Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objectives:To investigate the effect of cardiac rehabilitation exercise guided by cardiopulmonary exercise test on cardiopulmonary function, cardiopulmonary endurance and activities of daily living in patients who had coronary heart disease after percutaneous coronary intervention (PCI).Methods:Seventy patients who had coronary heart disease after PCI treated in the Wenzhou Central Hospital from July 2022 to May 2023 were enrolled perspectively (3 cases eventually dropped out of the study), and they were divided into the control group (33 cases) and the rehabilitation group (34 cases) by random number table method. The control group was given conventional medication after PCI, while the rehabilitation group was additionally provided with exercise rehabilitation for 12 weeks on the basis of the control group. The patient′s cardiopulmonary function indicators, cardiopulmonary endurance indicators and ability of daily living(ADL) score of the two groups were compared before and after treatment.Results:After treatment for 12 weeks, the cardiopulmonary function indicators left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), left atrial inner diameter (LAD), left ventricular end-diastolic diameter (LVEDD), left ventricular ejection fraction (LVEF), forced expiratory volume in the first second (FEV 1), ratio of FEV 1 to forced expiratory volume (FVC) (FEV 1/FVC), maximum ventilation quantity (MVV) in the rehabilitation group were obviously improved compared with the control group: (102.8 ± 14.4) ml vs. (114.8 ± 20.4) ml, (39.9 ± 13.7) ml vs. (48.4 ± 16.9) ml, (37.1 ± 3.4) mm vs. (38.9 ± 3.6) mm, (50.3 ± 3.6) mm vs. (52.5 ± 4.7) mm, (64.0 ± 6.8)% vs. (59.6 ± 6.5)%, (2.39 ± 0.38) L vs. (2.10 ± 0.26) L, (77.7 ± 4.0)% vs. (73.8 ± 4.3)%, (101.6 ± 18.7) L/min vs. (89.6 ± 11.1) L/min, there were statistical differences ( P<0.05). After treatment for 12 weeks, the cardiopulmonary endurance indicators peak oxygen uptake (VO 2peak), peak oxygen uptake per kilogram of body weight (VO 2peak/kg), anaerobic threshold (AT), peak metabolic equivalent (METspeak), maximum working load (MWL) in the rehabilitation group were obviously improved compared with the control group: (1 441.8 ± 251.9) ml/min vs. (1 272.5 ± 207.0) ml/min, (20.7 ± 3.6) ml/(min·kg) vs. (18.2 ± 2.5) ml/(min·kg), (1 346.8 ± 201.3) ml/min vs. (1 075.4 ± 176.7) ml/min, (5.9 ± 1.1) Met vs. (5.2 ± 0.7) Met, (107.3 ± 29.1) Watt vs. (88.4 ± 17.8)Watt, there were statistical differences ( P<0.05). After treatment for 12 weeks, the ADL scores in the rehabilitation group was higher than that in the control group: (90.9 ± 8.1) scores vs. (85.6 ± 11.6) scores, there was statistical difference ( P<0.05). Conclusions:Carrying out cardiac rehabilitation exercise guided by cardiopulmonary exercise test can significantly enhance the cardiopulmonary function, cardiopulmonary endurance and self-care ability in patients who had accepted PCI for coronary heart disease.