The scoring system combined with radiomics and imaging features in predicting the malignant potential of incidental indeterminate small (<20 mm) solid pulmonary nodules.

OBJECTIVE: Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm. METHODS: A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed. RESULTS: Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%. CONCLUSION: The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.

Four New Triterpenoid Saponins from the Root of Ilex centrochinensis.

One new siaresinolic acid saponin (1) and three new rotundic acid saponins (2-4) were isolated from the roots of Ilex centrochinensis. Their structures were confirmed by detailed analysis of standard spectroscopic data (IR, MS, 1D and 2D NMR). Compounds 1-4 exhibited anti-inflammatory activity by inhibiting nitric oxide production in a lipopolysaccharide-induced RAW264.7 cell inflammatory model. However, they showed no significant lipid-lowering activity against the production of triglycerides in the lipid-accumulation model of HepG2 cells induced by oleic acid.

Cancer patient management strategy in a Cancer Center of Zhejiang, China during the COVID-19 pandemic.

BACKGROUND: Due to the increased risk of viral infection and the severe shortage of medical resources during the pandemic of COVID-19, most hospitals in the epidemic areas significantly reduced non-emergency admissions and services, if not closed. As a result, it has been difficult to treat cancer patients on time, which adversely affects their prognosis. To address this problem, cancer centers must develop a strategic plan to manage both inpatients and outpatients during the pandemic, provide them with the necessary treatment, and at the same time prevent the spread of the virus among patients, visitors and medical staff. METHODS: Based upon the epidemic situation in Zhejiang Province, China, the number of running non-emergency medical wards in the Zhejiang Cancer Hospital was gradually increased in a controlled manner. All staff of the hospital received COVID-19 preventive training and was provided with three different levels of protection according to the risks of their services. Only patients without a known history of SARS-CoV-2 contact were eligible to schedule an appointment. Body temperature was measured on all patients upon their arrival at the hospital. Chest CT image, blood cell counting and travel/contact history were investigated in patients with fever. Respiratory tract samples, such as sputum and throat swabs, from all patients, including those clinically suspected of SARS-CoV-2 infection, were collected for nucleic acid detection of SARS-CoV-2 before treatment. RESULTS: A total of 3697 inpatients and 416 outpatients seeking cancer treatment were enrolled from February 1 to April 3, 2020, in compliance with the hospital's infection-control interventions. The clinicopathological parameters of the patients were summarized herein. 4237 samples from 4101 patients produced negative RNA testing results. Four clinically suspected patients all presented negative RNA test results and were excluded from the SARS-CoV-2 infection through follow-up retesting and monitoring. Seven patients with only N-gene positive results were retested, followed by CT scan and SARS-CoV-2 contact history investigation. All of them were finally diagnosed as non-infected patients. There was one outpatient who was confirmed positive by virus RNA test and then followed up. She might be an asymptomatic laboratory-confirmed case. During the study period, there was no SARS-CoV-2 infection among staff, patients and escorts of patients in the Zhejiang Cancer Hospital. CONCLUSION: This study suggested our infection-control interventions, including viral nucleic acid test, could be used as a reliable method to screen cancer patients in the area with moderate COVID-19 prevalence. Cancer may not be a high-risk factor of SARS-CoV-2 infection.

Grade 3 Pancreatic Neuroendocrine Tumors on MDCT: Establishing a Diagnostic Model and Comparing Survival Against Pancreatic Ductal Adenocarcinoma.

OBJECTIVE. The purpose of this study is to establish a diagnostic model for differentiating grade 3 (G3) pancreatic neuroendocrine tumors (PNETs) from pancreatic ductal adenocarcinomas (PDACs) and to analyze survival outcomes. MATERIALS AND METHODS. Twenty patients with G3 PNETs and 58 patients with PDACs confirmed by surgery or biopsy were retrospectively included. Demographic and radiologic information was collected. Univariate analyses and binary logistic regression analyses were performed to identify independent factors and establish a diagnostic model. An ROC curve was created to determine diagnostic ability. Kaplan-Meier survival analysis was performed. RESULTS. Patients with G3 PNETs were more likely to present with normal carbohydrate antigen (CA) 19-9 levels, normal pancreatic ducts, and round tumors with well-defined margins and higher portal enhancement ratios than were patients with PDAC (p < 0.05). After multivariate analysis, a normal CA 19-9 level (odds ratio, 0.0125; 95% CI, 0.0008-0.2036), round tumor shape (odds ratio, 0.0143; 95% CI, 0.0004-0.5461), and pancreatic duct dilation of 4 mm or less (odds ratio, 17.9804; 95% CI, 1.0098-320.1711) were independent predictors of G3 PNETs. The AUC of the ROC curve was 0.916, and sensitivity and specificity were 90.0% and 81.0%, respectively. Furthermore, patients with G3 PNETs had better overall survival than patients with PDACs. Among patients in the G3 PNET subgroup, patients with liver or lymph node metastases had worse overall survival than patients without metastases. CONCLUSION. A diagnostic model was established to differentiate G3 PNETs from PDACs. A normal CA 19-9 level, round tumor shape, and pancreatic duct dilation of 4 mm or less were factors that were strongly predictive of G3 PNET.

Ergosterols with rare peroxide, oxetane ring moiety, and a lactone ring from Aspergillus spectabilis and their immunosuppressive activities.

Ten ergostane-type steroids, including seven undescribed ones named spectasteroids A-G, were obtained from Aspergillus spectabilis. Their structures and absolute configurations were determined based on HRESIMS, NMR, ECD calculations, and single-crystal X-ray diffraction analyses. Structurally, spectasteroid A was a unique example of aromatic ergostane-type steroid that featured a rare peroxide ring moiety; spectasteroid B contained a rare oxetane ring system formed between C-9 and C-14; and spectasteroid C was an unusual 3,4-seco-ergostane steroid with an extra lactone ring between C-3 and C-9. Spectasteroids F and G specifically showed inhibitory effects against concanavalin A-induced T lymphocyte proliferation and lipopolysaccharide-induced B lymphocyte proliferation, with IC50 values ranging from 2.33 to 4.22 µM. Spectasteroid F also showed excellent antimultidrug resistance activity, which remarkable enhanced the inhibitory activity of PTX on the colony formation of SW620/Ad300 cells.

Duodenal neuroendocrine neoplasms on enhanced CT: establishing a diagnostic model with duodenal gastrointestinal stromal tumors in the non-ampullary area and analyzing the value of predicting prognosis.

OBJECTIVE: To identify CT features and establish a diagnostic model for distinguishing non-ampullary duodenal neuroendocrine neoplasms (dNENs) from non-ampullary duodenal gastrointestinal stromal tumors (dGISTs) and to analyze overall survival outcomes of all dNENs patients. MATERIALS AND METHODS: This retrospective study included 98 patients with pathologically confirmed dNENs (n = 44) and dGISTs (n = 54). Clinical data and CT characteristics were collected. Univariate analyses and binary logistic regression analyses were performed to identify independent factors and establish a diagnostic model between non-ampullary dNENs (n = 22) and dGISTs (n = 54). The ROC curve was created to determine diagnostic ability. Cox proportional hazards models were created and Kaplan-Meier survival analyses were performed for survival analysis of dNENs (n = 44). RESULTS: Three CT features were identified as independent predictors of non-ampullary dNENs, including intraluminal growth pattern (OR 0.450; 95% CI 0.206-0.983), absence of intratumoral vessels (OR 0.207; 95% CI 0.053-0.807) and unenhanced lesion > 40.76 HU (OR 5.720; 95% CI 1.575-20.774). The AUC was 0.866 (95% CI 0.765-0.968), with a sensitivity of 90.91% (95% CI 70.8-98.9%), specificity of 77.78% (95% CI 64.4-88.0%), and total accuracy rate of 81.58%. Lymph node metastases (HR: 21.60), obstructive biliary and/or pancreatic duct dilation (HR: 5.82) and portal lesion enhancement ≤ 99.79 HU (HR: 3.02) were independent prognostic factors related to poor outcomes. CONCLUSION: We established a diagnostic model to differentiate non-ampullary dNENs from dGISTs. Besides, we found that imaging features on enhanced CT can predict OS of patients with dNENs.

Differentiation Between Phyllodes Tumors and Fibroadenomas of Breast Using Mammography-based Machine Learning Methods: A Preliminary Study.

OBJECTIVE: To investigate the diagnostic performance of a mammography-based radiomics model for distinguishing phyllodes tumors (PTs) from fibroadenomas (FAs) of the breast. MATERIALS AND METHODS: A total of 156 patients were retrospectively included (75 with PTs, 81 with FAs) and divided into training and validation groups at a ratio of 7:3. Radiomics features were extracted from craniocaudal and mediolateral oblique images. The least absolute shrinkage and selection operator (LASSO) algorithm and principal component analysis (PCA) were performed to select features. Three machine learning classifiers, including logistic regression (LR), K-nearest neighbor classifier (KNN) and support vector machine (SVM), were implemented in the radiomics model, imaging model and combined model. Receiver operating characteristic curves, area under the curve (AUC), sensitivity and specificity were computed. RESULTS: Among 1084 features, the LASSO algorithm selected 17 features, and PCA further selected 6 features. Three machine learning classifiers yielded the same AUC of 0.935 in the validation group for the radiomics model. In the imaging model, KNN yielded the highest accuracy rate of 89.4% and AUC of 0.947 in the validation set. For the combined model, the SVM classifier reached the highest AUC of 0.918 with an accuracy rate of 86.2%, sensitivity of 83.9%, and specificity of 89.4% in the training group. In the validation group, LR yielded the highest AUC of 0.973. The combined model had a relatively higher AUC than the radiomics model or imaging model, especially in the validation group. CONCLUSIONS: Mammography-based radiomics features demonstrate good diagnostic performance for discriminating PTs from FAs.

A noninvasive nomogram model based on CT features to predict DNA mismatch repair deficiency in gastric cancer.

Objectives: DNA mismatch repair deficiency (dMMR) status has served as a positive predictive biomarker for immunotherapy and long-term prognosis in gastric cancer (GC). The aim of the present study was to develop a computed tomography (CT)-based nomogram for preoperatively predicting mismatch repair (MMR) status in GC. Methods: Data from a total of 159 GC patients between January 2020 and July 2021 with dMMR GC (n=53) and MMR-proficient (pMMR) GC (n=106) confirmed by postoperative immunohistochemistry (IHC) staining were retrospectively analyzed. All patients underwent abdominal contrast-enhanced CT. Significant clinical and CT imaging features associated with dMMR GC were extracted through univariate and multivariate analyses. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and internal validation of the cohort data were performed. Results: The nomogram contained four potential predictors of dMMR GC, including gender (odds ratio [OR] 9.83, 95% confidence interval [CI] 3.78-28.20, P < 0.001), age (OR 3.32, 95% CI 1.36-8.50, P = 0.010), tumor size (OR 5.66, 95% CI 2.12-16.27, P < 0.001) and normalized tumor enhancement ratio (NTER) (OR 0.15, 95% CI 0.06-0.38, P < 0.001). Using an optimal cutoff value of 6.6 points, the nomogram provided an area under the curve (AUC) of 0.895 and an accuracy of 82.39% in predicting dMMR GC. The calibration curve demonstrated a strong consistency between the predicted risk and observed dMMR GC. The DCA justified the relatively good performance of the nomogram model. Conclusion: The CT-based nomogram holds promise as a noninvasive, concise and accurate tool to predict MMR status in GC patients, which can assist in clinical decision-making.

Investigation of Parasitic Nematodes Detected in the Feces of Wild Carnivores in the Eastern Qinghai-Tibet Plateau, China.

Wildlife shares grazing areas with herders in the eastern Qinghai-Tibet Plateau, and humans can be infected by zoonotic nematodes through direct contact with animals or contaminated water. In this study, fecal samples (n = 296) from wild carnivores were collected to explore the infection rate and molecular genetic characteristics of nematodes by stratified random sampling in the survey areas. Host species and the nematodes they carried were then identified using 16S rRNA and 18S rRNA gene sequencing, respectively. Statistical analysis, neutrality tests, genetic diversity analysis and Bayesian inferred trees were performed to complete the study. In total, 10 species of nematodes were detected in 240 feces from six species of carnivores identified (including dominant Vulpes ferrilata and Vulpes vulpes), namely Uncinaria stenocephala, Toxascaris sp., Crenosoma vulpis, Parapharyngodon bainae, Oesophagostomum muntiacum, Aspiculuris tetraptera, Mastophorus muris, Nematodirus spathiger, Muellerius capillaris, and Molineus patens. Among these nematodes, U. stenocephala (35.83%, 86/240) and Toxascaris sp. (14.58%, 35/240) were detected at higher rates than the other nematodes (χ2 = 516.909, p < 0.05). Of 17 and 18 haplotypes were found based on the ITS1 gene for U. stenocephala and nad1 gene for Toxascaris sp., respectively. For the first time, using molecular methods, we report the infection of V. ferrilata by U. stenocephala, a potential zoonotic parasite, and suggest Toxascaris sp. may be a newly discovered nematode that lives within the fox intestine.

Diagnostic Value of CT- and MRI-Based Texture Analysis and Imaging Findings for Grading Cartilaginous Tumors in Long Bones.

OBJECTIVE: To confirm the diagnostic performance of computed tomography (CT)-based texture analysis (CTTA) and magnetic resonance imaging (MRI)-based texture analysis for grading cartilaginous tumors in long bones and to compare these findings to radiological features. MATERIALS AND METHODS: Twenty-nine patients with enchondromas, 20 with low-grade chondrosarcomas and 16 with high-grade chondrosarcomas were included retrospectively. Clinical and radiological information and 9 histogram features extracted from CT, T1WI, and T2WI were evaluated. Binary logistic regression analysis was performed to determine predictive factors for grading cartilaginous tumors and to establish diagnostic models. Another 26 patients were included to validate each model. Receiver operating characteristic (ROC) curves were generated, and accuracy rate, sensitivity, specificity and positive/negative predictive values (PPV/NPV) were calculated. RESULTS: On imaging, endosteal scalloping, cortical destruction and calcification shape were predictive for grading cartilaginous tumors. For texture analysis, variance, mean, perc.01%, perc.10%, perc.99% and kurtosis were extracted after multivariate analysis. To differentiate benign cartilaginous tumors from low-grade chondrosarcomas, the imaging features model reached the highest accuracy rate (83.7%) and AUC (0.841), with a sensitivity of 75% and specificity of 93.1%. The CTTA feature model best distinguished low-grade and high-grade chondrosarcomas, with accuracies of 71.9%, and 80% in the training and validation groups, respectively; T1-TA and T2-TA could not distinguish them well. We found that the imaging feature model best differentiated benign and malignant cartilaginous tumors, with an accuracy rate of 89.2%, followed by the T1-TA feature model (80.4%). CONCLUSIONS: The imaging feature model and CTTA- or MRI-based texture analysis have the potential to differentiate cartilaginous tumors in long bones by grade. MRI-based texture analysis failed to grade chondrosarcomas.