Knowledge, attitude, and practice toward total neoadjuvant therapy for rectal cancer among oncologists in China: A survey study.

This cross-sectional survey study aimed to explore the knowledge, attitude, and practice (KAP) toward total neoadjuvant therapy (TNT) for rectal cancer (RC) among specialists in Hainan Province, China. RC specialists working in Hainan Province (China) were enrolled in this cross-sectional study between March and June 2023. A self-designed questionnaire was used to collect the participants' characteristics and KAP toward TNT for RC. A total of 279 valid questionnaires were collected. The KAP scores were 15.91 ± 6.02 (possible range: 0-24), 34.16 ± 5.11 (possible range: 10-50), and 12.42 ± 1.83 (possible range: 3-15), respectively. The KAP scores of specialists who had applied TNT in clinical practice or research and had evaluated RC patients treated with TNT were significantly higher than those who had not (all P < 0.05). The structural equation model showed that knowledge of TNT directly affected attitude (ß = 0.292, P = 0.007) and practice (ß = 0.912, P = 0.007), and attitude toward TNT also had a direct effect on practice (ß = 1.047, P = 0.008). In conclusion, RC specialists in Hainan (China) had inadequate knowledge, negative attitudes, and sufficient practice toward TNT in Hainan Province, China. It is necessary to enhance education for RC specialists to improve their knowledge and attitude toward TNT.

The efficacy of using a multiparametric magnetic resonance imaging-based radiomics model to distinguish glioma recurrence from pseudoprogression.

OBJECTIVE: The early differential diagnosis of the postoperative recurrence or pseudoprogression (psPD) of a glioma is of great guiding significance for individualized clinical treatment. This study aimed to evaluate the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics model to distinguish between the postoperative recurrence and psPD of a glioma early on and in a noninvasive manner. METHODS: A total of 52 patients with gliomas who attended the Hainan Provincial People's Hospital between 2000 and 2021 and met the inclusion criteria were selected for this study. 1137 and 1137 radiomic features were extracted from T1 enhanced and T2WI/FLAIR sequence images, respectively.After clearing some invalid information and LASSO screening, a total of 9 and 10 characteristic radiological features were extracted and randomly divided into the training set and the test set according to 7:3 ratio. Select-Kbest and minimum Absolute contraction and selection operator (LASSO) were used for feature selection. Support vector machine and logistic regression were used to form a multi-parameter model for training and prediction. The optimal sequence and classifier were selected according to the area under the curve (AUC) and accuracy. RESULTS: Radiomic models 1, 2 and 3 based on T1WI, T2FLAIR and T1WI + T2T2FLAIR sequences have better performance in the identification of postoperative recurrence and false progression of T1 glioma. The performance of model 2 is more stable, and the performance of support vector machine classifier is more stable. The multiparameter model based on CE-T1 + T2WI/FLAIR sequence showed the best performance (AUC:0.96, sensitivity: 0.87, specificity: 0.94, accuracy: 0.89,95% CI:0.93-1). CONCLUSION: The use of multiparametric MRI-based radiomics provides a noninvasive, stable, and accurate method for differentiating between the postoperative recurrence and psPD of a glioma, which allows for timely individualized clinical treatment.

Hepatic artery diameter predicts bleeding risk after gastroesophageal varices treatment: a contrast-enhanced CT study.

OBJECTIVE: Portal hypertension leads to hepatic artery dilatation and a higher risk of bleeding. We tried to identify the bleeding risk after gastroesophageal varices (GOV) treatment using hepatic artery diameter of contrast-enhanced CT. METHODS: Retrospective retrieval of 258 patients with cirrhosis who underwent contrast-enhanced CT from January 2022 to May 2023 and endoscopy within one month thereafter at Hainan Affiliated Hospital of Hainan Medical University. Cirrhotic patients before GOV treatment were used as the test cohort (n = 199), and cirrhotic patients after GOV treatment were used as the validation cohort (n = 59). The grading and bleeding risk was classified according to the endoscopic findings. Arterial-phase images of contrast-enhanced CT were used for coronal reconstruction, and the midpoint diameter of the hepatic artery was measured on coronal images. The optimal cutoff value for identifying bleeding risk was analyzed and calculated in the test cohort, and its diagnostic performance was evaluated in the validation cohort. RESULTS: In the test cohort, hepatic artery diameters were significantly higher in high-risk GOV than in low-risk GOV [4.69 (4.31, 5.56) vs. 3.10 (2.59, 3.77), P < 0.001]. With a hepatic artery diameter cutoff value of 4.06 mm, the optimal area under the operating characteristic curve was 0.940 (95% confidence interval: 0.908-0.972), with a sensitivity of 0.887, a specificity of 0.892, a positive predictive value of 0.904, and a negative predictive value of 0.874 for identifying bleeding risk in the test cohort, while in the validation cohort, the sensitivity was 0.885, specificity was 0.939, positive predictive value was 0.920, and negative predictive value was 0.912. CONCLUSION: Hepatic artery diameter has high diagnostic performance in identifying bleeding risk after GOV treatment.

Relationship between rs7586085, GALNT3 and CCDC170 gene polymorphisms and the risk of osteoporosis among the Chinese Han population.

Osteoporosis (OP) has plagued many women for years, and bone density loss is an indicator of OP. The purpose of this study was to evaluate the relationship between the polymorphism of the rs7586085, CCDC170 and GALNT3 gene polymorphisms and the risk of OP in the Chinese Han population. Using the Agena MassArray method, we identified six candidate SNPs on chromosomes 2 and 6 in 515 patients with OP and 511 healthy controls. Genetic model analysis was performed to evaluate the significant association between variation and OP risk, and meanwhile, the multiple tests were corrected by false discovery rate (FDR). Haploview 4.2 was used for haplotype analysis. In stratified analysis of BMI ˃ 24, rs7586085, rs6726821, rs6710518, rs1346004, and rs1038304 were associated with the risk of OP based on the results of genetic models among females even after the correction of FDR (qd < 0.05). In people at age ≤ 60 years, rs1038304 was associated with an increased risk of OP under genetic models after the correction of FDR (qd < 0.05). Our study reported that GALNT3 and CCDC170 gene polymorphisms and rs7586085 are the effective risk factors for OP in the Chinese Han population.

Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia.

BACKGROUND: To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19). METHODS: In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the volume of interest (VOI), and radiomic features were extracted. The Support Vector Machine (SVM) model was built on the combination of 4 groups of features, including radiomic features, traditional radiological features, quantifying features, and clinical features. By repeating cross-validation procedure, the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS: For the SVM model built on the combination of 4 groups of features (integrated model), the per-exam AUC was 0.925 (95% CI 0.856 to 0.994) for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816 (95% CI 0.651 to 0.917) and 0.923 (95% CI 0.621 to 0.996), respectively. As for the SVM models built on radiomic features, radiological features, quantifying features, and clinical features, individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607, and 0.739, respectively, significantly lower than the integrated model, except for the radiomic model. CONCLUSION: The machine learning-based CT radiomics models may accurately classify COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.