neighbor model obtained the highest value of area under curve (AUC) among the 4 models in the validation set. XGBoost and support vector machine were better than other machine learning methods in terms of prediction performance (AUC: 0.942 0, and 0.959 4 on the test set, respectively), and the training speed of XGBoost was significantly faster. Conclusion A prediction model based on XGBoost is successfully built to achieve early prediction of disease severity of COVID-19 patients.

Objective: To create a deep‐learning automatic segmentation model for esophageal cancer (EC), metastatic lymph nodes (MLNs) and their adjacent structures using the UperNet Swin network and computed tomography angiography (CTA) images and to improve the effectiveness and precision of EC automatic segmentation and TN stage diagnosis. Methods: Attention U‐Net, UperNet Swin, UNet++ and UNet were used to train the EC segmentation model to automatically segment the EC, esophagus, pericardium, aorta and MLN from CTA images of 182 patients with postoperative pathologically proven EC. The Dice similarity coefficient (DSC), sensitivity, and positive predictive value (PPV) were used to assess their segmentation effectiveness. The volume of EC was calculated using the segmentation results, and the outcomes and times of automatic and human segmentation were compared. All statistical analyses were completed using SPSS 25.0 software. Results: Among the four EC autosegmentation models, the UperNet Swin had the best autosegmentation results with a DSC of 0.7820 and the highest values of EC sensitivity and PPV. The esophagus, pericardium, aorta and MLN had DSCs of 0.7298, 0.9664, 0.9496 and 0.5091. The DSCs of the UperNet Swin were 0.6164, 0.7842, 0.8190, and 0.7259 for T1‐4 EC. The volume of EC and its adjacent structures between the ground truth and UperNet Swin model were not significantly different. Conclusions: The UperNet Swin showed excellent efficiency in autosegmentation and volume measurement of EC, MLN and its adjacent structures in different T stage, which can help to T and N stage diagnose EC and will save clinicians time and energy. [ABSTRACT FROM AUTHOR]

Purpose: Using visible human, MRI and ultrasound images, we aim to provide an anatomical basis for the identification and diagnosis of pelvic floor structure and disease by ultrasound imaging. Methods: One Chinese visible human (CVH) image, one American visible human image, 9 MRI images of normal volunteers, and 40 ultrasound images of normal volunteers or pelvic organ prolapse patients were used. Pelvic organs, pelvic floor muscles, and the connective tissue in CVH, VHP, MRI, and ultrasound images were selected for comparative study. Results: We successfully identified the boundary of the anal sphincter complex, including the subcutaneous, superficial, and deep parts of the external anal sphincter, conjoined longitudinal muscles and internal anal sphincter; the levator ani muscle (LAM), including the internal and external parts of the pubovisceral muscle and the superficial and deep parts of the puborectal muscle; the urethral sphincter complex, including the urethral sphincter proper and the urethral compressor; and the perineal body, the rectoperineal muscle and superficial transverse perineal muscle. Conclusions: We successfully recognized and studied the location, subdivisions, 2D morphology and spatial relationships of the LAM, anal sphincter complex, urethral sphincter complex and perineal body in ultrasound images, thereby helping sonologists or clinicians accurately identify pelvic floor muscles and supporting structures in ultrasound images. [ABSTRACT FROM AUTHOR]

In general, COVID-19 has certain features that differ from the other type of pneumonia in chest imaging; however, manual screening of COVID-19 from medical images is a time-consuming and labor-intensive task. The global spread of COVID-19 (also known as SARS-CoV-2) is a major international public health crisis. It should be noted that in Oh's work, bacterial pneumonia was considered as a separate category, while COVID-19 was combined with other viral pneumonia, which is disadvantageous for rapid screening of COVID-19 [[11]]. [Extracted from the article]

Evaluating the severity of ulcerative colitis (UC) through the Mayo endoscopic subscore (MES) is crucial for understanding patient conditions and providing effective treatment. However, UC lesions present different characteristics in endoscopic images, exacerbating interclass similarities and intraclass differences in MES classification. In addition, inexperience and review fatigue in endoscopists introduces nontrivial challenges to the reliability and repeatability of MES evaluations. In this paper, we propose a pyramid hybrid feature fusion framework ( PHF 3 ) as an auxiliary diagnostic tool for clinical UC severity classification. Specifically, the PHF 3 model has a dual-branch hybrid architecture with ResNet50 and a pyramid vision Transformer (PvT), where the local features extracted by ResNet50 represent the relationship between the intestinal wall at the near-shot point and its depth, and the global representations modeled by the PvT capture similar information in the cross-section of the intestinal cavity. Furthermore, a feature fusion module (FFM) is designed to combine local features with global representations, while second-order pooling (SOP) is applied to enhance discriminative information in the classification process. The experimental results show that, compared with existing methods, the proposed PHF 3 model has competitive performance. The area under the receiver operating characteristic curve (AUC) of MES 0, MES 1, MES 2, and MES 3 reached 0.996, 0.972, 0.967, and 0.990, respectively, and the overall accuracy reached 88.91%. Thus, our proposed method is valuable for developing an auxiliary assessment system for UC severity. [ABSTRACT FROM AUTHOR]