MRI quantitative assessment of the effects of low-carbohydrate therapy on Hashimoto's thyroiditis.

Objective: Hashimoto's thyroiditis is an inflammatory disease, and research suggests that a low-carbohydrate diet may have potential anti-inflammatory effects. This study aims to utilize Dixon-T2-weighted imaging (WI) sequence for a semi-quantitative assessment of the impact of a low-carbohydrate diet on the degree of thyroid inflammation in patients with Hashimoto's thyroiditis. Methods: Forty patients with Hashimoto's thyroiditis were recruited for this study and randomly divided into two groups: one with a normal diet and the other with a low-carbohydrate diet. Antibodies against thyroid peroxidase (TPOAb) and thyroglobulin (TgAb) were measured for all participants. Additionally, thyroid water content was semi-quantitatively measured using Dixon-T2WI. The same tests and measurements were repeated for all participants after 6 months. Results: After 6 months of a low-carbohydrate diet, patients with Hashimoto's thyroiditis showed a significant reduction in thyroid water content (94.84 ± 1.57% vs 93.07 ± 2.05%, P < 0.05). Concurrently, a decrease was observed in levels of TPOAb and TgAb (TPOAb: 211.30 (92.63-614.62) vs 89.45 (15.9-215.67); TgAb: 17.05 (1.47-81.64) vs 4.1 (0.51-19.42), P < 0.05). In contrast, there were no significant differences in thyroid water content or TPOAb and TgAb levels for patients with Hashimoto's thyroiditis following a normal diet after 6 months (P < 0.05). Conclusion: Dixon-T2WI can quantitatively assess the degree of thyroid inflammation in patients with Hashimoto's thyroiditis. Following a low-carbohydrate diet intervention, there is a significant reduction in thyroid water content and a decrease in levels of TPOAb and TgAb. These results suggest that a low-carbohydrate diet may help alleviate inflammation in patients with Hashimoto's thyroiditis.

A diagnostic scoring model of ENKTCL in the nose-Waldeyer's ring based on logistic regression: Differential diagnosis from DLBCL.

Objective: To establish a logistic regression model based on CT and MRI imaging features and Epstein-Barr (EB) virus nucleic acid to develop a diagnostic score model to differentiate extranodal NK/T nasal type (ENKTCL) from diffuse large B cell lymphoma (DLBCL). Methods: This study population was obtained from two independent hospitals. A total of 89 patients with ENKTCL (n = 36) or DLBCL (n = 53) from January 2013 to May 2021 were analyzed retrospectively as the training cohort, and 61 patients (ENKTCL=27; DLBCL=34) from Jun 2021 to Dec 2022 were enrolled as the validation cohort. All patients underwent CT/MR enhanced examination and EB virus nucleic acid test within 2 weeks before surgery. Clinical features, imaging features and EB virus nucleic acid results were analyzed. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors of ENKTCL and establish a predictive model. Independent predictors were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the predictive model and score model. Results: We searched for significant clinical characteristics, imaging characteristics and EB virus nucleic acid and constructed the scoring system via multivariate logistic regression and converted regression coefficients to weighted scores. The independent predictors for ENKTCL diagnosis in multivariate logistic regression analysis, including site of disease (nose), edge of lesion (blurred), T2WI (high signal), gyrus like changes, EB virus nucleic acid (positive), and the weighted score of regression coefficient was 2, 3, 4, 3, 4 points. The ROC curves, AUCs and calibration tests were carried out to evaluate the scoring models in both the training cohort and the validation cohort. The AUC of the scoring model in the training cohort were 0.925 (95% CI, 0.906-0.990) and the cutoff point was 5 points. In the validation cohort, the AUC was 0.959 (95% CI, 0.915-1.000) and the cutoff value was 6 points. Four score ranges were as follows: 0-6 points for very low probability of ENKTCL, 7-9 points for low probability; 10-11 points for middle probability; 12-16 points for very high probability. Conclusion: The diagnostic score model of ENKTCL based on Logistic regression model which combined with imaging features and EB virus nucleic acid. The scoring system was convenient, practical and could significantly improve the diagnostic accuracy of ENKTCL and the differential diagnosis of ENKTCL from DLBCL.

MRI Extraprostatic Extension Grade: Accuracy and Clinical Incremental Value in the Assessment of Extraprostatic Cancer.

Objective: The purpose was to compare the accuracy of extraprostatic extension (EPE) grade on MRI predicting EPE with Partin tables, Memorial Sloan Kettering Cancer Center nomogram (MSKCCn), and combined models and to analyze the clinical incremental value of EPE grade. Materials and Methods: 105 prostate cancer patients confirmed by pathology after radical prostatectomy in our hospital from 2017 to 2021 were selected. The clinical stage, PSA, Gleason score, number of positive biopsy cores, and percentage of positive biopsy cores were recorded. Evaluate EPE grade according to EPE grade criteria, and calculate the probability of predicting EPE with Partin tables and MSKCCn. EPE grade is combined with Partin tables and MSKCCn to construct EPE grade+Partin tables and EPE grade+MSKCCn models. Calculate the area under the curve (AUC), sensitivity, and specificity of EPE grade, Partin tables, MSKCCn, EPE grade+Partin tables, and EPE grade+MSKCCn and compare their diagnostic efficacy. The clinical decision curve was used to analyze the clinical net income of each prediction scheme. Results: The AUC of EPE grade was 0.79, Partin tables was 0.50, MSKCCn was 0.78, the EPE grade+Partin table model was 0.79, and the EPE grade+MSKCCn model was 0.83. After EPE grade was combined with Partin tables and MSKCCn, the diagnostic efficiency of clinical model was significantly improved (P < 0.05). There was no significant difference in the diagnostic efficacy of the combined model compared with the single EPE grade (P > 0.05). The calibration curve of the combined model shows that it has a good calibration degree for EPE. In the analysis of the decision curve, the net income of the EPE grade is higher than that of Partin tables and MSKCCn and is equal to the EPE grade+Partin tables and is slightly lower than that of EPE grade+MSKCCn. The clinical net income of the combined model is obviously higher than that of individual clinical models. Conclusion: The accuracy of EPE classification in predicting prostate cancer EPE is high, and combined with the clinical model, it can significantly improve the diagnostic efficiency of the clinical model and increase the clinical benefit.

A nomogram diagnostic prediction model of pancreatic metastases of small cell lung carcinoma based on clinical characteristics, radiological features and biomarkers.

Objective: To investigate clinical characteristics, radiological features and biomarkers of pancreatic metastases of small cell lung carcinoma (PM-SCLC), and establish a convenient nomogram diagnostic predictive model to differentiate PM-SCLC from pancreatic ductal adenocarcinomas (PDAC) preoperatively. Methods: A total of 299 patients with meeting the criteria (PM-SCLC n=93; PDAC n=206) from January 2016 to March 2022 were retrospectively analyzed, including 249 patients from hospital 1 (training/internal validation cohort) and 50 patients from hospital 2 (external validation cohort). We searched for meaningful clinical characteristics, radiological features and biomarkers and determined the predictors through multivariable logistic regression analysis. Three models: clinical model, CT imaging model, and combined model, were developed for the diagnosis and prediction of PM-SCLC. Nomogram was constructed based on independent predictors. The receiver operating curve was undertaken to estimate the discrimination. Results: Six independent predictors for PM-SCLC diagnosis in multivariate logistic regression analysis, including clinical symptoms, CA199, tumor size, parenchymal atrophy, vascular involvement and enhancement type. The nomogram diagnostic predictive model based on these six independent predictors showed the best performance, achieved the AUCs of the training cohort (n = 174), internal validation cohort (n = 75) and external validation cohort (n = 50) were 0.950 (95%CI, 0.917-0.976), 0.928 (95%CI, 0.873-0.971) and 0.976 (95%CI, 0.944-1.00) respectively. The model achieved 94.50% sensitivity, 83.20% specificity, 86.80% accuracy in the training cohort and 100.00% sensitivity, 80.40% specificity, 86.70% accuracy in the internal validation cohort and 100.00% sensitivity, 88.90% specificity, 87.50% accuracy in the external validation cohort. Conclusion: We proposed a noninvasive and convenient nomogram diagnostic predictive model based on clinical characteristics, radiological features and biomarkers to preoperatively differentiate PM-SCLC from PDAC.

[FEM study on displacement, position of rotation center and stress distribution of PDL under various loading force systems].

OBJECTIVE: To investigate the displacement, the position of rotation center and the stress distribution of PDL under different loading force system (Moment/Force, M/F) by simulating clinical loading force system. METHODS: A three-dimensional finite element model of upper central incisor, which consists of 945 isoparametric elements and 1,245 nodes was developed. The displacement, the position of the rotation center and the stress distribution of PDL were analyzed under 13 types of loading force system. RESULTS: 1. Different force system led to different types of tooth movement. When M/F= -9.15:1, -10.30 - -10.50:1 and -10.90:1, it brought the result of controlled tipping movement, the bodily movement and the root movement; 2. The graph of the center rotation was a hyperbolic asymptotic line: Mx/Fy = -10.50 (horizontal axis) and L = 6.75 (vertical axis). Moreover, a little change of M/F between -9.15 and -10.90 led to apparent change of the position of rotation center. 3. The maximum strain and stress during the tipping movement were 1.47 x 10(-2) MPa and -2.81 x 10(-2) MPa, and during the bodily movement the results were 1.10 x 10(-2) MPa and -1.86 x 10(-2) MPa, while during the root movement were 0.96 x 10(-2) MPa and -2.58 x 10(-2) MPa. CONCLUSION: 1 . Different force system (M/F) leads to different types of tooth movement. 2. It is necessary to adjust the force system accurately to obtain prescient tooth movement, especially when M/F changes between -9.15:1 and -10.90:1. 3. This study suggested that the tooth movement style and the force system (M/F) should be controlled to protect the periodontal tissue.

[Study of tooth movement under different loading force system by FEM].

OBJECTIVES: To investigate different type of tooth movement under different force systems by simulating clinical loading force systems. METHODS: Develop a three-dimensional finite element model of upper central incisor, which consists of 945 isoparametric elements, 1245 nodes. Analyse the types of tooth movement under different force system with a horizontal force of 1 N being applied at the upper central incisor. RESULTS: Different force systems(M/F) lead to different types of tooth movement. This study showed the relation between them. When M/F =-9.15:1,it bringed the result of the controlled tipping movement, When M/F=-10.30 -10.50:1, it bringed the bodily movement; While M/F=-10.90:1, it lead to the root movement. CONCLUSION: This study suggested that it is necessary to adopt different loading force systems in order ot obtain determined tooth movement.