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.

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.

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.

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.

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.

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.

Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis.

OBJECTIVE: To establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs). MATERIALS AND METHODS: Fifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity. RESULTS: Following multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively. CONCLUSION: This study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

CT-guided needle biopsy through mandibular area for the diagnosis of nasopharyngeal carcinoma in the parapharyngeal space.

BACKGROUND AND OBJECTIVE: The primary submucous type of nasopharyngeal carcinoma (NPC) or the recurrent NPC in the parapharyngeal space is difficult to be diagnosed histologically by conventional biopsy because of the obstruction of the surrounding structures. This study was performed to evaluate the needle biopsy approach through the madibular area into the parapharyngeal space under the guidance of computed tomography (CT) for NPC. METHODS: Between July 6, 2005 and October 23, 2009, a total of 6 patients were enrolled into the study. Two patients with cervical lymph node metastasis were clinically suspicious of NPC according to their clinical manifestations. However, no cancer cell could be found by repeated nasopharyngeal biopsies followed by histologic examinations. The other 4 patients were diagnosed with recurrent NPCs by magnetic resonance imaging (MRI) or/and positron emission tomography (PET)-CT scan, showing tumors in the parapharyngeal spaces in 3 patients and enlarged retropharyngeal lymph node in 1 patient. The CT-guided puncture was performed through the mandibular skin and the cutting needle biopsy was taken at the parapharyngeal space focus. RESULTS: All the cutting needle biopsies of projected locations have been performed safely. Finally, all the 7 specimens met the requirement of pathologic diagnosis and the cases were all confirmed histologically to be NPCs. The main complication was mild ache at the puncture point. No blood vessel or nerve was injured and no patient needed special treatment. CONCLUSIONS: The CT-guided puncture biopsy of the parapharyngeal space through the mandibular area is simple and feasible. It can be an additional option for routine nasopharyngeal biopsy.

Analysis of cervical and retropharyngeal lymph node metastases in the patients with hypopharyngeal carcinoma with computed tomography and magnetic resonance imaging.

BACKGROUND AND OBJECTIVE: Hypopharyngeal carcinoma has a high risk for early regional lymphatic dissemination. However, reports about regional lymph node metastases, especially retropharyngeal lymph node metastases, are rare. This research explored the spread of hypopharyngeal carcinoma, especially metastases of the retropharyngeal lymph nodes by studying computed tomography (CT) and magnetic resonance imaging (MRI) images. METHODS: The CT/MRI images of 88 patients with pathologically confirmed hypopharyngeal carcinomas that were performed at our hospital between August 2000 and March 2009 were analyzed retrospectively. The interrelations among local stage and lymph nodes in various regions were analyzed by Chi2 test and multivariate logistical regression. RESULTS: The rate of regional lymph node metastasis for all patients was 73.9%, and the highest rates of positive lymph nodes were at levels IIa (61.4%), IIb (44.3%), and III (37.5%). Metastases to levels I, IV, V, and VI were rare, as were retropharyngeal lymph-node metastases, which were always combined with metastases at levels II and III. Univariate analysis showed that level-IV metastases correlated to metastases at levels Ib and III; retropharyngeal lymph node metastases were correlated to level IIb and bilateral cervical lymph node metastases. Multivariate analysis showed that level-VI metastases correlated to level IV and that retropharyngeal lymph-node metastases correlated to bilateral cervical lymph node metastases. CONCLUSIONS: Regional lymph node metastases in patients with hypopharyngeal carcinoma follow some regulations, and skip metastasis is rare. The highest rates of positive lymph nodes are at levels II and III. Bilateral lymph node metastases may be a risk factor for retropharyngeal lymph node metastases.

Chest CT of COVID-19 in patients with a negative first RT-PCR test: Comparison with patients with a positive first RT-PCR test.

To compare clinical and imaging features between patients with an initial negative reverse-transcription-polymerase chain-reaction (RT-PCR) test and patients with an initial positive RT-PCR test. CT follow-up analysis in the negative RT-PCR group is also described.Thirty-three patients with SARS-CoV-2 infection confirmed by RT-PCR, with 216 lesions upon CT, were included. Demographic information and chest CT imaging features were collected.The average age in the whole study group was 46.9 ±â€Š11.1 years, with 18 males and 15 females. Patients in the positive RT-PCR test group were more likely to have a fever than patients in the negative RT-PCR test group (85.7% vs 50%, P < .05). Lesions in the positive group were more likely to be located in the peripheral area than lesions in the negative group (83.6% vs 68.2%, P < .05). Regarding the appearance of 216 lesions, ground-glass opacities (GGOs) with consolidation (43.2%) was the most common appearance in the negative group, followed by pure GGOs (31.8%), while in the positive group, pure GGOs (32%) and GGOs with interlobular septal thickening (32.8%) were both most frequent, and the difference between them was evident (P < .05). For the follow-up analysis, the largest short-axis of a lesion was smaller upon follow-up (median size 13.6 mm vs 14 mm), albeit by a smaller margin. Pure GGOs decreased in frequency, from 31.3% to 21.3%, while consolidation increased in frequency, from 7.5% to 12.5%.The manifestations of COVID-19 in patients with a first negative RT-PCR test and patients with a positive first RT-PCR test are different to some extent. The consolidation component may increase after follow-up.

Using CT or MRI to assess locoregional spread to determine the radiotherapy target of hypopharyngeal carcinoma.

AIM: To identify the locoregional extension of hypopharyngeal carcinoma (HPC), particularly the invasion of the nasopharynx and skull base, and metastasis of level VI and retropharyngeal lymph node (RPLN) by investigating computed tomography (CT) and magnetic resonance (MR) images; together with the radiotherapy target of HPC. METHODS: CT and MR images of 186 patients with pathologically confirmed HPC between Aug 2000 and Dec 2010 were analyzed retrospectively. We used the χ(2) test and logistic regression to analyze local invasion and regional spread and to determine their relationships. RESULTS: Of the 186 patients, there was only one case of invasion of the nasopharynx without skull base involvement. The rate of regional node metastasis was 79%. There was no significant relationship between T stage and lymph node metastasis (P = 0.1). Level IV metastasis (P = 0.001), RPLN metastasis (P = 0.041) and esophageal invasion (P = 0.003) were significantly correlated with level VI metastasis. Primary tumor subsite (P = 0.024), bilateral cervical node metastasis (P < 0.001) and size of cervical nodes (P = 0.01) significantly contributed to the occurrence of RPLN metastasis. CONCLUSION: The locoregional spread of HPC occurs via certain routes. It is potentially unnecessary to routinely and prophylactically irradiate the nasopharynx and skull base. Patients with early stage HPC should receive bilateral cervical prophylactic irradiation. The decision regarding the administration of prophylactic irradiation to the level VI and RPLN areas should be according to the relative risk factors.

Analysis of risk factors for retropharyngeal lymph node metastasis in carcinoma of the hypopharynx.

BACKGROUND: The purpose of this study was to investigate the incidence of retropharyngeal lymph node (RPLN) metastasis and to explore the associated risk factors using CT and MRI, to direct clinical radiotherapy in hypopharyngeal carcinoma (HPC). METHODS: The CT and MRI images of 218 patients with pathologically confirmed HPC were analyzed retrospectively. The chi-square test and logistic regression were used for statistical analysis. RESULTS: The incidence of RPLN metastasis in HPC was 17.0%, and the highest rate of 36.4% was found in pharyngeal wall carcinoma. None of the patients with N0 classification exhibited RPLN metastasis. Univariate and multivariate analyses demonstrated that primary tumor subsites, bilateral cervical lymph node metastasis, the number and size of cervical lymph nodes, and level V metastasis were significantly associated with RPLN metastasis. CONCLUSIONS: Our findings demonstrated that primary carcinoma subsites and multiple metastatic cervical lymph nodes are the principal risk factors for RPLN metastasis.

[Regional invasion of hypopharyngeal carcinoma base on CT--a report of 65 cases].

BACKGROUND AND OBJECTIVE: Identifying the characteristics of regional extension and accurately evaluating the extent of regional invasion is the key to delineate the target volume of hypopharyngeal carcinoma. This study was to investigate the characteristics of regional invasion of hypopharyngeal carcinoma using the enhanced computed tomography (CT) scan. METHODS: CT images of 65 patients with hypopharyngeal carcinoma treated at Sun Yat-sen University Cancer Center between August 2000 and September 2008 were retrospectively analyzed. The patients included 58 males and two females, with a median age of 55 years. RESULTS: Pyriform sinus carcinoma (50 cases)easily extended to aryepiglottic fold (98%), posterior hypopharyngeal wall (80%) and ipsilateral epiglottis (82%), preepiglottic space (66%), arytenoid cartilage (74%), paraglotic space (82%), ventricular bands (72%), vocal (62%), laminae of thyroid cartilage (58%) and lateral wall of oropharynx (52%). Posterior pharyngeal carcinoma (14 cases) usually invaded pyriform sinus (100%), aryepiglottic fold (92.9%), postcricoid region (71.4%), prevertebral fascia (71.4%) and esophagus (64.3%). One case of postcricoid carcinoma spread to pyriform sinus, posterior wall of hypopharynx, aryepiglottic fold, aryepiglottic cartilage, paraglotic space, cricoid cartilage thyroid cartilage and esophagus. CONCLUSION: Regional invasion of hypopharyngeal carcinoma is mainly direct extension through tissues. Tissues close to the primary tumor site and lack of regional tissue barrier are easier to be encroached and destroyed. The skip lesion is not detected. Routine prophylactic irradiation of nasopharynx and base of skull is not necessary.