Background Pneumoconiosis remains a critical occupational disease in China. As a major industrial province, Hebei historically faced severe challenges regarding this condition. The 1986 national epidemiological survey ranked Hebei sixth in reported pneumoconiosis cases nationwide. Objective To analyze 76 years of pneumoconiosis data (1949–2024) in Hebei Province to provide evidence-based support for prevention and control strategies. Methods Occupational pneumoconiosis cases diagnosed in Hebei from 1949 to 2024 were included. Five-year intervals were used to analyze incidence composition, dust exposure duration, age at first diagnosis, and age at death. A hybrid model, integrating autoregressive integrated moving average (ARIMA) with generalized regression neural network (GRNN), was employed to predict case numbers for the 2025−2030 period. Results A total of 36107 cases of pneumoconiosis were recorded (2.10% female) in Hebei from 1949 to 2024. Stages I, II, and III accounted for 77.35%, 16.45%, and 6.20%, respectively. Silicosis (55.60%), coal workers’ pneumoconiosis (27.24%), and pottery workers’ pneumoconiosis (8.88%) were the predominant types. Observed trends included a shortened dust exposure duration [silicosis: (12.78±7.063) years in 2020–2024] and an increasing age at first diagnosis [(54.01±7.499) years in 2020–2024]. The case-fatality rate was 14.84%, with a rising age at death [(82.26±9.632) years in 2015–2019]. Three incidence peaks (1963, 1984, and 2014) correlated with national policies and industrialization phases. The number of pneumoconiosis cases showed a decreasing trend from 2015 to 2024. Chengde and Tangshan were identified as current high-risk areas. The ARIMA-GRNN model projected annual cases between 416 and 429 during 2025–2030. Conclusion In the past decade，the number of pneumoconiosis cases in Hebei Province had shown a decreasing trend, it is projected to plateau with no significant decline over the next five years. Furthermore, challenges such as the proportion of silicosis remaining high and shortened dust exposure duration remain severe. It is recommended to focus on key regions like Chengde (ore mining) and Tangshan (ceramics industry). A comprehensive strategy integrating stricter regulation, technological controls, model innovation, and targeted interventions should be implemented to achieve a further substantial reduction in pneumoconiosis incidence.

Background Pneumoconiosis remains a critical occupational disease in China. As a major industrial province, Hebei historically faced severe challenges regarding this condition. The 1986 national epidemiological survey ranked Hebei sixth in reported pneumoconiosis cases nationwide. Objective To analyze 76 years of pneumoconiosis data (1949–2024) in Hebei Province to provide evidence-based support for prevention and control strategies. Methods Occupational pneumoconiosis cases diagnosed in Hebei from 1949 to 2024 were included. Five-year intervals were used to analyze incidence composition, dust exposure duration, age at first diagnosis, and age at death. A hybrid model, integrating autoregressive integrated moving average (ARIMA) with generalized regression neural network (GRNN), was employed to predict case numbers for the 2025−2030 period. Results A total of 36107 cases of pneumoconiosis were recorded (2.10% female) in Hebei from 1949 to 2024. Stages I, II, and III accounted for 77.35%, 16.45%, and 6.20%, respectively. Silicosis (55.60%), coal workers’ pneumoconiosis (27.24%), and pottery workers’ pneumoconiosis (8.88%) were the predominant types. Observed trends included a shortened dust exposure duration [silicosis: (12.78±7.063) years in 2020–2024] and an increasing age at first diagnosis [(54.01±7.499) years in 2020–2024]. The case-fatality rate was 14.84%, with a rising age at death [(82.26±9.632) years in 2015–2019]. Three incidence peaks (1963, 1984, and 2014) correlated with national policies and industrialization phases. The number of pneumoconiosis cases showed a decreasing trend from 2015 to 2024. Chengde and Tangshan were identified as current high-risk areas. The ARIMA-GRNN model projected annual cases between 416 and 429 during 2025–2030. Conclusion In the past decade，the number of pneumoconiosis cases in Hebei Province had shown a decreasing trend, it is projected to plateau with no significant decline over the next five years. Furthermore, challenges such as the proportion of silicosis remaining high and shortened dust exposure duration remain severe. It is recommended to focus on key regions like Chengde (ore mining) and Tangshan (ceramics industry). A comprehensive strategy integrating stricter regulation, technological controls, model innovation, and targeted interventions should be implemented to achieve a further substantial reduction in pneumoconiosis incidence.

Implementation Science is an interdisciplinary field dedicated to systematically studying how to effectively translate evidence-based research findings into practical application and implementation. In the health-related context, it focuses on enhancing the efficiency and quality of healthcare services, thereby facilitating the transition from scientific evidence to real-world practice. This article elaborates on Theories, Models, and Frameworks (TMF) within health-related Implementation Science, clarifying their basic concepts and classifications, and discussing their roles in guiding implementation processes. Furthermore, it reviews and prospects current research from three aspects: the constituent elements of TMF, their practical applications, and future directions. Five representative frameworks are emphasized, including the Consolidated Framework for Implementation Research (CFIR), the Practical Robust Implementation and Sustainability Model (PRISM), the Exploration, Preparation, Implementation, Sustainment (EPIS)framework, the Behavior Change Wheel (BCW), and the Normalization Process Theory (NPT). Additionally, resources such as the Dissemination & Implementation Models Webtool and the T-CaST tool are introduced to assist researchers in selecting appropriate TMFs based on project-specific needs.

Objective:To explore the influencing factors for pathological upgrading in prostate cancer patients with a Gleason score of 3 + 3 undergoing targeted biopsy，and to establish a nomogram prediction model.Methods:A retrospective analysis was conducted on 191 patients with localized prostate cancer diagnosed with a Gleason score of 3 + 3 through targeted biopsies at the First Affiliated Hospital of Nanjing Medical University from January 2020 to June 2024. The age of the patients was 67（61，73）years，with prostate-specific antigen（PSA）level of 7.44（5.53，10.19）ng/ml，prostate volume of 35.64（26.59，48.97）ml，and PSA density（PSAD）of 0.20（0.14，0.31）ng/ml 2. Among them，61 cases（31.94%）had a Prostate Imaging Reporting and Data System（PI-RADS）score of 3，104 cases（54.45%）had a score of 4，and 26 cases（13.61%）had a score of 5. The diameter of the main lesion was 10.75（7.86，14.00）mm. The lesions were located in the peripheral zone in 78 cases（40.84%），the transition zone in 99 cases（51.83%），and the anterior fibromuscular stroma in 14 cases（7.33%）. The lesions were found at the apex in 56 cases（29.32%），in the body in 120 cases（62.83%），and at the base in 15 cases（7.85%）. MRI revealed only one lesion with a PI-RADS score ≥ 3 in 131 cases，two suspected lesions in 43 cases，three suspected lesions in 12 cases，and four suspected lesions in 5 cases. Systematic biopsy was positive in 121 cases（63.4%）and negative in 70 cases（36.6%）. The lesions were confined to the left lobe in 63 cases（32.98%），right lobe in 68 cases（35.60%），and involved both lobes in 60 cases（31.41%）. The interval between biopsy and surgery was 9.0（7.0，14.0）days. Univariate analyses were performed using Mann-Whitney U tests or χ2 tests，and multivariate logistic regression was used to identify independent predictors of pathological upgrading. A nomogram model was constructed based on these independent predictors. The model’s discriminative ability was assessed using the area under the receiver operating characteristic（ROC）curve（AUC），and internal validation of the model’s consistency was conducted using the bootstrap resampling method. Decision curve analysis（DCA）was performed to assess clinical utility. Results:Among the 191 cases，60（31.4%）had no pathological upgrading after surgery，while 131（68.6%）showed upgrading. Univariate analysis showed that the maximum diameter of the main lesion［9.0（6.0，13.2）mm vs. 11.0（8.4，14.0）mm］，number of suspicious lesions on MRI［1.0（1.0，1.0）vs. 1.0（1.0，2.0）］，number of positive systematic biopsy cores［1.0（0，2.0）vs. 1.0（0，3.0）］，percentage of positive systematic biopsy cores［0.08（0，0.17）vs. 0.12（0，0.25）］，number of positive targeted biopsy cores［2.0（1.0，3.0）vs. 3.0（1.0，4.0）］，percentage of positive targeted biopsy cores［0.37（0.24，0.75）vs. 0.50（0.38，0.85）］，level of the index lesion，location of the index lesion，and PI-RADS score were associated with pathological upgrading（ P < 0.05）. Multivariate logistic regression analysis showed that PI-RADS score 4（ OR = 5.88，95% CI 2.41 - 14.35），number of suspicious lesions on MRI（ OR = 4.15，95% CI 1.88 - 9.17），location of the index lesion in the transition zone（ OR = 6.86，95% CI 2.81 - 16.73），and percentage of positive targeted biopsy cores（ OR = 4.37，95% CI 1.38 - 14.90）were independent risk factors for pathological upgrading（ P < 0.05）. The nomogram model constructed using these predictors had an AUC of 0.845. Internal validation using the Bootstrap method yielded an AUC value of 0.812，indicating high predictive accuracy of the model. The calibration curve indicated good calibration. Decision curve analysis showed that the threshold range for net benefit in the model was between 12% - 100%. Conclusions:The PI-RADS score 4，the number of lesions with PI-RADS ≥ 3，the location of the main lesion in the transition zone，and the percentage of positive needles in targeted biopsy are independent risk factors for pathological upgrading from Gleason score 3 + 3. The nomogram model constructed from these factors demonstrates good predictive performance and provides a reference for clinical decision-making.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

Objective:To explore the influencing factors for pathological upgrading in prostate cancer patients with a Gleason score of 3 + 3 undergoing targeted biopsy，and to establish a nomogram prediction model.Methods:A retrospective analysis was conducted on 191 patients with localized prostate cancer diagnosed with a Gleason score of 3 + 3 through targeted biopsies at the First Affiliated Hospital of Nanjing Medical University from January 2020 to June 2024. The age of the patients was 67（61，73）years，with prostate-specific antigen（PSA）level of 7.44（5.53，10.19）ng/ml，prostate volume of 35.64（26.59，48.97）ml，and PSA density（PSAD）of 0.20（0.14，0.31）ng/ml 2. Among them，61 cases（31.94%）had a Prostate Imaging Reporting and Data System（PI-RADS）score of 3，104 cases（54.45%）had a score of 4，and 26 cases（13.61%）had a score of 5. The diameter of the main lesion was 10.75（7.86，14.00）mm. The lesions were located in the peripheral zone in 78 cases（40.84%），the transition zone in 99 cases（51.83%），and the anterior fibromuscular stroma in 14 cases（7.33%）. The lesions were found at the apex in 56 cases（29.32%），in the body in 120 cases（62.83%），and at the base in 15 cases（7.85%）. MRI revealed only one lesion with a PI-RADS score ≥ 3 in 131 cases，two suspected lesions in 43 cases，three suspected lesions in 12 cases，and four suspected lesions in 5 cases. Systematic biopsy was positive in 121 cases（63.4%）and negative in 70 cases（36.6%）. The lesions were confined to the left lobe in 63 cases（32.98%），right lobe in 68 cases（35.60%），and involved both lobes in 60 cases（31.41%）. The interval between biopsy and surgery was 9.0（7.0，14.0）days. Univariate analyses were performed using Mann-Whitney U tests or χ2 tests，and multivariate logistic regression was used to identify independent predictors of pathological upgrading. A nomogram model was constructed based on these independent predictors. The model’s discriminative ability was assessed using the area under the receiver operating characteristic（ROC）curve（AUC），and internal validation of the model’s consistency was conducted using the bootstrap resampling method. Decision curve analysis（DCA）was performed to assess clinical utility. Results:Among the 191 cases，60（31.4%）had no pathological upgrading after surgery，while 131（68.6%）showed upgrading. Univariate analysis showed that the maximum diameter of the main lesion［9.0（6.0，13.2）mm vs. 11.0（8.4，14.0）mm］，number of suspicious lesions on MRI［1.0（1.0，1.0）vs. 1.0（1.0，2.0）］，number of positive systematic biopsy cores［1.0（0，2.0）vs. 1.0（0，3.0）］，percentage of positive systematic biopsy cores［0.08（0，0.17）vs. 0.12（0，0.25）］，number of positive targeted biopsy cores［2.0（1.0，3.0）vs. 3.0（1.0，4.0）］，percentage of positive targeted biopsy cores［0.37（0.24，0.75）vs. 0.50（0.38，0.85）］，level of the index lesion，location of the index lesion，and PI-RADS score were associated with pathological upgrading（ P < 0.05）. Multivariate logistic regression analysis showed that PI-RADS score 4（ OR = 5.88，95% CI 2.41 - 14.35），number of suspicious lesions on MRI（ OR = 4.15，95% CI 1.88 - 9.17），location of the index lesion in the transition zone（ OR = 6.86，95% CI 2.81 - 16.73），and percentage of positive targeted biopsy cores（ OR = 4.37，95% CI 1.38 - 14.90）were independent risk factors for pathological upgrading（ P < 0.05）. The nomogram model constructed using these predictors had an AUC of 0.845. Internal validation using the Bootstrap method yielded an AUC value of 0.812，indicating high predictive accuracy of the model. The calibration curve indicated good calibration. Decision curve analysis showed that the threshold range for net benefit in the model was between 12% - 100%. Conclusions:The PI-RADS score 4，the number of lesions with PI-RADS ≥ 3，the location of the main lesion in the transition zone，and the percentage of positive needles in targeted biopsy are independent risk factors for pathological upgrading from Gleason score 3 + 3. The nomogram model constructed from these factors demonstrates good predictive performance and provides a reference for clinical decision-making.

Objective:To summarize the HRCT and MRI appearances of stapical footplate fistula related to inner ear malformation (SFF-Re-IEM).Methods:The HRCT and MRI materials of 48 cases (53 ears) SFF-Re-IEM were retrospectively analyzed. Among them, 25 SFF-Re-IEM ears were confirmed by surgery. Their CT and MRI findings including associated IEM type, internal auditory canal (IAC) malformation, tympanic fluid, its density and signal features, and accompanied labyrinthitis were recorded.Results:Among 48 cases (53 ears) with SFF-Re-IEM, 17 ears with incomplete partition type Ⅰ, accounting for 32.1%, 13 ears with common cavity for 24.5%, 13 ears with cochlear aplasia for 24.5%, 7 ears with cochlear dysplasia Ⅱ for 13.2%, and 3 ears with Mondini for 5.7%,were found respectively. 94.3% of them were associated with a defect or dysplasia in the found of the IAC. They were divided into 4 types according to the intact of the stapical footplate and accompanied CSF otorrhea: 22 ears were diagnosed as the stapical footplate leaking, of them, 2 ears might come from the stapical footplate bony defect, 6 ears were from the stapical footplate hernia. 1 ear belonged to the peristapical footplate leaking. 30 ears with the isolated the stapical footplate hernia were another found. The bony defect in 2 ears with the stapical footplate bony defect were not presented on CT and MRI.The focal bony defect of the affected stapical footplate of 36 ears with the stapical footplate hernia were demonstrated, which presented the hemispherical protruding into the tympana, the soft-tissue density on CT, and CSF-like signal on the MR heaved-T2WI images. Among 22 ears with the stapical footplate leaking, their imaging appearances varied from the different amount of the leaking CSF. Besides the focal bony defects of the affected stapical footplates, there were much more CSF-like density or signal in the ipsilateral tympanic cavity in 17 affected ears connecting with the vestibule through the defect area. In the CSF leaking ears with less CSF leaking in 5 ears, the CSF-like cysts like SFH were shown on the stapical footplate defect area, but their outer edges were irregular, and the CSF-like signal scattering in the tympanic cavity did not connect with the protruding cysts at the stapical area.Conclusion:The variable appearances of the SFF-Re-IEM ears based on the different subtypes are its characteristic HRCT and MRI appearances. This is helpful for the SFF-Re-IEM diagnosing to grasp its imaging features.

A retrospective analysis was conducted on 5 516 patients diagnosed with prostate cancer(PCa) at our hospital. Among these, 52 patients aged ≤ 50 years were defined as the early-onset group.For the control group, 228 patients aged >50 years were randomly selected at a ratio of 1∶4.4. The early-onset group predominantly presented with elevated PSA levels at diagnosis and had a lower positive rate of digital rectal examination. There were no significant differences in clinical and pathological characteristics between the early-onset group and the control group. Young PCa patients in the low to intermediate risk categories had similar survival prognosis to older patients. However, young patients with high-risk prostate cancer had 5-year progression-free survival rate of 38.4% compared to 55.6% for older patients, and 5-year cancer-specific survival rate of 70.1% compared to 84.1% for older patients, indicating that high-risk young patients exhibited poorer oncological outcomes.

A retrospective analysis was conducted on 5 516 patients diagnosed with prostate cancer(PCa) at our hospital. Among these, 52 patients aged ≤ 50 years were defined as the early-onset group.For the control group, 228 patients aged >50 years were randomly selected at a ratio of 1∶4.4. The early-onset group predominantly presented with elevated PSA levels at diagnosis and had a lower positive rate of digital rectal examination. There were no significant differences in clinical and pathological characteristics between the early-onset group and the control group. Young PCa patients in the low to intermediate risk categories had similar survival prognosis to older patients. However, young patients with high-risk prostate cancer had 5-year progression-free survival rate of 38.4% compared to 55.6% for older patients, and 5-year cancer-specific survival rate of 70.1% compared to 84.1% for older patients, indicating that high-risk young patients exhibited poorer oncological outcomes.