Professor Lau Wanyee, a member of the Chinese Academy of Sciences and a pro-fessor at the Chinese University of Hong Kong, actively advocates conducting clinical researches through "planting fruit trees" and "growing orchards", aiming to cultivate a team of dual-skilled talents in clinical practice and research, effectively improve the scientific and technological level of clinical medicine in China, make voice heard in the international medical science field, and better serve human health. He organized a clinical research training course in scholars′ forum for Hepatobiliary Young Expert Working Group of Chinese College of Surgeons. Throughout three sessions of the training course, a distinct theme was focused on how to enhance the level of clinical research in China and make voice heard by the international scholars. A group of multi-dimensional experts were gathered, including experts from surgery, methodology, and management, as well as both renowned experts and young talents. A lively teaching model was adopted, combining guided presentations with interactive discussion and debate sessions. A clean and upright academic spirit was strongly advocated, in which international rules were adopted to conduct in-depth analysis and sharp criticism of seven proposed clinical research projects and four published papers with high international influence to find quarrel in a straw. This clinical research training course provides a new model of guidance for young physicians in conducting clinical research. As a result, all attendees felt deeply educated and benefited greatly from the training session. This training activity not only laid a solid foundation for the development of scientization, standardization, and internationali-zation of clinical research in digestive surgery in China, but also demonstrated a correct path for cultivating a group of young and middle-aged clinical medical scientists with scientific spirit.

Surgical treatment is the primary curative approach for hepatocellular carcinoma (HCC). In China, the proportion of advanced HCC is high, with a low rate of surgical removal at initial diagnosis and a high rate of postoperative recurrence, posing a serious threat to public health. With the advent of new therapeutic drugs and updated treatment concepts, the comprehensive treatment of HCC has entered a new era. Systemic treatments represented by targeted therapy and immuno-therapy, non-surgical local treatments such as interventional and radiotherapy, and the combination of systemic and local treatments, have significantly improved the treatment efficacy, bringing hope to patients. The authors review past studies, summarize diagnostic and treatment experience, and discuss the comprehensive treatment strategy for HCC in the era of targeted and immunotherapy, with surgery as the main approach.

Advanced heart failure, also known as stage D heart failure, is a complex condition that requires specialized interventions due to its resource-intensive nature.The clinical course of advanced heart failure is highly unpredictable, with various factors complicating its management.The continuous development of new drugs, enhanced surgical techniques, and device therapy has the potential to extend the life expectancy of heart failure patients.With the aging population, the prevalence of advanced heart failure is on the rise.One of the defining characteristics of advanced heart failure is the inability to adequately meet the metabolic demands of end-organs and skeletal muscle, often associated with low cardiac output syndrome.For patients who do not respond to optimized guideline-directed therapies, consideration of heart transplantation, mechanical circulatory support, and palliative care is appropriate.This systematic review examines the definition, key features, short-term and long-term management strategies, as well as palliative care for advanced heart failure.

Advanced heart failure, also known as stage D heart failure, is a complex condition that requires specialized interventions due to its resource-intensive nature.The clinical course of advanced heart failure is highly unpredictable, with various factors complicating its management.The continuous development of new drugs, enhanced surgical techniques, and device therapy has the potential to extend the life expectancy of heart failure patients.With the aging population, the prevalence of advanced heart failure is on the rise.One of the defining characteristics of advanced heart failure is the inability to adequately meet the metabolic demands of end-organs and skeletal muscle, often associated with low cardiac output syndrome.For patients who do not respond to optimized guideline-directed therapies, consideration of heart transplantation, mechanical circulatory support, and palliative care is appropriate.This systematic review examines the definition, key features, short-term and long-term management strategies, as well as palliative care for advanced heart failure.

Humans ; Consensus ; Liver Neoplasms/therapy* ; China

Objective:To evaluate the efficacy and safety of 192Ir brachytherapy combined with external beam radiation for the treatment of unresectable hilar cholangiocarcinoma. Methods:The clinical data of 26 patients with unresectable hilar cholangiocarcinoma admitted to Hebei Province Cangzhou Hospital of Integrated Traditional and Western Medicine from February 2016 to July 2018 were retrospectively analyzed. All patients received 192Ir brachytherapy combined with external beam radiation. First, percutaneous hepatobiliary stent implantation was given, followed by external beam radiotherapy (radiotherapy dose 45 Gy, 25 times), and then 192Ir brachytherapy (radiotherapy dose 20 Gy, 4 times), a total of 4 to 6 cycles of chemotherapy. The short-term and long-term efficacy were evaluated, and the Karnofsky performance status (KPS) score, CA19-9, total bilirubin (TBIL), direct bilirubin (DBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST) level changes and percutaneous transhepatic biliary drainage (PTCD) tube indwelling time were analyzed. Adverse reactions were recorded according to the Radiation Therapy Oncology Group (RTOG) injury classification standard. Results:Three months after the end of treatment, 4 cases (15.4%) were completely remitted, 20 cases (76.9%) were partially remitted, 2 cases (7.7%) were stable, and there was no disease progression. The objective remission rate was 92.3% (24/26). The 1-year and 2-year local control rates were 75.0% and 62.5% respectively, and the 1-year and 2-year overall survival rates were 57.7% and 26.9% respectively. The KPS score was 70.39±10.76 one month after the treatment, which was significantly higher than the 60.00±10.58 before treatment ( t=-27.00, P<0.001). The levels of CA19-9 before treatment and 1 month, 2 months and 3 months after treatment were (390.88±202.62) U/ml, (322.45±204.06) U/ml, (254.00±160.49) U/ml, (182.85±124.05) U/ml, which showed a gradual decrease trend, and there was a statistically significant difference ( F=126.94, P<0.001). TBIL [(250.88±80.83) μmol/L, (153.98±61.74) μmol/L, (93.45±38.12) μmol/L, (53.82±26.75) μmol/L], DBIL [(205.82±66.68) μmol/L, (133.23±58.53) μmol/L, (64.31±36.25) μmol/L, (40.55±26.16) μmol/L], ALT [(163.92±54.12) U/L, (68.23±28.86) U/L, (45.73±21.94) U/L, (32.66±12.88) U/L], AST [(177.69±58.68) U/L, (79.23±32.87) U/L, (49.77±25.45) U/L, (35.54±16.10) U/L] showed progressive decline, with statistically significant differences ( F=315.60, P<0.001; F=385.30, P<0.001; F=284.24, P<0.001; F=311.80, P<0.001), and liver function was improved. The PTCD tube was removed after treatment in all patients, with a median time of 54 days (49-96 days). During the treatment, bone marrow suppression, nausea and vomiting, abdominal pain and biliary tract infection occurred. All of them improved after symptomatic treatment. No serious complications such as bile leakage and biliary hemorrhage occurred. Conclusion:192Ir brachytherapy combined with external beam radiation has a reliable curative effect in the treatment of unresectable hilar cholangiocarcinoma. It can improve the quality of life of patients, and the adverse reactions can be tolerated. It provides a feasible and safe treatment method for the clinic.

Objective:To explore the efficacy and safety of 192Ir three-dimensional brachytherapy combined with intensity-modulated radiotherapy in patients with unresectable advanced obstructive extrahepatic cholangiocarcinoma. Methods:A retrospective analysis of the efficacy and safety of 18 patients with unresectable advanced malignant obstructive extrahepatic cholangiocarcinoma admitted to the Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine in Hebei province from January 2014 to January 2018.All patients were pathologically confirmed and received biliary stent implantation, followed by sequential intensity-modulated radiotherapy and 192Ir three-dimensional brachytherapy.The clinical data of all patients were collected, including gender, age, clinical stage, adverse reactions, brachytherapy dose-volume parameters, changes in bilirubin levels before and after treatment, evaluation of efficacy, local control rate and survival rate. Results:All patients successfully completed the treatment.Re-examination was conducted one month after the combined treatment, of the 18 patients, 16 had partial remission (PR) and 2 were stable(SD). Among them, 14 patients had tumor shrinkage after brachytherapy compared with intensity-modulated radiotherapy, and 4 patients showed no significant change.The 6-month local control rate (LC) was 94.4% (17/18). The 1-year survival rate was 55.6% (10/18), and the 2-year survival rate was 38.9% (7/18). Statistical analysis showed that after combined treatment, total bilirubin and direct bilirubin were significantly reduced, and jaundice symptoms were significantly relieved.The adverse reactions of the patient were nausea, vomiting, fever, biliary tract infection, etc.There were no adverse reactions of grade 3 or above.Conclusions:192Ir three-dimensional brachytherapy combined with intensity-modulated radiotherapy in patients with inoperable advanced extrahepatic cholangiocarcinoma has a good local control rate and tolerable adverse reactions, but the impact on long-term survival requires a large sample of controlled studies.

Objective:To evaluate the clinical efficacy and adverse events of 192Ir high-dose rate brachytherapy (HDR-BT) in the treatment of locally recurrent non-small cell lung cancer (NSCLC). Methods:Clinical data of 22 cases of recurrent NSCLC after radiotherapy admitted to our hospital from September 2013 to March 2018 were retrospectively analyzed. 192Ir HDR-BT was adopted for reradiotherapy. The prescription dose was 30Gy for 1 fraction. CT scan was reviewed every 1 month in the first 3 months after treatment and every 3 months after 3 months. Local control rate and adverse events were evaluated. The 1-and 2-year overall survival (OS) rates of re-treatment after relapse were calculated. Results:All the 22 patients completed the treatment successfully. The 1-, 3-and 6-month complete response (CR) rates were 9%, 14% and 14%, 82%, 82% and 82% for the partial response (PR) rates, 5%, 0% and 0% for the stable disease (SD) rates, 5%, 5% and 5% for the progressive disease (PD) rates, 91%, 96% and 96% for the objective response rates (ORR), respectively. The 1-and 2-year OS rates of re-treatment after relapse were 59% and 27%. Five patients (23%) experienced acute radiation-induced pneumonitis (3 cases of grade 1 and 2 cases of grade Ⅱ), 4 cases (18%) of radiation-induced bone marrow suppression (3 cases of grade I leukopenia and 1 case of grade I thrombocytopenia) and 1 case of postoperative pneumothorax. All these adverse events were mitigated after symptomatic treatment.Conclusion:192Ir HDR-BT is an efficacious and safe treatment of locally recurrent NSCLC.

Objective:To investigate the application value of machine learning algorithms and COX nomogram in the survival prediction of hepatocellular carcinoma (HCC) after resection.Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 375 patients with HCC who underwent radical resection in the Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College from January 2012 to January 2017 were collected. There were 304 males and 71 females, aged from 21 to 79 years, with a median age of 57 years. According to the random numbers showed in the computer, 375 patients were divided into training dataset consisting of 300 patients and validation dataset consisting of 75 patients, with a ratio of 8∶2. Machine learning algorithms including logistic regression (LR), supporting vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural network (ANN) were used to construct survival prediction models for HCC after resection, so as to identify the optimal machine learning algorithm prediction model. A COX nomogram prediction model for predicting postoperative survival in patients with HCC was also constructed. Comparison of performance for predicting postoperative survival of HCC patients was conducted between the optimal machine learning algorithm prediction model and the COX nomogram prediction model. Observation indicators: (1) analysis of clinicopathological data of patients in the training dataset and validation dataset; (2) follow-up and survival of patients in the training dataset and validation dataset; (3) construction and evaluation of machine learning algorithm prediction models; (4) construction and evaluation of COX nomogram prediction model; (5) evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model. Follow-up was performed using outpatient examination or telephone interview to detect survival of patients up to December 2019 or death. Measurement data with normal distribution were expressed as Mean± SD, and comparison between groups was analyzed by the paired t test. Measurement data with skewed distribution were expressed as M ( P25, P75) or M (range), and comparison between groups was analyzed by the Mann-Whitney U test. Count data were represented as absolute numbers. Comparison between groups was performed using the chi-square test when Tmin ≥5 and N ≥40, using the calibration chi-square test when 1≤ Tmin ≤5 and N ≥40, and using Fisher exact probability when Tmin <1 or N <40. The Kaplan-Meier method was used to calculate survival rate and draw survival curve. The COX proportional hazard model was used for univariate analysis, and variables with P<0.2 were included for the Lasso regression analysis. According to the lambda value, variables affecting prognosis were screened for COX proportional hazard model to perform multivariate analysis. Results:(1) Analysis of clinicopathological data of patients in the training dataset and validation dataset: cases without microvascular invasion or with microvascular invasion, cases without liver cirrhosis or with liver cirrhosis of the training dataset were 292, 8, 105, 195, respectively, versus 69, 6, 37, 38 of the validation dataset, showing significant differences between the two groups ( χ2=4.749, 5.239, P<0.05). (2) Follow-up and survival of patients in the training dataset and validation dataset: all the 375 patients received follow-up. The 300 patients in the training dataset were followed up for 1.1-85.5 months, with a median follow-up time of 50.3 months. Seventy-five patients in the validation dataset were followed up for 1.0-85.7 months, with a median follow-up time of 46.7 months. The postoperative 1-, 3-year overall survival rates of the 375 patients were 91.7%, 79.5%. The postoperative 1-, 3-year overall survival rates of the training dataset were 92.0%, 79.7%, versus 90.7%, 81.9% of the validation dataset, showing no significant difference in postoperative survival between the two groups ( χ2=0.113, P>0.05). (3) Construction and evaluation of machine learning algorithm prediction models. ① Selection of the optimal machine learning algorithm prediction model: according to information divergence of variables for prediction of 3 years postoperative survival of HCC, five machine learning algorithms were used to comprehensively rank the variables of clinicopathological factors of HCC, including LR, SVM, DT, RF, and ANN. The main predictive factors were screened out, as hepatitis B e antigen (HBeAg), surgical procedure, maximum tumor diameter, perioperative blood transfusion, liver capsule invasion, and liver segment Ⅳ invasion. The rank sequence 3, 6, 9, 12, 15, 18, 21, 24, 27, 29 variables of predictive factors were introduced into 5 machine learning algorithms in turn. The results showed that the area under curve (AUC) of the receiver operating charateristic curve of LR, SVM, DT, and RF machine learning algorithm prediction models tended to be stable when 9 variables are introduced. When more than 12 variables were introduced, the AUC of ANN machine learning algorithm prediction model fluctuated significantly, the stability of AUC of LR and SVM machine learning algorithm prediction models continued to improve, and the AUC of RF machine learning algorithm prediction model was nearly 0.990, suggesting RF machine learning algorithm prediction model as the optimal machine learning algorithm prediction model. ② Optimization and evaluation of RF machine learning algorithm prediction model: 29 variables of predictive factors were sequentially introduced into the RF machine learning algorithm to construct the optimal RF machine learning algorithm prediction model in the training dataset. The results showed that when 10 variables were introduced, results of grid search method showed 4 as the optimal number of nodes in DT, and 1 000 as the optimal number of DT. When the number of introduced variables were not less than 10, the AUC of RF machine learning algorithm prediction model was about 0.990. When 10 variables were introduced, the RF machine learning algorithm prediction model had an AUC of 0.992 for postoperative overall survival of 3 years, a sensitivity of 0.629, a specificity of 0.996 in the training dataset, an AUC of 0.723 for postoperative overall survival of 3 years, a sensitivity of 0.177, a specificity of 0.948 in the validation dataset. (4) Construction and evaluation of COX nomogram prediction model. ① Analysis of postoperative survival factors of HCC patients in the training dataset. Results of univariate analysis showed that HBeAg, alpha fetoprotein (AFP), preoperative blood transfusion, maximum tumor diameter, liver capsule invasion, and degree of tumor differentiation were related factors for postoperative survival of HCC patients [ hazard ratio ( HR)=1.958, 1.878, 2.170, 1.188, 2.052, 0.222, 95% confidence interval ( CI): 1.185-3.235, 1.147-3.076, 1.389-3.393, 1.092-1.291, 1.240-3.395, 0.070-0.703, P<0.05]. Clinico-pathological data with P<0.2 were included for Lasso regression analysis, and the results showed that age, HBeAg, AFP, surgical procedure, perioperative blood transfusion, maximum tumor diameter, tumor located at liver segment Ⅴ or Ⅷ, liver capsule invasion, and degree of tumor differentiation as high differentiation, moderate-high differentiation, moderate differentiation, moderate-low differentiation were related factors for postoperative survival of HCC patients. The above factors were included for further multivariate COX analysis, and the results showed that HBeAg, surgical procedure, maximum tumor diameter were independent factors affecting postoperative survival of HCC patients ( HR=1.770, 8.799, 1.142, 95% CI: 1.049- 2.987, 1.203-64.342, 1.051-1.242, P<0.05). ② Construction and evaluation of COX nomogram prediction model: the clinicopathological factors of P≤0.1 in the COX multivariate analysis were induced to Rstudio software and rms software package to construct COX nomogram prediction model in the training dataset. The COX nomogram prediction model for predicting postoperative overall survival had an consistency index of 0.723 (se=0.028), an AUC of 0.760 for postoperative overall survival of 3 years in the training dataset, an AUC of 0.795 for postoperative overall survival of 3 years in the validation dataset. The verification of the calibration plot in the training dataset showed that the COX nomogram prediction model had a good prediction performance for postoperative survival. COX nomogram score=0.627 06×HBeAg (normal=0, abnormal=1)+ 0.134 34×maximum tumor diameter (cm)+ 2.107 58×surgical procedure (laparoscopy=0, laparotomy=1)+ 0.545 58×perioperative blood transfusion (without blood transfusion=0, with blood transfusion=1)-1.421 33×high differentiation (non-high differentiation=0, high differentiation=1). The COX nomogram risk scores of all patients were calculated. Xtile software was used to find the optimal threshold of COX nomogram risk scores. Patients with risk scores ≥2.9 were assigned into high risk group, and patients with risk scores <2.9 were assigned into low risk group. Results of Kaplan-Meier overall survival curve showed a significant difference in the postoperative overall survival between low risk group and high risk group of the training dataset ( χ2=33.065, P<0.05). There was a significant difference in the postoperative overall survival between low risk group and high risk group of the validation dataset ( χ2=6.585, P<0.05). Results of further analysis by the decision-making curve showed that COX nomogram prediction model based on the combination of HBeAg, surgical procedure, perioperative blood transfusion, maximum tumor diameter, and degree of tumor differentiation was superior to any of the above individual factors in prediction performance. (5) Evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model: prediction difference between two models was investigated by analyzing maximun tumor diameter (the important variable shared in both models), and by comparing the predictive error curve of both models. The results showed that the postoperative 3-year survival rates predicted by RF machine learning algorithm prediction model and COX nomogram prediction model were 77.17% and 74.77% respectively for tumor with maximum diameter of 2.2 cm ( χ2=0.182, P>0.05), 57.51% and 61.65% for tumor with maximum diameter of 6.3 cm ( χ2=0.394, P>0.05), 51.03% and 27.52% for tumor with maximum diameter of 14.2 cm ( χ2=12.762, P<0.05). With the increase of the maximum tumor diameter, the difference in survival rates predicted between the two models turned larger. In the validation dataset, the AUC for postoperative overall survival of 3 years of RF machine learning algorithm prediction model and COX nomogram prediction model was 0.723 and 0.795, showing a significant difference between the two models ( t=3.353, P<0.05). Resluts of Bootstrap cross-validation for prediction error showed that the integrated Brier scores of RF machine learning algorithm prediction model and COX nomogram prediction model for predicting 3-year survival were 0.139 and 0.134, respectively. The prediction error of COX nomogram prediction model was lower than that of RF machine learning algorithm prediction model. Conclusion:Compared with machine learning algorithm prediction models, the COX nomogram prediction model performs better in predicting 3 years postoperative survival of HCC, with fewer variables, which is easy for clinical use.