Predicting lymphovascular invasion in non-small cell lung cancer using deep convolutional neural networks on preoperative chest CT.

RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images with deep learning technology. MATERIALS AND METHODS: This retrospective observational study included a series of consecutive patients who underwent surgical resection for non-small cell lung cancer (NSCLC) and received pathologically confirmed diagnoses. The cohort was randomly divided into a training group comprising 70 % of the patients and a validation group comprising the remaining 30 %. Four distinct deep convolutional neural network (DCNN) prediction models were developed, incorporating different combination of two-dimensional (2D) and three-dimensional (3D) CT imaging features as well as clinical-radiological data. The predictive capabilities of the models were evaluated by receiver operating characteristic curves (AUC) values and confusion matrices. The Delong test was utilized to compare the predictive performance among the different models. RESULTS: A total of 3034 patients with NSCLC were recruited in this study including 106 LVI+ patients. In the validation cohort, the Dual-head Res2Net_3D23F model achieved the highest AUC of 0.869, closely followed by the models of Dual-head Res2Net_3D3F (AUC, 0.868), Dual-head Res2Net_3D (AUC, 0.867), and EfficientNet-B0_2D (AUC, 0.857). There was no significant difference observed in the performance of the EfficientNet-B0_2D model when compared to the Dual-head Res2Net_3D3F and Dual-head Res2Net_3D23F. CONCLUSION: Findings of this study suggest that utilizing deep convolutional neural network is a feasible approach for predicting pathological LVI in patients with NSCLC.

Convolutional neural network applied to preoperative venous-phase CT images predicts risk category in patients with gastric gastrointestinal stromal tumors.

OBJECTIVE: The risk category of gastric gastrointestinal stromal tumors (GISTs) are closely related to the surgical method, the scope of resection, and the need for preoperative chemotherapy. We aimed to develop and validate convolutional neural network (CNN) models based on preoperative venous-phase CT images to predict the risk category of gastric GISTs. METHOD: A total of 425 patients pathologically diagnosed with gastric GISTs at the authors' medical centers between January 2012 and July 2021 were split into a training set (154, 84, and 59 with very low/low, intermediate, and high-risk, respectively) and a validation set (67, 35, and 26, respectively). Three CNN models were constructed by obtaining the upper and lower 1, 4, and 7 layers of the maximum tumour mask slice based on venous-phase CT Images and models of CNN_layer3, CNN_layer9, and CNN_layer15 established, respectively. The area under the receiver operating characteristics curve (AUROC) and the Obuchowski index were calculated to compare the diagnostic performance of the CNN models. RESULTS: In the validation set, CNN_layer3, CNN_layer9, and CNN_layer15 had AUROCs of 0.89, 0.90, and 0.90, respectively, for low-risk gastric GISTs; 0.82, 0.83, and 0.83 for intermediate-risk gastric GISTs; and 0.86, 0.86, and 0.85 for high-risk gastric GISTs. In the validation dataset, CNN_layer3 (Obuchowski index, 0.871) provided similar performance than CNN_layer9 and CNN_layer15 (Obuchowski index, 0.875 and 0.873, respectively) in prediction of the gastric GIST risk category (All P >.05). CONCLUSIONS: The CNN based on preoperative venous-phase CT images showed good performance for predicting the risk category of gastric GISTs.

Relationships between Size-specific Dose Estimate and Signal to Noise Ratio under Chest CT Examinations with Tube Current Modulation.

PURPOSE: Exploring the relationship between the signal-to-noise ratio (SNR) of organs and size-specific dose estimate (SSDE) in tube current modulation (TCM) chest CT examination. METHODS: Forty patients who received TCM chest CT scanning were retrospectively collected and divided into four groups according to the tube voltage and sexes. We chose to set up the region of interest (ROI) at the tracheal bifurcation and its upper and lower parts in slice images of the heart, aorta, lungs, paracranial muscles, and female breast, and the SNR of each organ was calculated. We also calculated the corresponding axial volume CT dose index (CTDIvolz) and axial size-specific dose estimate (SSDEz). RESULTS: The correlation analysis showed that the correlation between the SNR of the slice images of most organs and SSDEz was more significant than 0.8, and that between the SNR and CTDIvol was more significant than 0.7. The simple linear regression analysis results showed that when the sex is the same, the SNR of the same organ at 100kVp was higher than 120kVp, except for the lung. In multiple regression analysis, the result indicated that the determination coefficients of the SNR and SSDEz of the four groups were 0.934, 0.971, 0.905, and 0.709, respectively. CONCLUSION: In chest CT examinations with TCM, the correlation between the SNR of each organ in slice images and SSDEz was better than that of CTDIvolz. And when the SSDEz was the same, the SNR at 100 kVp was better than that at 120 kVp.

Clinical and radiological characteristics of pediatric COVID-19 before and after the Omicron outbreak: a multi-center study.

Introduction: The emergence of the Omicron variant has seen changes in the clinical and radiological presentations of COVID-19 in pediatric patients. We sought to compare these features between patients infected in the early phase of the pandemic and those during the Omicron outbreak. Methods: A retrospective study was conducted on 68 pediatric COVID-19 patients, of which 31 were infected with the original SARS-CoV-2 strain (original group) and 37 with the Omicron variant (Omicron group). Clinical symptoms and chest CT scans were examined to assess clinical characteristics, and the extent and severity of lung involvement. Results: Pediatric COVID-19 patients predominantly had normal or mild chest CT findings. The Omicron group demonstrated a significantly reduced CT severity score than the original group. Ground-glass opacities were the prevalent radiological findings in both sets. The Omicron group presented with fewer symptoms, had milder clinical manifestations, and recovered faster than the original group. Discussion: The clinical and radiological characteristics of pediatric COVID-19 patients have evolved with the advent of the Omicron variant. For children displaying severe symptoms warranting CT examinations, it is crucial to weigh the implications of ionizing radiation and employ customized scanning protocols and protective measures. This research offers insights into the shifting disease spectrum, aiding in the effective diagnosis and treatment of pediatric COVID-19 patients.

The value of MRI for downgrading of breast suspicious lesions detected on ultrasound.

BACKGROUND: Most of suspicious lesions classified as breast imaging reporting and data system (BI-RADS) 4A and 4B categories on ultrasound (US) were benign, resulting in unnecessary biopsies. MRI has a high sensitivity to detect breast cancer and high negative predictive value (NPV) to exclude malignancy. The purpose of this study was to investigate the value of breast MRI for downgrading of suspicious lesions with BI-RADS 4A and 4B categories on US. METHODS: Patients who underwent breast MRI for suspicious lesions classified as 4A and 4B categories were included in this retrospective study. Two radiologists were aware of the details of suspicious lesions detected on US and evaluated MR images. MRI BI-RADS categories were given by consensus on the basis on dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). Pathological results and imaging follow-up at least 12 months were used as a reference standard. Sensitivity, specificity, positive predictive value (PPV), NPV and their 95% confidence interval (CI) were calculated for MRI findings. RESULTS: One sixty seven patients with 186 lesions (US 4A category: 145, US 4B category: 41) consisted of the study cohort. The malignancy rate was 34.9% (65/186). On MRI, all malignancies showed true-positive results and 92.6% (112/121) benign lesions were correctly diagnosed. MRI increased PPV from 34.9% (65/186) to 87.8% (65/74) and reduced the false-positive biopsies by 92.6% (112/121). The sensitivity, specificity, PPV and NPV of MRI were 100% (95% CI: 94.5%-100%), 92.6% (95% CI: 86.3%-96.5%), 87.8% (95% CI: 78.2%-94.3%) and 100% (95% CI: 96.8%-100%), respectively. 2.2% (4/186) of suspicious lesions were additionally detected on MRI, 75% (3/4) of which were malignant. CONCLUSION: MRI could downgrade suspicious lesions classified as BI-RADS 4A and 4B categories on US and avoided unnecessary benign biopsies without missing malignancy. Additional suspicious lesions detected on MRI needed further work-up.

Serum biomarker panel for disease severity and prognosis in patients with COVID-19.

BACKGROUND: Coronavirus disease-2019 (COVID-19) has become a worldwide emergency and has had a severe impact on human health. Inflammatory factors have the potential to either enhance the efficiency of host immune responses or damage the host organs with immune overreaction in COVID-19. Therefore, there is an urgent need to investigate the functions of inflammatory factors and serum markers that participate in disease progression. METHODS: In total, 54 COVID-19 patients were enrolled in this study. Disease severity was evaluated by clinical evaluation, laboratory tests, and computed tomography (CT) scans. Data were collected at: admission, 3-5 days after admission, when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA detection became negative, and composite endpoint. RESULTS: We found that the positive rate in sputum was three times higher than that in throat swabs. Higher levels of C-reactive protein (CRP), lactate dehydrogenase (LDH), D-dimer (D-D), interleukin-6 (IL-6) and neutrophil-to-lymphocyte ratio (NLR) or lower lymphocyte counts suggested more severe disease, and the levels of cytokines and serum markers were intrinsically correlated with disease progression. When SARS-CoV-2 RNA detection became negative, the receiver operating characteristic (ROC) curve demonstrated that LDH had the highest sensitivity independently, and four indicators (NLR, CRP, LDH, and D-D) when combined had the highest sensitivity in distinguishing critically ill patients from mild ones. CONCLUSIONS: Monitoring dynamic changes in NLR, CRP, LDH, IL-6, and D-D levels, combined with CT imaging and viral RNA detection in sputum, could aid in severity evaluation and prognosis prediction and facilitate COVID-19 treatment.

Risk prediction model establishment with tri-phasic CT image features for differential diagnosis of adrenal pheochromocytomas and lipid-poor adenomas: Grouping method.

Objectives: The purpose of this study was to establish a risk prediction model for differential diagnosis of pheochromocytomas (PCCs) from lipid-poor adenomas (LPAs) using a grouping method based on tri-phasic CT image features. Methods: In this retrospective study, we enrolled patients that were assigned to a training set (136 PCCs and 183 LPAs) from two medical centers, along with an external independent validation set (30 PCCs and 54 LPAs) from another center. According to the attenuation values in unenhanced CT (CTu), the lesions were divided into three groups: group 1, 10 HU < CTu ≤ 25 HU; group 2, 25 HU < CTu ≤ 40 HU; and group 3, CTu > 40 HU. Quantitative and qualitative CT imaging features were calculated and evaluated. Univariate, ROC, and binary logistic regression analyses were applied to compare these features. Results: Cystic degeneration, CTu, and the peak value of enhancement in the arterial and venous phase (DEpeak) were independent risk factors for differential diagnosis of adrenal PCCs from LPAs. In all subjects (groups 1, 2, and 3), the model formula for the differentiation of PCCs was as follows: Y = -7.709 + 3.617*(cystic degeneration) + 0.175*(CTu ≥ 35.55 HU) + 0.068*(DEpeak ≥ 51.35 HU). ROC curves were drawn with an AUC of 0.95 (95% CI: 0.927-0.973) in the training set and 0.91 (95% CI: 0.860-0.929) in the external validation set. Conclusion: A reliable and practical prediction model for differential diagnosis of adrenal PCCs and LPAs was established using a grouping method.

Risk stratification of 2- to 5-cm gastric stromal tumors based on clinical and computed tomography manifestations.

OBJECTIVE: To evaluate the risk stratification of 2- to 5-cm gastric stromal tumors (GSTs) by analyzing their clinical and computed tomography (CT) manifestations with the goal of providing imaging evidence for rational selection of surgical methods. METHODS: This study involved 223 patients with pathologically diagnosed GSTs of 2 to 5 cm in diameter. According to the pathological results and malignant risk category, the patients were divided into a low-risk biological behavior group (very low and low risk) and high-risk biological behavior group (intermediate and high risk). The clinical and CT manifestations were compared between the groups. The chi-square test was used to analyze categorical variables, and the independent-samples t test was used to analyze continuous variables. Multivariate logistic regression and receiver operating characteristic curve analysis were performed for statistically significant variables. RESULTS: The tumor contour, necrosis, surface ulceration, and long diameter were significantly different between the low-risk group and the high-risk group (P < 0.05). Multivariate logistic regression analysis showed that the tumor contour and long diameter were independent risk factors. The area under the curve was 0.82, and the accuracy, sensitivity, and specificity were 0.78, 77.4 %, and 79.7 %, respectively. CONCLUSIONS: The risk associated with 2- to 5-cm GSTs can be preoperatively predicted in an indirect manner through analysis of clinical and CT manifestations, and this model has high diagnostic value.

Tumor immunosuppressive microenvironment modulating hydrogels for second near-infrared photothermal-immunotherapy of cancer.

Immunotherapy has recently been seen as a hopeful therapeutic device to inhibit tumor growth and metastasis, while the curative efficacy is limited by intrinsic immunosuppressive tumor microenvironment. Herein, we reported a tumor immunosuppressive microenvironment modulating hydrogel (TIMmH) platform to achieve second near-infrared (NIR-II) photothermal therapy (PTT) combined immunotherapy for durable inhibition of breast cancer. This TIMmH platform was synthesized through co-loading of NIR-II photothermal nanoagent and an immunoadjuvant cytosine-phosphateguanosine oligodeoxynucleotides (CpG ODNs) into the alginate hydrogel (ALG). Upon the administration of ALG into the tumor, the TIMmH was in situ formed via the coordination effect with Ca2+, locally encapsulating the semiconducting polymer nanoparticles (SPIIN) and CpG in the colloid, achieving to prolong the accumulation time and prevent the premature damage and release of immunotherapeutic agents. Upon 1064-nm photoirradiation, the TIMmHSD was able to elevate the intratumoral temperature for the ablation of tumors, which could induce the apoptosis of tumor cells and achieve thermal immune activation by regulating of an immunosuppressive microenvironment. The TIMmH-mediated combined treatment effectively suppressed the growths of breast cancers, and even acquired a sustained inhibition of the lung metastasis. This study provides a novel tumor immunosuppressive microenvironment modulating hydrogel platform with NIR-II photoexcited capacity for the safe, effective and durable lung metastasis-inhibiting breast cancer treatment.

Intra- and Peritumoral Radiomics Model Based on Early DCE-MRI for Preoperative Prediction of Molecular Subtypes in Invasive Ductal Breast Carcinoma: A Multitask Machine Learning Study.

Purpose: The aim of this study is to investigate radiomics features extracted from the optimal peritumoral region and the intratumoral area on the early phase of dynamic contrast-enhanced MRI (DCE-MRI) for predicting molecular subtypes of invasive ductal breast carcinoma (IDBC). Methods: A total of 422 IDBC patients with immunohistochemical and fluorescence in situ hybridization results from two hospitals (Center 1: 327 cases, Center 2: 95 cases) who underwent preoperative DCE-MRI were retrospectively enrolled. After image preprocessing, radiomic features were extracted from the intratumoral area and four peritumoral regions on DCE-MRI from two centers, and selected the optimal peritumoral region. Based on the intratumoral, peritumoral radiomics features, and clinical-radiological characteristics, five radiomics models were constructed through support vector machine (SVM) in multiple classification tasks related to molecular subtypes and visualized by nomogram. The performance of radiomics models was evaluated by receiver operating characteristic curves, confusion matrix, calibration curves, and decision curve analysis. Results: A 6-mm peritumoral size was defined the optimal peritumoral region in classification tasks of hormone receptor (HR)-positive vs others, triple-negative breast cancer (TNBC) vs others, and HR-positive vs human epidermal growth factor receptor 2 (HER2)-enriched vs TNBC, and 8 mm was applied in HER2-enriched vs others. The combined clinical-radiological and radiomics models in three binary classification tasks (HR-positive vs others, HER2-enriched vs others, TNBC vs others) obtained optimal performance with AUCs of 0.838, 0.848, and 0.930 in the training cohort, respectively; 0.827, 0.813, and 0.879 in the internal test cohort, respectively; and 0.791, 0.707, and 0.852 in the external test cohort, respectively. Conclusion: Radiomics features in the intratumoral and peritumoral regions of IDBC on DCE-MRI had a potential to predict the HR-positive, HER2-enriched, and TNBC molecular subtypes preoperatively.

The Role of ET-1 in Early Cerebral Microcirculation Changes after Subarachnoid Hemorrhage.

Subarachnoid hemorrhage (SAH), especially aneurysmal subarachnoid hemorrhage, is a serious cerebrovascular disease with high mortality and morbidity. However, there is no effective treatment in clinics. In recent years, more and more studies have shown that early brain injury (EBI) may be an important reason for poor prognosis of SAH. Explore the mechanism of early brain injury after subarachnoid hemorrhage (SAH). In this study, 20 male New Zealand white rabbits were selected and divided into the experimental group and sham operation group, with 10 rabbits in each group. The neurobehavioral scores, food intake, and cerebral perfusion parameters, cerebral blood volume (CBV), cerebral blood flow velocity (CBF), ET-1, IL-1, and IL-6, in rabbit plasma were compared. The food intake scores and neurological dysfunction scores of the experimental group at 1 h, 6 h, 24 h, and 72 h after modeling were higher than those of the sham operation group, which had a statistical significance (P < 0.05). The dysfunction scores all showed a gradual decrease; the CBV and CBF values of the experimental group at 1 h, 6 h, 24 h, and 72 h after modeling were all lower than those of the sham operation group, which had a statistical significance (P < 0.05), and the MTT values were all higher than that of the sham operation group, which had a statistical significance (P < 0.05). The TTP values of rats in the experimental group were higher than those in the sham operation group at 6 h, 24 h, and 72 h after modeling (P < 0.05), the experimental group was in the modeling. The levels of serum ET-1, IL-1, and IL-6 at 1 h, 6 h, 24 h, and 72 h were higher than those in the sham operation group, which had a statistical significance (P < 0.05). New Zealand white rabbits can have brain perfusion volume disorder, inflammatory reaction, and cerebral vasospasm in the early stage after SAH, and brain injury can appear in the early stage.

An Analysis of Computed Tomography Imaging Features and Predictive Factors for Postoperative Recurrence and Metastasis of Abdominal Paragangliomas.

Methods: We studied 51 abdominal PGL patients at the First Affiliated Hospital of Bengbu Medical College, Tongde Hospital, and Sir Run Shaw Hospital, Hangzhou, Zhejiang Province, China, from June 2009 to May 2019. Thereafter, the clinical research data, tumor biomarkers, and CT features were compared between the aggressive PGLs and the nonaggressive PGLs using independent-samples t-tests and chi-square tests. Results: Of the 51 cases, 43 were benign and 8 had malignant tendencies. Postoperative recurrence and metastasis were more likely to occur when the tumor diameter was >8 cm or/and the enhancement degree was not obvious. Clinical symptoms, tumor markers, sex, age, and CT image characteristics including morphology, presence of cystic degeneration, "pointed peach" sign, calcification, hemorrhage, enlarged lymph nodes, and peritumor and intratumor blood vessels were not significantly different between the two groups (p > 0.05). Conclusion: Our findings suggest that CT features, including size >8 cm and enhancement degree, could provide important evidence to assess risk factors for aggressive PGLs.

Global burden of oropharyngeal cancer attributable to human papillomavirus by anatomical subsite and geographic region.

BACKGROUND: Oropharynx is the anatomical site with the highest human papillomavirus (HPV) infection in head and neck. Many studies on HPV prevalence and p16INK4a positivity in oropharyngeal cancer have been published in recent years. We aimed to update the global burden estimates of oropharyngeal cancer attributable to HPV with the latest data and estimate global burden of tonsillar cancer and base of tongue cancer attributable to HPV by region and country. METHODS: We calculated the number of new cancer cases using the Cancer Incidence in Five Continents Volume XI (CI5XI) and country-specific population in 2012 issued by the United Nations. Estimates of HPV prevalence and p16INK4a positivity were obtained from literature search and pooled analyses where necessary. RESULTS: Globally the number of oropharyngeal cancer and tonsillar cancer attributable to HPV were 42,000 and 20,000 in 2012, corresponding to AFs of 42.7% and 52.7%. The number of cancer cases attributable to HPV among males was about 4-fold greater than that among females. For both oropharyngeal cancer and tonsillar cancer, AFs were higher in more developed countries. Among HPV positive oropharyngeal cancer cases, 86.7%, 87.8%, and 92.5% could have been prevented by bivalent (2v), quadrivalent (4v), and nonavalent (9v) HPV vaccines. CONCLUSIONS: It is worth considering the inclusion of HPV immunization in males, especially in the regions where oropharyngeal cancer is highly prevalent.

CT-based radiomic nomogram for predicting the severity of patients with COVID-19.

BACKGROUND: The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severity of COVID-19 determines the management, treatment, and even prognosis. We aim to develop and validate a radiomics nomogram for identifying patients with severe COVID-19. METHODS: There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts, respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness. RESULTS: The radiomics signature consisting of four selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P < 0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19. CONCLUSION: We present an easy-to-use radiomics nomogram to identify the patients with severe COVID-19 for better guiding a prompt management and treatment.

A Mammography-Based Nomogram for Prediction of Malignancy in Breast Suspicious Calcification.

AIM: To establish a predictive nomogram for malignancy risk stratification of micro-calcifications (MCCs) detected on mammography. MATERIALS AND METHODS: Consecutive mammograms from January 2017 to March 2021 were retrospectively reviewed. Traditional clinical features were recorded and mammographic features were estimated according to the 5th BI-RADS. A nomogram was developed to graphically predict the malignancy risk based on multivariate logistic regression analysis. The discrimination and calibration performance of the prediction model was assessed. RESULTS: There were 123 cases of suspicious MCCs with final pathological results identified with a malignancy rate of 55.2%. The malignancy rates of subgroups divided according to the morphology and distribution of MCCs, age, menopausal status and the maximum diameter of MCCs were significantly different. Multivariate logistic analysis showed that a menopause status of postmenopausal, maximum diameters of MCCs ≥2 cm, the morphology of MCCs as fine pleomorphic or fine linear or branching, and the distribution of MCCs as linear or segmental were predictive of a higher probability of malignancy. A prediction nomogram was developed based on four risk factors, including menopausal status as well as the maximum diameters, distribution and morphology of the MCCs. The AUC of that nomogram was 0.839 (95%CI:0.771-0.903). CONCLUSION: In mammography, the morphology, distribution and maximum diameter of MCCs, and the menopausal status are independent predictors of malignant suspicious MCCs and are readily available in the clinical setting. The nomogram developed in this study for individualized malignancy risk stratification of suspicious MCCs shows a reliable discrimination performance.

Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors.

We aimed to build radiomics models based on triple-phase CT images combining clinical features to predict the risk rating of gastrointestinal stromal tumors (GISTs). A total of 231 patients with pathologically diagnosed GISTs from July 2012 to July 2020 were categorized into a training data set (82 patients with high risk, 80 patients with low risk) and a validation data set (35 patients with high risk, 34 patients with low risk) with a ratio of 7:3. Four diagnostic models were constructed by assessing 20 clinical characteristics and 18 radiomic features that were extracted from a lesion mask based on triple-phase CT images. The receiver operating characteristic (ROC) curves were applied to calculate the diagnostic performance of these models, and ROC curves of these models were compared using Delong test in different data sets. The results of ROC analyses showed that areas under ROC curves (AUC) of model 4 [Clinic + CT value of unenhanced (CTU) + CT value of arterial phase (CTA) + value of venous phase (CTV)], model 1 (Clinic + CTU), model 2 (Clinic + CTA), and model 3 (Clinic + CTV) were 0.925, 0.894, 0.909, and 0.914 in the training set and 0.897, 0.866, 0,892, and 0.892 in the validation set, respectively. Model 4, model 1, model 2, and model 3 yielded an accuracy of 88.3%, 85.8%, 86.4%, and 84.6%, a sensitivity of 85.4%, 84.2%, 76.8%, and 78.0%, and a specificity of 91.2%, 87.5%, 96.2%, and 91.2% in the training set and an accuracy of 88.4%, 84.1%, 82.6%, and 82.6%, a sensitivity of 88.6%, 77.1%, 74.3%, and 85.7%, and a specificity of 88.2%, 91.2%, 91.2%, and 79.4% in the validation set, respectively. There was a significant difference between model 4 and model 1 in discriminating the risk rating in gastrointestinal stromal tumors in the training data set (Delong test, p < 0.05). The radiomic models based on clinical features and triple-phase CT images manifested excellent accuracy for the discrimination of risk rating of GISTs.

Menetrier's disease and differential diagnosis: A case report.

BACKGROUND: Gastric mucosal hypertrophy, also known as Menetrier's disease (MD), is more common in men over 50 years of age, and the cause is unknown. The symptoms of the disease are atypical, mostly accompanied by hypoproteinemia and edema, and sometimes accompanied by symptoms such as epigastric pain, weight loss, and diarrhea. Most experts believe that the site of the disease is mainly located in the fundus of the stomach and the body of the stomach. We found that the site of the disease in this patient involved the antrum of the stomach. CASE SUMMARY: We introduced the case of a 24-year-old woman who had repeated vomiting for 5 d and was admitted to our hospital. After various examinations such as computed tomography and pathology in our hospital, the final diagnosis of the presented case is MD. The salient feature is that the mucosal folds in the fundus and body of the stomach are huge and present in the shape of gyrus. The greater curvature is more prominent, and there are multiple erosions or ulcers on the folds. The patient did not undergo gastric surgery and did not undergo re-examination. She is drinking Chinese medicine for treatment, and her vomiting and abdominal pain symptoms have improved. This disease is relatively rare in clinical practice, and it is easy to be misdiagnosed as gastric cancer, chronic gastritis and gastric lymphoma, etc. CONCLUSION: MD can occur in the antrum, it is necessary to raise awareness of the disease and reduce misdiagnosis.

A novel CT-based radiomics in the distinction of severity of coronavirus disease 2019 (COVID-19) pneumonia.

BACKGROUND: Convenient and precise assessment of the severity in coronavirus disease 2019 (COVID-19) contributes to the timely patient treatment and prognosis improvement. We aimed to evaluate the ability of CT-based radiomics nomogram in discriminating the severity of patients with COVID-19 Pneumonia. METHODS: A total of 150 patients (training cohort n = 105; test cohort n = 45) with COVID-19 confirmed by reverse transcription polymerase chain reaction (RT-PCR) test were enrolled. Two feature selection methods, Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO), were used to extract features from CT images and construct model. A total of 30 radiomic features were finally retained. Rad-score was calculated by summing the selected features weighted by their coefficients. The radiomics nomogram incorporating clinical-radiological features was eventually constructed by multivariate regression analysis. Nomogram, calibration, and decision-curve analysis were all assessed. RESULTS: In both cohorts, 40 patients with COVID-19 pneumonia were severe and 110 patients were non-severe. By combining the 30 radiomic features extracted from CT images, the radiomics signature showed high discrimination between severe and non-severe patients in the training set [Area Under the Curve (AUC), 0.857; 95% confidence interval (CI), 0.775-0.918] and the test set (AUC, 0.867; 95% CI, 0.732-949). The final combined model that integrated age, comorbidity, CT scores, number of lesions, ground glass opacity (GGO) with consolidation, and radiomics signature, improved the AUC to 0.952 in the training cohort and 0.98 in the test cohort. The nomogram based on the combined model similarly exhibited excellent discrimination performance in both training and test cohorts. CONCLUSIONS: The developed model based on a radiomics signature derived from CT images can be a reliable marker for discriminating the severity of COVID-19 pneumonia.

Polymer-based hydrogels with local drug release for cancer immunotherapy.

Immunotherapy that boosts the body's immune system to treat local and distant metastatic tumors has offered a new treatment option for cancer. However, cancer immunotherapy via systemic administration of immunotherapeutic agents often has two major issues of limited immune responses and potential immune-related adverse events in the clinic. Hydrogels, a class of three-dimensional network biomaterials with unique porous structures can achieve local delivery of drugs into tumors to trigger the antitumor immunity, resulting in amplified immunotherapy at lower dosages. In this review, we summarize the recent development of polymer-based hydrogels as drug release systems for local delivery of various immunotherapeutic agents for cancer immunotherapy. The constructions of polymer-based hydrogels and their local delivery of various drugs in tumors to achieve sole immunotherapy, and chemotherapy-, and phototherapy-combinational immunotherapy are introduced. Furthermore, a brief conclusion is given and existing challenges and further perspectives of polymer-based hydrogels for cancer immunotherapy are discussed.

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS.

BACKGROUND: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. METHODS: This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. CONCLUSIONS: The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.