Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Objective:To explore the value of salivary gland imaging based on deep learning and Delta radiomics in assessing salivary gland injury after 131I treatment in post-thyroidectomy thyroid cancer patients. Methods:A retrospective analysis on 223 patients (46 males, 177 females, age(47.7±14.0) years ) with papillary thyroid cancer, who underwent total thyroidectomy and 131I treatment in Affiliated Hospital of Guilin Medical University between December 2019 and January 2022, was conducted. All patients underwent salivary gland 99Tc mO 4- imaging before and after 131I therapy. The patients were categorized according to salivary gland function based on 99Tc mO 4- imaging results (normal salivary gland vs salivary gland injury), and divided into training and test sets in a ratio of 7∶3. A ResNet-34 neural network model was trained using images at the time of maximum salivary gland radioactivity and those based on background radioactivity counts for structured image feature data. The Delta radiomics approach was then used to subtract the image feature values of the two periods, followed by feature selection through t-test, correlation analysis, and the least absolute shrinkage and selection operator( LASSO) algorithm, to develop logistic regression (LR), support vector machine (SVM), and K-nearest neighbor (KNN) predictive models. The diagnostic performance of 3 models for salivary gland function on the test set was compared with that of the manual interpretation. The AUCs of the 3 models on the test set were compared (Delong test). Results:Among the 67 cases of the test set, the diagnostic accuracy of 3 physicians were 89.6%(60/67), 83.6%(56/67), and 82.1%(55/67) respectively, with the time required for diagnosis of 56, 74 and 55 min, respectively. The accuracies of LR, SVM, and KNN models were 91.0%(61/67), 86.6%(58/67), and 82.1%(55/67), with the required times of 12.5, 15.3 and 17.9 s, respectively. All 3 radiomics models demonstrated good classification and predictive capabilities, with AUC values for the training set of 0.972, 0.965, and 0.943, and for the test set of 0.954, 0.913, and 0.791, respectively. There were no significant differences among the AUC values for the test set ( z values: 0.72, 1.18, 1.82, all P>0.05). Conclusion:The models based on deep learning and Delta radiomics possess high predictive value in assessing salivary gland injury following 131I treatment after surgery in patients with thyroid cancer.

Objective:To investigate the safety and therapeutic effect of split liver transplantation (SLT) in clinical application.Methods:This is a retrospective case-series study. The clinical data of 203 consecutive SLT, 79 living donor liver transplantation (LDLT) and 1 298 whole liver transplantation (WLT) performed at the Third Affiliated Hospital of Sun Yat-sen University from July 2014 to July 2023 were retrospectively analyzed. Two hundred and three SLT liver grafts were obtained from 109 donors. One hundred and twenty-seven grafts were generated by in vitro splitting and 76 grafts were generated by in vivo splitting. There were 90 adult recipients and 113 pediatric recipients. According to time, SLT patients were divided into two groups: the early SLT group (40 cases, from July 2014 to December 2017) and the mature SLT technology group (163 cases, from January 2018 to July 2023). The survival of each group was analyzed and the main factors affecting the survival rate of SLT were analyzed. The Kaplan-Meier method and Log-rank test were used for survival analysis.Results:The cumulative survival rates at 1-, 3-, and 5-year were 74.58%, 71.47%, and 71.47% in the early SLT group, and 88.03%, 87.23%, and 87.23% in the mature SLT group, respectively. Survival rates in the mature SLT group were significantly higher than those in the early SLT group ( χ2=5.560, P=0.018). The cumulative survival rates at 1-, 3- and 5-year were 93.41%, 93.41%, 89.95% in the LDLT group and 87.38%, 81.98%, 77.04% in the WLT group, respectively. There was no significant difference among the mature SLT group, the LDLT group and the WLT group ( χ2=4.016, P=0.134). Abdominal hemorrhage, infection, primary liver graft nonfunction,and portal vein thrombosis were the main causes of early postoperative death. Conclusion:SLT can achieve results comparable to those of WLT and LDLT in mature technology liver transplant centers, but it needs to go through a certain time learning curve.

Objective:The National Central Cancer Registry estimates the number of new cancer cases and deaths in China in 2022, using incidence and mortality data collected by the National Cancer Center.Methods:According to the data of 700 cancer registries in 2018 and the data of 106 cancer registries from 2010 to 2018, the age-period-cohort model was used to estimate the incidence rate and mortality rate of all cancers and 23 types of cancer in 2022, stratified by gender and urban and rural areas. We estimated the number of new cancer cases and deaths in China in 2022 based on the estimated rate and population data in 2022.Results:The estimated results showed that in 2022, there were approximately 4 824 700 new cancer cases in China (2 533 900 in males and 2 290 800 in females), with an age-standardized incidence rate of Chinese population (ASIR) of 208.58 per 100 000 (212.67 per 100 000 for males and 208.08 per 100 000 for females). Approximately 2 903 900 new cancer cases occurred in urban areas, with an ASIR of 212.95 per 100 000. It was estimated about 1 920 800 new cancer cases in rural areas, and the ASIR was 199.65 per 100 000. The top five cancers (lung cancer 1 060 600, colorectal cancer 517 100, thyroid cancer 466 100, liver cancer 367 700 and female breast cancer 357 200) accounted for 57.4% of all new cases. The estimated number of deaths from cancer in China in 2022 was 2 574 200 (1 629 300 in males and 944 900 in females), with an age-standardized mortality rate of Chinese population (ASMR) of 97.08 per 100 000 (127.70 per 100 000 in males and 68.67 per 100 000 in females). The number of deaths from cancer in urban and rural areas was about 1 400 600 and 1 173 400, with the ASMR of 92.37 and 103.97 per 100 000 in urban and rural areas, respectively. The top five leading cause of cancers death (lung cancer 733 300, liver cancer 316 500, gastric cancer 260 400, colorectal cancer 240 000 and esophageal cancer 187 500) accounted for 67.5% of all cancer deaths. Lung cancer ranked first in the incidence and mortality in men and women. The incidence rate in urban areas was higher than that in rural areas, while the mortality rate was lower than that in rural areas.Conclusions:The burden of cancer in China is still relatively heavy, with significant differences in cancer patterns in gender, urban-rural, and regional. The burden of cancer presents a coexistence of developed and developing countries, and the situation of cancer prevention and control is still serious in China.

Background::Breast cancer is ranked among the most prevalent malignancies in the Chinese female population. However, comprehensive reports detailing the latest epidemiological data and attributable disease burden have not been extensively documented.Methods::In 2018, high-quality cancer surveillance data were recorded in 700 population-based cancer registries in China. We extracted data on female breast cancers (International Classification of Diseases, Tenth Revision [ICD-10]: C50) and estimated the incidence and mortality in 2022 according to the baseline data and corresponding trends from 2010 to 2018. Pathological types were classified according to the ICD for Oncology, 3rd Edition codes. Disability-adjusted life years (DALYs) were calculated as the sum of the years of life lost (YLLs) and years lived with disability (YLDs).Results::In 2022, approximately 357,200 new female breast cancer cases and 75,000 deaths occurred in China, accounting for 15.59% and 7.94% of total new cancer cases and deaths, respectively. The age-standardized incidence rate (ASIR) was 33.04 per 100,000. When analyzed by pathological type, the ASIRs for papillary neoplasms, invasive breast carcinoma, rare and salivary gland-type tumors, and other types were 1.13, 29.79, 0.24, and 1.88 per 100,000, respectively. The age-standardized mortality rate (ASMR) was 6.10 per 100,000. A total of 2,628,000 DALYs were found to be attributable to female breast cancer in China, comprising 2,278,300 YLLs and 349,700 YLDs. The ASIR, ASMR, and age-standardized rate (ASR) for DALYs in urban areas were consistently higher than those in rural areas. We observed a four-fold increase in the ASIR and ASR for DALYs and an eight-fold increase in the ASMR among females over 55 years compared with those aged under 55 years.Conclusion::These data provide invaluable insights into the latest epidemiology of female breast cancer in China and highlight the urgency for disease prevention and control strategy formulation.