Objective:To explore the value of spectral CT parameters combined with dynamic contrast enhanced magnetic resonance imaging（DCE-MRI） parameters in the short-term efficacy of concurrent chemoradiotherapy for nasopharyngeal carcinoma. Methods: A total of 110 cases with nasopharyngeal carcinoma Ⅲ-Ⅳ staging who received synchronous radiotherapy and chemotherapy at our Hospital from October 2022 to October 2024 were regarded as the study subjects. Complying with the evaluation results after radiotherapy and chemotherapy, they were divided into a complete remission（CR） group of 53 cases and a non CR group of 57 cases. All patients underwent DCE-MRI and energy dispersive CT scans to obtain parameters, such as iodine concentration（IC）, volume transfer constant（Ktrans）, slope of spectral HU curve（λHU）, rate constant（Kep）, and normalized iodine concentration（NIC）. Logistic regression analysis was used to screen for influencing factors. ROC curve was used to analyze the evaluation value of various parameters. In addition, Z-test was used to compare area under the curve（AUC）. Results:The proportion of retropharyngeal lymph node metastasis and λHUvalue in the non CR group were higher than those in the CR group, while Ktrans, Kep, IC value, and NIC value were lower than those in the CR group（P<0.05）. Retropharyngeal lymph node metastasis, Ktrans, Kep, IC value, λHUvalue, and NIC value were all influencing factors（P<0.05）. The AUC of individual prediction of Ktrans, Kep, IC value, λHUvalue, and NIC value was 0.817, 0.800, 0.785, 0.783, and 0.835, respectively. The AUC of the combination of DCE-MRI parameters, the combination of spectral CT parameters, and the combination of the five parameters were 0.874, 0.900, and 0.980, respectively, the AUC of the combination of the five parameters was significantly higher than the AUC of each indicator alone, the AUC of the combination of DCE-MRI parameters, and the AUC of the combination of spectral CT parameters（P<0.05）. Conclusion:The DCE-MRI, and spectral CT parameters （Ktrans, Kep, IC value, λHUvalue, and NIC value）can be used to evaluate concurrent radiotherapy and chemotherapy short-term efficacy for nasopharyngeal carcinoma. And the combination of various parameters can greatly improve the predictive value of efficacy, which has important clinical application value.
Humans ; Chemoradiotherapy ; Nasopharyngeal Neoplasms/diagnostic imaging* ; Magnetic Resonance Imaging ; Nasopharyngeal Carcinoma ; Tomography, X-Ray Computed ; Male ; Female ; Contrast Media ; Middle Aged ; Adult ; Lymphatic Metastasis ; Dynamic Contrast Enhanced Magnetic Resonance Imaging

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To investigate the incidence and treatment of gastric cancer in 16 medical centers in Tianjin from 2020 to 2021.Methods:The retrospective and descriptive study was conducted. The clinical data of 3 122 gastric cancer patients who underwent surgery in 16 medical centers, including Tianjin Medical University Cancer Institute & Hospital, et al, in Tianjin from 2020 to 2021 were collected. There were 2 112 males and 1 010 females, aged (64±11)years. Observation indicators: (1) general data of patients; (2) treatment situations; (3) postoperative complications. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M(range). Count data were descri-bed as absolute numbers or percentages, and comparison between groups was conducted by the chi-square test. Results:(1) General data of patients. From 2020 to 2021, a total of 3 122 gastric cancer patients received surgeries in 16 medical centers in Tianjin, including 2 112 males and 1 010 females. There were 1 443 cases in 2020, including 976 males and 467 females, aged (63±11) years. There were 1 679 cases in 2021, including 1 136 males and 543 females, aged (65±11) years. Of the 3 122 pati-ents, cases in stage Ⅰ, Ⅱ, Ⅲ, Ⅳ were 696, 667, 1 466, 293, accounting for 22.293%(696/3 122), 21.365%(667/3 122), 46.957%(1 466/3 122), 9.385%(293/3 122), respectively. Cases with early gastric cancer, locally advanced gastric cancer, advanced gastric cancer account for 17.265%(539/3 122), 73.350%(2 290/3 122), 9.385%(293/3 122). There were 2 829 patients without distant metastasis and 293 patients with distant metastasis. For the 2 829 patients without distant metas-tasis, cases in stage T1, T2, T3, T4a, T4b accounted for 19.053%(539/2 829), 12.089%(342/2 829), 20.148%(570/2 829), 41.499%(1 174/2 829), 7.211%(204/2 829)respectively, cases in stage N0, N1, N2, N3 account for 37.328%(1 056/2 829), 16.331%(462/2 829), 15.836%(448/2 829), 30.505%(863/2 829). For the 293 advanced gastric cancer patients with distant metastasis, 190 cases had peri-toneal metastasis, 47 cases had lymph node metastasis, 27 cases had ovarian metastasis, 37 cases had liver metastasis, 14 cases had other metastasis (some patients had ≥2 distant metastases). (2) Treatment situations. ① For the 539 with early gastric cancer, cases undergoing endoscopic submu-cosal dissection, laparoscopic surgery, open surgery were 22, 150, 86 in 2020, versus 19, 212, 50 in 2021, showing a significant difference between them ( χ2=19.42, P<0.05). For the 498 patients with early gastric cancer who underwent laparoscopic or open surgery, cases undergoing open surgery including total gastrectomy, distal gastrectomy, proximal gastrectomy were 25, 81, 30, and cases undergoing laparoscopic surgery including total gastrectomy, distal gastrectomy, proximal gastrec-tomy were 18, 309, 35, respectively, showing a significant difference between them ( χ2=40.62, P<0.05). For the 2 290 patients with locally advanced gastric cancer, cases undergoing open surgery and laparoscopic surgery were 446 and 617 in 2020, versus 410 and 817 in 2021, showing a significant difference between them ( χ2=17.75, P<0.05). For the 2 290 patients with locally advanced gastric cancer, cases undergoing open surgery including total gastrectomy, distal gastrectomy, proxi-mal gastrectomy were 336, 377, 143, and cases undergoing laparoscopic surgery including total gastrectomy, distal gastrectomy, proximal gastrectomy were 377, 920, 137, respectively, showing a significant difference between them ( χ2=89.64, P<0.05). Of the 293 patients with advanced gastric cancer, 175 cases underwent surgeries due to hemorrhage, stenosis, perforation, 76 cases under-went surgery after chemotherapy, 42 cases underwent surgery directly. ② For 756 cases of 3 122 pati-ents undergoing total gastrectomy, 357 and 4 cases received open digestive tract reconstruction including Roux-en-Y and other anastomosis, versus 380 and 15 cases with laparoscopic digestive tract reconstruction including Roux-en-Y and other anastomosis, showing a significant difference between them ( χ2=5.57, P<0.05). For 1 687 cases undergoing distal gastrectomy, 84, 160, 158, 55 cases received open digestive tract reconstruction including Billroth Ⅰ anastomosis, Billroth Ⅱ + Braun anastomosis, Roux-en-Y anastomosis, uncut Roux-en-Y anastomosis, versus 154, 489, 417, 170 cases with laparoscopic digestive tract reconstruction including Billroth Ⅰ anastomosis, Billroth Ⅱ + Braun anastomosis, Roux-en-Y anastomosis, uncut Roux-en-Y anastomosis, showing a significant difference between them ( χ2=10.90, P<0.05) . Of the 539 patients with early gastric cancer, 65 cases had lymph node metastasis, in which 18 of 306 stage T1a cases had lymph node metastasis and 47 of 233 stage T1b cases had lymph node metastasis. The number of detected lymph nodes for the 2 290 patients with advanced gastric cancer was 31±15, including ≥16 for 2 059 cases and ≥30 for 1 276 cases. Of the 3 122 patients, cases with neoadjuvant therapy, complete response and incomplete response was 128, 13 and 115 in 2020, versus 250, 49 and 201 in 2021, showing a significant difference between them ( χ2=5.51, P<0.05). (3) Postoperative complications. Of the 3 122 patients, 746 cases had postoperative complications, with an incidence of 23.895%(746/3 122). There were 62 patients with grade 3 or more complications. Reoperation was conducted in 34 patients. There were 14 cases of postoperative death. The duration of postoperative hospital stay and hospital expense were (11±5)days and (98 114±46 598)yuan for the 3 122 patients, (26±14)days and (122 066±68 317)yuan for cases with complications, (40±21)days and (196 926±12 747)yuan for cases with grade 3 or more complications. Conclusion:Compared with 2020, cases undergoing laparoscopic surgery and distal gastrectomy for gastric cancer in Tianjin increases in 2021, and the digestive tract reconstruction also differs. The number of patients with neoadjuvant chemotherapy and complete response rate for advanced gastric cancer increases.

Objective:To identify the influencing factors for community elderly health services provided by general practitioners (GPs) using the social ecological theory, for reference in improving their participation and satisfaction.Methods:According to the social ecological theory, an ecological model for GPs to carry out community elderly health services was constructed from four levels: public policy ecology, community health service ecology, interpersonal relationship ecology, and individual characteristics ecology of general practitioners. A survey questionnaire was designed with six latent variables: public health policy support, public health service and basic medical service supply, doctor-patient relationship, individual participation and individual satisfaction. The questionnaire was distributed to 220 GPs from 11 primary healthcare institutions in Jiangsu province, China, who were randomly selected between October and November 2021. Exploratory and confirmatory analyses of the model were conducted using AMOS 25.0.Results:A total of 207 valid questionnaires were collected, and all the KMO values of the six latent variables were greater than 0.7, while the composite reliability values and average variance extracted values greater than 0.7 and 0.5, respectively. Both the reliability and validity of the data met the analysis requirements. Exploratory analysis revealed that public health policy support had a direct positive effect on both public health service and basic medical service supply (both effect sizes being 0.37). Public health service had a direct positive effect on doctor-patient relationship, individual participation and individual satisfaction (effect sizes being 0.52, 0.22, and 0.31, respectively). The direct effect of basic medical service supply on doctor-patient relationship was not significant (effect size being 0.03), but it had a direct positive effect on public health service (effect size being 0.46). Doctor-patient relationship had a direct positive effect on individual participation (effect size being 0.51), but its direct effect on individual satisfaction was not significant (effect size being 0.06). Individual participation had a direct positive effect on individual satisfaction (effect size being 0.52). Conclusions:By optimizing the public policy ecosystem, community health service ecosystem, and interpersonal relationship ecosystem, the participation and satisfaction of general practitioners can be systematically improved.

Objective:To investigate the application value of magnetic resonance imaging (MRI) intravoxel incoherent motion (IVIM)-diffusion-weighted imaging (DWI) metrics and texture analysis in the differential diagnosis and staging of nasopharyngeal carcinoma.Methods:The clinical data of 125 nasopharyngeal carcinoma patients (the research group) in Tangshan People's Hospital from October 2019 to October 2021 and 76 patients with nasopharyngeal hyperplasia during the same period (the control group) were retrospectively analyzed. All patients underwent MRI T2WI and IVIM-DWI sequence scanning, and then the plain T2WI images, DWI, and IVIM-DWI quantitative parameter pseudo-color maps including pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) were obtained. The texture analysis metrics like apparent diffusion coefficient (ADC), D, D* and f were recorded. IVIM-DWI and texture analysis metrics were compared among patients in both groups and patients in different clinical stages; and the receiver operating characteristic (ROC) curve was plotted to evaluate the efficacy of IVIM sequence parameters and texture analysis metrics in the differential diagnosis and staging of nasopharyngeal carcinoma.Results:Compared with the control group, a marked reduction in D value [(0.80±0.13)×10 -3 mm 2/s vs. (1.19±0.27)×10 -3 mm 2/s], f value [(11.3±2.2)% vs. (15.6±3.3)%], mean ADC value [(0.92±0.17)×10 -3 mm 2/s vs. (1.16±0.19)×10 -3 mm 2/s] and variance (2 189±862 vs. 3 563±925) (all P < 0.05); a notable increase in skewness (0.50±0.17 vs. 0.31±0.12), kurtosis (0.56±0.13 vs. -0.21±0.06) and entropy (10.5±2.3 vs. 7.1±2.1) (all P < 0.05). The area under the curve (AUC) of IVIM sequence parameters and texture analysis metrics in the differential diagnosis of nasopharyngeal carcinoma was 0.763 and 0.803, respectively; the AUC, sensitivity and specificity of the combination of IVIM sequence parameters and texture analysis metrics for the differential diagnosis of nasopharyngeal carcinoma was 0.868, 89.6% and 86.8%, respectively. Compared with patients in stage Ⅰ-Ⅱ nasopharyngeal carcinoma, patients in stage Ⅲ-Ⅳ reported the lower D value [(0.75±0.13)×10 -3 mm 2/s vs. (0.89±0.16)×10 -3 mm 2/s], f value [(10.8±2.8)% vs. (12.1±3.0)%] (all P < 0.05), and the lower mean ADC value [(0.90±0.14)×10 -3 mm 2/s vs. (0.96±0.16)×10 -3 mm 2/s], and variance (2 063±831 vs. 2 431±846) (all P < 0.05), skewness (0.56±0.15 vs. 0.39±0.16), kurtosis (0.62±0.15 vs. 0.44±0.13) and entropy (11.0±2.1 vs. 9.1±2.4) (all P < 0.05). The AUC of IVIM sequence parameters and texture analysis metrics in differentiating nasopharyngeal carcinoma with different stages was 0.863 and 0.796, respectively; the AUC, sensitivity and specificity of the combination of IVIM sequence parameters and texture analysis metrics in differentiating nasopharyngeal carcinoma with different stages was 0.894, 85.4% and 90.7%, respectively. Conclusions:MRI texture analysis and IVIM quantitative analysis are of high value in the differential diagnosis and staging of nasopharyngeal carcinoma; and the texture analysis achieves higher sensitivity and specificity in the differential diagnosis and staging of nasopharyngeal carcinoma compared with IVIM quantitative analysis; the combined application of both has the highest overall efficacy.

Objective:To study the trend of menarche age in Han and Mongolian women born from 1951 to 2005 in Mongolian region.Methods:A cross-sectional cluster sampling survey method was adopted, From 2003 to 2019, a retrospective survey was carried out in three banners/counties in Tongliao region on the female population of Han and Mongols nationalities aged 16 to 46 and conducted under standardized survey procedures and quality control standards. The basic data of menarche age of women born between 1951 and 2005 were obtained. The changes and rules were analyzed by taking 1 year, 5 years and 10 years as nodes.Results:Totally 46 and conducted under standardized survey procedures and quality control standards 928 pepole (24 450 Han and 22 478 Mongolian) were recruited, the survey response rate was 96.09% (46 928/48 836). In one-year-period analysis, the menarche age gradually decreased from 1951 to 2005. The mean menarche age of Han and Mongolian women changed from (16.22±0.52) years and (15.86±1.24) years in 1951 to (12.37±1.15) years and (12.33±0.98) years in 2005, respectively. The mean menarche age of Han and Mongolian women decreased 3.85 years and 3.54 years. The trend of the mean menarche age's change showed a significant negative correlation with the years (all P<0.000 1). In five-year-period analysis, the mean menarche age of Han and Mongolian women changed from (15.54±1.45) years and (15.53±1.48) years from 1951 to 1955 to (12.41±0.97) years and (12.47±0.96) years from 2001 to 2005, the mean menarche age decreased 3.13 years (3.41 months ahead of schedule every 5 years on average) and 3.06 years (3.34 months ahead of schedule every 5 years on average) in Han and Mongolian women respectively. In ten-year-period analysis, the mean menarche age of Han and Mongolian women changed from (15.79±0.95) years and (15.53±1.33) years from 1951 to 1960 to (12.41±0.97) years and (12.47±0.96) years from 2001 to 2005, the mean menarche age decreased 3.38 years (6.76 months ahead of schedule every 10 years on average) and 3.06 years (6.12 months ahead of schedule every 10 years on average) in Han and Mongolian women respectively. During the 15 years from 1951 to 1965, 1966 to 1970, 1971 to 1990, and 1991 to 2000, they were concentrated at the ages of 15-16, 14-15, 13-14, and 12-13, respectively. The proportion of women at 11 years, 12 years and 13 years menarche age were 26.79% (457/1 706), 73.27% (1 250/1 706), and 92.85% (1 584/1 706) during 2001—2005 in Han women, while the proportion were 23.25% (653/2 809), 62.01% (1 742/2 809), and 90.14% (2 532/2 809) in Mongolian women. Conclusion:The menarche age decreased in Han and Mongolian women from 1951 to 2005, and the ethnic groups tended to be the same. It is recommended to start adolescent education at the age of 8-9 years and pay attention to the changing pattern of early onset of menarche.

Objective:To study the trend of menarche age in Han and Mongolian women born from 1951 to 2005 in Mongolian region.Methods:A cross-sectional cluster sampling survey method was adopted, From 2003 to 2019, a retrospective survey was carried out in three banners/counties in Tongliao region on the female population of Han and Mongols nationalities aged 16 to 46 and conducted under standardized survey procedures and quality control standards. The basic data of menarche age of women born between 1951 and 2005 were obtained. The changes and rules were analyzed by taking 1 year, 5 years and 10 years as nodes.Results:Totally 46 and conducted under standardized survey procedures and quality control standards 928 pepole (24 450 Han and 22 478 Mongolian) were recruited, the survey response rate was 96.09% (46 928/48 836). In one-year-period analysis, the menarche age gradually decreased from 1951 to 2005. The mean menarche age of Han and Mongolian women changed from (16.22±0.52) years and (15.86±1.24) years in 1951 to (12.37±1.15) years and (12.33±0.98) years in 2005, respectively. The mean menarche age of Han and Mongolian women decreased 3.85 years and 3.54 years. The trend of the mean menarche age's change showed a significant negative correlation with the years (all P<0.000 1). In five-year-period analysis, the mean menarche age of Han and Mongolian women changed from (15.54±1.45) years and (15.53±1.48) years from 1951 to 1955 to (12.41±0.97) years and (12.47±0.96) years from 2001 to 2005, the mean menarche age decreased 3.13 years (3.41 months ahead of schedule every 5 years on average) and 3.06 years (3.34 months ahead of schedule every 5 years on average) in Han and Mongolian women respectively. In ten-year-period analysis, the mean menarche age of Han and Mongolian women changed from (15.79±0.95) years and (15.53±1.33) years from 1951 to 1960 to (12.41±0.97) years and (12.47±0.96) years from 2001 to 2005, the mean menarche age decreased 3.38 years (6.76 months ahead of schedule every 10 years on average) and 3.06 years (6.12 months ahead of schedule every 10 years on average) in Han and Mongolian women respectively. During the 15 years from 1951 to 1965, 1966 to 1970, 1971 to 1990, and 1991 to 2000, they were concentrated at the ages of 15-16, 14-15, 13-14, and 12-13, respectively. The proportion of women at 11 years, 12 years and 13 years menarche age were 26.79% (457/1 706), 73.27% (1 250/1 706), and 92.85% (1 584/1 706) during 2001—2005 in Han women, while the proportion were 23.25% (653/2 809), 62.01% (1 742/2 809), and 90.14% (2 532/2 809) in Mongolian women. Conclusion:The menarche age decreased in Han and Mongolian women from 1951 to 2005, and the ethnic groups tended to be the same. It is recommended to start adolescent education at the age of 8-9 years and pay attention to the changing pattern of early onset of menarche.