Objective:To develop and explore the clinical value of a nomogram model for the preoperative diagnosis of proliferative hepatocellular carcinoma (HCC) based on gadoxetate disodium (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI).Methods:The preoperative Gd-EOB-DTPA-enhanced MRI data and clinical pathological data of patients with pathologically confirmed proliferative (178 cases) and non-proliferative type HCC (378 cases) from September 2017 to November 2022 were retrospectively collected. The MRI features and clinicopathological features of proliferative and non-proliferative type HCC were evaluated. Multivariate logistic regression analysis was used to determine the independent predictive factors of proliferative-type HCC. The nomogram prediction model was constructed using R software. The receiver operating characteristic curve (ROC) was used to evaluate its diagnostic efficacy. The calibration curve and decision curve analysis (DCA) were drawn to evaluate the calibration performance and clinical application value of the nomogram model. The optimal threshold for distinguishing high-risk from low-risk was determined using the Youden index. The survival prognosis of proliferative and non-proliferative type HCC was analyzed and compared using the Kaplan-Meier survival curve and the log-rank test. The measurement data were analyzed using the independent sample t-test or the Mann-Whitney U test. The count data were compared using the χ2 test. Results:There were statistically significant differences in alpha-fetoprotein (AFP) levels ( χ2=17.244, P<0.001), tumor morphology ( χ2=13.669, P<0.001), intratumoral fatty degeneration ( χ2=10.495, P=0.001), abnormal enhancement of peritumoral abnormalities during arterial phase ( χ2=37.662, P<0.001), tumor capsule ( χ2=23.961, P<0.001), intratumoral necrosis ( χ2=77.184, P<0.001), intratumoral hemorrhage ( χ2=4.892, P=0.027), peritumoral hypointense in hepatobiliary phase ( χ2=47.675, P<0.001), rim arterial phase hyperenhancement ( χ2=115.976, P<0.001), intratumoral artery ( χ2=15.528, P<0.001) and intravenous tumor thrombus ( χ2=10.532, P=0.001) between proliferative and non-proliferative type HCC groups. Multivariate logistic regression analysis showed that AFP>200 μg/L ( OR=1.561, P=0.044), no intratumoral fatty degeneration ( OR=1.947, P=0.033), intratumoral necrosis ( OR=2.084, P=0.003), peritumoral hypointensity in the hepatobiliary phase ( OR=2.314, P=0.001), and annular hyperenhancement in the arterial phase ( OR=5.557, P<0.001) were independent predictors for preoperative diagnosis of proliferative-type HCC. A nomogram model for preoperative prediction of proliferative type HCC was constructed based on the independent predictors. The area under the ROC curve model for predicting proliferative-type HCC was 0.772 (95% CI: 0.735-0.807), with a sensitivity of 69.1% and a specificity of 75.4%. The calibration curve and DCA curve showed superior calibration performance and clinical applicability of the nomogram model. The Kaplan-Meier curve showed that the recurrence free survival rate after liver resection was significantly lower in patients with proliferative-type HCC than that of non-proliferative-type HCC ( P<0.001), and the high-risk group was significantly lower than the low-risk group ( P<0.001). Conclusions:The construction of a nomogram model based on Gd-EOB-DTPA-enhanced MRI features combined with AFP >200μg/L can accurately diagnose proliferative-type HCC and predict its preoperative prognosis.

Objective:To evaluate the efficacy of gadolinium ethoxybenzyl- diethy-lenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI features in the preoperative prediction of tertiary lymphoid structures (TLS) within hepatocellular carcinoma (HCC) lesions.Methods:This retrospective cross-sectional study included clinical and pathological data from 297 HCC patients treated at the Southwest Hospital, Army Medical University between June 2021 and November 2022. Based on postoperative pathology, patients were categorized into TLS-negative ( n=93) and TLS-positive ( n=204) groups. MRI features of HCC lesions using Gd-EOB-DTPA enhancement and relevant clinical data were analyzed. Intergroup comparisons of imaging features and laboratory findings were performed using independent sample t-test, Mann-Whitney U test, χ2 test, or Fisher exact test, as appropriate. The logistic regression analysis was conducted to identify independent predictors of TLS positivity. A predictive model was constructed and visualized using a nomogram. The model′s predictive performance and clinical utility were assessed using the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The area under the ROC curve (AUC) was compared using the DeLong test. Results:Significant differences were observed between the TLS-negative and TLS-positive groups in alpha-fetoprotein (AFP) levels, intratumoral hemorrhage, and peritumoral satellite nodules in the hepatobiliary phase ( P<0.05). Multivariate logistic regression identified intratumoral hemorrhage ( OR=0.123, 95% CI 0.070-0.216, P<0.001) and peritumoral satellite nodules in the hepatobiliary phase ( OR=0.236, 95% CI 0.093-0.596, P=0.002) as independent predictive factors for TLS-positivity. The imaging model based on these two features yielded an AUC of 0.764 (95% CI 0.709-0.809) for predicting TLS-positivity. When combined with AFP levels, the resulting clinical-imaging model achieved a superior AUC of 0.784 (95% CI 0.732-0.829), which was significantly higher than that of the imaging model alone ( Z=2.20, P=0.028). A nomogram was constructed based on the clinical-imaging model. The calibration curve demonstrated good predictive performance of the nomogram, and the DCA showed that the curve remained above the default line across a range of reasonable threshold probabilities, indicating that patients could derive clinical benefit. Conclusion:A nomogram model based on Gd-EOB-DTPA enhanced MRI features combined with AFP levels can effectively predict the presence of TLS in HCC.

OBJECTIVE To explore the risk evaluation of healthcare workers contracting tuberculosis in a designated three-A tuberculosis hospital in Guangxi based on the failure mode and effect analysis(FMEA)method.METHODS A designated three-A tuberculosis hospital in Guangxi was selected.Healthcare workers from Jan.1,2023 to Dec.31,2023 served as the control group and received routine management,i.e.,implementing various tuberculosis prevention and control measures according to current guidelines.Healthcare workers from Jan.1,2024 to Dec.31,2024 served as the experimental group and were managed by the FMEA method.High-risk fac-tors were screened through the FMEA method,and targeted intervention strategies were formulated to priori-tize intervention for high-risk events.The incidence rate of tuberculosis among healthcare workers and the imple-mentation rate of tuberculosis infection prevention and control measures before and after FMEA intervention were compared.RESULTS The six high-risk events screened were improper use of surgical masks by patients,inade-quate respiratory hygiene practices,improper use of medical protective masks by healthcare workers,lack of full process isolation management for tuberculosis patients,unreasonable sputum collection areas and improper venti-lation and air disinfection measure setting or maintenance.After implementing a series of tuberculosis prevention and control measures,the incidence rate of tuberculosis infection among healthcare workers decreased from 1.63％before FMEA intervention to 0.30％(P=0.021).The implementation rate of tuberculosis infection prevention and control measures increased from 69.95％to 73.61％(P=0.003).CONCLUSIONS Risk evaluation based on the FMEA method can identify weaknesses in tuberculosis infection prevention among healthcare workers in desig-nated tuberculosis hospitals.Implementing multiple measures simultaneously can effectively reduce the incidence rate of tuberculosis among healthcare workers,ensure occupational safety,improve the implementation rate of tu-berculosis infection prevention and control measures and achieve scientific and precise prevention and control.

Objective:To develop and explore the clinical value of a nomogram model for the preoperative diagnosis of proliferative hepatocellular carcinoma (HCC) based on gadoxetate disodium (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI).Methods:The preoperative Gd-EOB-DTPA-enhanced MRI data and clinical pathological data of patients with pathologically confirmed proliferative (178 cases) and non-proliferative type HCC (378 cases) from September 2017 to November 2022 were retrospectively collected. The MRI features and clinicopathological features of proliferative and non-proliferative type HCC were evaluated. Multivariate logistic regression analysis was used to determine the independent predictive factors of proliferative-type HCC. The nomogram prediction model was constructed using R software. The receiver operating characteristic curve (ROC) was used to evaluate its diagnostic efficacy. The calibration curve and decision curve analysis (DCA) were drawn to evaluate the calibration performance and clinical application value of the nomogram model. The optimal threshold for distinguishing high-risk from low-risk was determined using the Youden index. The survival prognosis of proliferative and non-proliferative type HCC was analyzed and compared using the Kaplan-Meier survival curve and the log-rank test. The measurement data were analyzed using the independent sample t-test or the Mann-Whitney U test. The count data were compared using the χ2 test. Results:There were statistically significant differences in alpha-fetoprotein (AFP) levels ( χ2=17.244, P<0.001), tumor morphology ( χ2=13.669, P<0.001), intratumoral fatty degeneration ( χ2=10.495, P=0.001), abnormal enhancement of peritumoral abnormalities during arterial phase ( χ2=37.662, P<0.001), tumor capsule ( χ2=23.961, P<0.001), intratumoral necrosis ( χ2=77.184, P<0.001), intratumoral hemorrhage ( χ2=4.892, P=0.027), peritumoral hypointense in hepatobiliary phase ( χ2=47.675, P<0.001), rim arterial phase hyperenhancement ( χ2=115.976, P<0.001), intratumoral artery ( χ2=15.528, P<0.001) and intravenous tumor thrombus ( χ2=10.532, P=0.001) between proliferative and non-proliferative type HCC groups. Multivariate logistic regression analysis showed that AFP>200 μg/L ( OR=1.561, P=0.044), no intratumoral fatty degeneration ( OR=1.947, P=0.033), intratumoral necrosis ( OR=2.084, P=0.003), peritumoral hypointensity in the hepatobiliary phase ( OR=2.314, P=0.001), and annular hyperenhancement in the arterial phase ( OR=5.557, P<0.001) were independent predictors for preoperative diagnosis of proliferative-type HCC. A nomogram model for preoperative prediction of proliferative type HCC was constructed based on the independent predictors. The area under the ROC curve model for predicting proliferative-type HCC was 0.772 (95% CI: 0.735-0.807), with a sensitivity of 69.1% and a specificity of 75.4%. The calibration curve and DCA curve showed superior calibration performance and clinical applicability of the nomogram model. The Kaplan-Meier curve showed that the recurrence free survival rate after liver resection was significantly lower in patients with proliferative-type HCC than that of non-proliferative-type HCC ( P<0.001), and the high-risk group was significantly lower than the low-risk group ( P<0.001). Conclusions:The construction of a nomogram model based on Gd-EOB-DTPA-enhanced MRI features combined with AFP >200μg/L can accurately diagnose proliferative-type HCC and predict its preoperative prognosis.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective:To evaluate the efficacy of gadolinium ethoxybenzyl- diethy-lenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI features in the preoperative prediction of tertiary lymphoid structures (TLS) within hepatocellular carcinoma (HCC) lesions.Methods:This retrospective cross-sectional study included clinical and pathological data from 297 HCC patients treated at the Southwest Hospital, Army Medical University between June 2021 and November 2022. Based on postoperative pathology, patients were categorized into TLS-negative ( n=93) and TLS-positive ( n=204) groups. MRI features of HCC lesions using Gd-EOB-DTPA enhancement and relevant clinical data were analyzed. Intergroup comparisons of imaging features and laboratory findings were performed using independent sample t-test, Mann-Whitney U test, χ2 test, or Fisher exact test, as appropriate. The logistic regression analysis was conducted to identify independent predictors of TLS positivity. A predictive model was constructed and visualized using a nomogram. The model′s predictive performance and clinical utility were assessed using the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The area under the ROC curve (AUC) was compared using the DeLong test. Results:Significant differences were observed between the TLS-negative and TLS-positive groups in alpha-fetoprotein (AFP) levels, intratumoral hemorrhage, and peritumoral satellite nodules in the hepatobiliary phase ( P<0.05). Multivariate logistic regression identified intratumoral hemorrhage ( OR=0.123, 95% CI 0.070-0.216, P<0.001) and peritumoral satellite nodules in the hepatobiliary phase ( OR=0.236, 95% CI 0.093-0.596, P=0.002) as independent predictive factors for TLS-positivity. The imaging model based on these two features yielded an AUC of 0.764 (95% CI 0.709-0.809) for predicting TLS-positivity. When combined with AFP levels, the resulting clinical-imaging model achieved a superior AUC of 0.784 (95% CI 0.732-0.829), which was significantly higher than that of the imaging model alone ( Z=2.20, P=0.028). A nomogram was constructed based on the clinical-imaging model. The calibration curve demonstrated good predictive performance of the nomogram, and the DCA showed that the curve remained above the default line across a range of reasonable threshold probabilities, indicating that patients could derive clinical benefit. Conclusion:A nomogram model based on Gd-EOB-DTPA enhanced MRI features combined with AFP levels can effectively predict the presence of TLS in HCC.

Purpose To explore the predictive value of CT-enhanced for tumor mutation burden(TMB)in hepatocellular carcinoma(HCC).Materials and Methods A total of 22 patients with pathologically confirmed HCC after undergoing radical resection in the First Affiliated Hospital,Army Medical University(Third Military Medical University)from January 2020 to January 2023 were collected,all of whom were quantified for TMB.Clinical,laboratory tests,CT imaging characteristics and follow-up of patients were recorded.Variables with P<0.2 were screened by stepwise regression analysis for independent risk factors for TMB.The area under the curve of receiver operating characteristic was used to assess the diagnostic efficacy.Results High TMB level was a risk factor for disease-free survival after HCC surgery(HR=1.115,P<0.05).According to the optimal cut-off value,TMB was classified into a high-risk group(>9.25 mutation/Mb)and low-risk group(≤9.25 mutation/Mb).Univariate analysis of intratumor ischemia or necrosis was statistically different between the high-risk and low-risk groups(P=0.005),and only intratumor ischemia or necrosis was an independent risk factor for predicting high TMB level by stepwise regression analysis(P<0.05).The area under the curve for predicting disease-free survival was 0.833(95%CI 0.615-0.956,P<0.001),with a sensitivity of 100.0%and a specificity of 66.7%.Conclusion High TMB level is associated with poor prognosis after HCC resection.Intratumor ischemia or necrosis have certain clinical value in predicting high TMB level,and are expected to provide a reference basis for personalized diagnosis and treatment of HCC patients.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Purpose To explore the predictive value of CT-enhanced for tumor mutation burden(TMB)in hepatocellular carcinoma(HCC).Materials and Methods A total of 22 patients with pathologically confirmed HCC after undergoing radical resection in the First Affiliated Hospital,Army Medical University(Third Military Medical University)from January 2020 to January 2023 were collected,all of whom were quantified for TMB.Clinical,laboratory tests,CT imaging characteristics and follow-up of patients were recorded.Variables with P<0.2 were screened by stepwise regression analysis for independent risk factors for TMB.The area under the curve of receiver operating characteristic was used to assess the diagnostic efficacy.Results High TMB level was a risk factor for disease-free survival after HCC surgery(HR=1.115,P<0.05).According to the optimal cut-off value,TMB was classified into a high-risk group(>9.25 mutation/Mb)and low-risk group(≤9.25 mutation/Mb).Univariate analysis of intratumor ischemia or necrosis was statistically different between the high-risk and low-risk groups(P=0.005),and only intratumor ischemia or necrosis was an independent risk factor for predicting high TMB level by stepwise regression analysis(P<0.05).The area under the curve for predicting disease-free survival was 0.833(95%CI 0.615-0.956,P<0.001),with a sensitivity of 100.0%and a specificity of 66.7%.Conclusion High TMB level is associated with poor prognosis after HCC resection.Intratumor ischemia or necrosis have certain clinical value in predicting high TMB level,and are expected to provide a reference basis for personalized diagnosis and treatment of HCC patients.

OBJECTIVE To explore the risk evaluation of healthcare workers contracting tuberculosis in a designated three-A tuberculosis hospital in Guangxi based on the failure mode and effect analysis(FMEA)method.METHODS A designated three-A tuberculosis hospital in Guangxi was selected.Healthcare workers from Jan.1,2023 to Dec.31,2023 served as the control group and received routine management,i.e.,implementing various tuberculosis prevention and control measures according to current guidelines.Healthcare workers from Jan.1,2024 to Dec.31,2024 served as the experimental group and were managed by the FMEA method.High-risk fac-tors were screened through the FMEA method,and targeted intervention strategies were formulated to priori-tize intervention for high-risk events.The incidence rate of tuberculosis among healthcare workers and the imple-mentation rate of tuberculosis infection prevention and control measures before and after FMEA intervention were compared.RESULTS The six high-risk events screened were improper use of surgical masks by patients,inade-quate respiratory hygiene practices,improper use of medical protective masks by healthcare workers,lack of full process isolation management for tuberculosis patients,unreasonable sputum collection areas and improper venti-lation and air disinfection measure setting or maintenance.After implementing a series of tuberculosis prevention and control measures,the incidence rate of tuberculosis infection among healthcare workers decreased from 1.63％before FMEA intervention to 0.30％(P=0.021).The implementation rate of tuberculosis infection prevention and control measures increased from 69.95％to 73.61％(P=0.003).CONCLUSIONS Risk evaluation based on the FMEA method can identify weaknesses in tuberculosis infection prevention among healthcare workers in desig-nated tuberculosis hospitals.Implementing multiple measures simultaneously can effectively reduce the incidence rate of tuberculosis among healthcare workers,ensure occupational safety,improve the implementation rate of tu-berculosis infection prevention and control measures and achieve scientific and precise prevention and control.