Preoperative computed tomography semantic features in predicting lymph node metastasis of part-solid nodules in non-small cell lung cancer: a multicenter retrospective study.

Background: Lymph node metastasis (LNM) is the most common route of metastasis for lung cancer, and it is an independent risk factor for long-term survival and recurrence in patients with non-small cell lung cancer (NSCLC). The purpose of this study was to explore the value of preoperative computed tomography (CT) semantic features in the differential diagnosis of LNM in part-solid nodules (PSNs) of NSCLC. Methods: A total of 955 patients with NSCLC confirmed by postoperative pathology were retrospectively enrolled from January 2019 to March 2023. The clinical, pathological data and preoperative CT images of these patients were investigated and statistically analyzed in order to identify the risk factors for LNM. Multivariate logistic regression was used to select independent risk factors and establish different prediction models. Ten-fold cross-validation was used for model training and validation. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated, and the Delong test was used to compare the predictive performance between the models. Results: LNM occurred in 68 of 955 patients. After univariate analysis and adjustment for confounding factors, smoking history, pulmonary disease, solid component proportion, pleural contact type, and mean diameter were identified as the independent risk factors for LNM. The image predictors model established by the four independent factors of CT semantic features, except smoking history, showed a good diagnostic efficacy for LNM. The AUC in the validation group was 0.857, and the sensitivity, specificity, and accuracy of the model were all 77.6%. Conclusions: Preoperative CT semantic features have good diagnostic value for the LNM of NSCLC. The image predictors model based on pulmonary disease, solid component proportion, pleural contact type, and mean diameter demonstrated excellent diagnostic efficacy and can provide non-invasive evaluation in clinical practice.

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.

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.

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.

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.

Differentiation of gastric schwannomas from gastrointestinal stromal tumors by CT using machine learning.

OBJECTIVE: To identify schwannomas from gastrointestinal stromal tumors (GISTs) by CT features using Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT). METHODS: This study enrolled 49 patients with schwannomas and 139 with GISTs proven by pathology. CT features with P < 0.1 derived from univariate analysis were inputted to four models. Five machine learning (ML) versions, multivariate analysis, and radiologists' subjective diagnostic performance were compared to evaluate diagnosis performance of all the traditional and advanced methods. RESULTS: The CT features with P < 0.1 were as follows: (1) CT attenuation value of unenhancement phase (CTU), (2) portal venous enhancement (CTV), (3) degree of enhancement in the portal venous phase (DEPP), (4) CT attenuation value of portal venous phase minus arterial phase (CTV-CTA), (5) enhanced potentiality (EP), (6) location, (7) contour, (8) growth pattern, (9) necrosis, (10) surface ulceration, (11) enlarged lymph node (LN). LR (M1), RF, DT, and GBDT models contained all of the above 11 variables, while LR (M2) was developed using six most predictive variables derived from (M1). LR (M2) model with AUC of 0.967 in test dataset was thought to be optimal model in differentiating the two tumors. Location in gastric body, exophytic and mixed growth pattern, lack of necrosis and surface ulceration, enlarged lymph nodes, and larger EP were the most important CT features suggestive of schwannomas. CONCLUSION: LR (M2) provided the optimal diagnostic potency among other ML versions, multivariate analysis, and radiologists' performance on differentiation of schwannomas from GISTs.

Comparison of spectral computed tomography imaging parameters between squamous cell carcinoma and adenocarcinoma at the gastroesophageal junction.

OBJECTIVE: To compare the spectral computed tomography (CT) imaging parameters between squamous cell carcinoma (SCC) and adenocarcinoma (AC) at the gastroesophageal junction (GEJ). METHODS: A total of 80 patients were enrolled in this retrospective study. Among them, 35 were diagnosed with SCC (SCC group) and 45 were diagnosed with AC (AC group). All patients underwent an enhanced scan with spectral CT. The following CT imaging parameters were evaluated: iodine concentration (IC), water content (WC), effective atomic number (Eff-Z) and slope of the spectral HU curve (λHU) of lesions. Receiver operating characteristic (ROC) curve was used to analyze the predictive value of spectral CT imaging parameters for diagnosis of SCC and AC. RESULTS: Patients with SCC had lower IC, Eff-Z, and λHU in arterial phase and venous phase compared with AC (p< 0.05). There were no significant differences in WC between the two groups. ROC curve analyses revealed that IC, Eff-Z, and λHU in arterial phase and venous phase were predictors for diagnosis of SCC and AC (AUC > 0.5). Moreover, the IC, Eff-Z and λHU in venous phase had better differential diagnostic performances than that in arterial phase. CONCLUSIONS: Spectral CT could be useful in the differential diagnosis of SCC and AC at the GEJ. Therefore, a routine spectral CT scan is recommended for patients with carcinoma of the GEJ.

Coagulopathy as a Prodrome of Cytokine Storm in COVID-19-Infected Patients.

Background: The rapid coronavirus disease 2019 (COVID-19) pandemic has hit hard on the world and causes panic since the virus causes serious infectious respiratory illness and easily leads to severe conditions such as immune system overactivation or cytokine storm. Due to the limited knowledge on the course of infection of this coronavirus and the lack of an effective treatment for this fatal disease, mortality remains high. The emergence of a cytokine storm in patients with a severe condition has been reported as the top reason of the death of patients with COVID-19 infection. However, the causative mechanism of cytokine storm remains elusive. Thus, we aim to observe the association of coagulopathy (D-dimer) with cytokine (i.e., IL-6) and CT imaging in COVID-19-infected patients. Methods: In this retrospective observational study, we systematically analyzed the comprehensive clinical laboratory data of COVID-19-positive patients in different illness groups of mild, moderate, and severe conditions according to the Chinese Clinical Guidance for COVID-19 Pneumonia Diagnosis and Treatment (7th edition). T tests and chi-square tests were used for two-group comparisons. One-way ANOVA was used for three-group comparisons. Pearson and Spearman correlation coefficients of the D-dimer level with IL-6 and CT imaging were computed at baseline. With regular liquid biopsy approach, D-dimer, IL-6, and neutrophil-to-lymphocyte ratio were recorded repeatedly with a time curve to investigate disease progression, along with CT imaging, and other indicators. Results: All the 64 patients were clinically evaluated and classified into three groups of mild (32 cases), moderate (23 cases), and severe (nine cases) conditions. The D-dimer level positively correlated with IL-6 (R = 0.5) at baseline when the COVID-19-infected patients were admitted. In addition, we observed that D-dimer rises earlier than the cytokine storm represented by IL-6 surge, which suggests that coagulopathy might act as a trigger to potentiate a cytokine storm. Conclusion: Integrated analysis revealed a positive correlation of coagulopathy with cytokine storm in COVID-19-infected patients; the D-dimer rises early, which indicates that coagulopathy acts as a prodrome of cytokine storm. Coagulopathy can be used to monitor early cytokine storm in COVID-19-infected patients.

Metastatic brain tumors respond favorably to pyrotinib in a HER2-positive breast cancer following failure using trastuzumab.

Treatment of breast cancer (BC) with overexpression of human epidermal growth factor receptor 2 (HER2) has undergone a prosperous development with the advent of emerging small molecule tyrosine kinase inhibitors (TKIs). However, their efficacy in brain metastases (BMs) requires further investigation in both clinical trials and practice by specifically targeting this population. We herein reported a HER2-positive metastatic bilateral BC case with symptomatic diffusion of parenchymal BM after first-line treatment with trastuzumab-based regimen. She then received pyrotinib (with a disease-free survival of 5.7 months) followed by whole brain radiotherapy. Unexpectedly, under satisfactory control of intracranial parenchymal lesions, the patient based on clinical manifestations, auxiliary examinations and exclusive diagnosis was diagnosed with meningeal progression, and soon died of tumor progression. Thus, pyrotinib response differed from meningeal to parenchymal BM in BC patients, and the need of comprehensive and systematic evaluation of TKIs in some particular clinical scenarios has become a critical issue.