Objective To develop and validate a machine learning model integrating serum tumor markers with multiparametric magnetic resonance imaging(mpMRI)-derived scores for discriminating prostate cancer(PCa)from benign prostatic hyperplasia(BPH)in men with a total prostate-specific antigen(tPSA)of 4-10 ng/mL, so as to optimize prostate biopsy decisions. Methods A retrospective analysis was conducted on the clinical data of 85 PCa patients and 204 BPH patients treated in our hospital during Jan. 2022 and May 2025. All patients underwent prostate biopsy to obtain the pathological results. The data collected were analyzed using univariate and multivariate logistic regression to determine the independent risk factors for PCa. Model performance was assessed in terms of discrimination, calibration, and clinical utility using receiver operating characteristic(ROC)curves, calibration plots, and decision curve analysis(DCA). A classification and regression tree(CART)algorithm was further developed to derive biopsy decision rules. The area under the ROC curve(AUC)of the logistic and CART models were compared to select the optimal predictive model, which was subsequently visualized using SHAP values. Results Age, free prostate-specific antigen(fPSA), fPSA/tPSA(f/tPSA)ratio, and Prostate Imaging-Reporting and Data System(PI-RADS)scores differed significantly between the PCa and BPH groups(P＜0.05). Univariate and multivariate logistic regression identified age, f/tPSA ratio, and PI-RADS scores as independent influencing factors(P＜0.05). A logistic model incorporating these variables achieved an AUC of 0.785(95%CI:0.724-0.846, P＜0.05), with a sensitivity of 70.6% and specificity of 77.9%. The model showed excellent calibration. DCA indicated a positive net benefit when the risk threshold exceeded 7%. The CART model yielded an AUC of 0.769(95%CI:0.717-0.821), with the sensitivity of 63.5% and specificity of 79.9%. Within threshold probabilities of 5%-56%, the CART model provided higher net benefit. When the risk threshold reached 55%, the number of diagnosed cases of PCa began to converge with the actual number. Conclusion Compared with the CART model, the logistic model based on f/tPSA, PI-RADS score and age has a higher diagnostic efficacy for PCa and is the optimal model.

Patients with lung cancer experience a heavy symptom burden during the perioperative period, which seriously affects their recovery and quality of life. Traditional symptom management models rely mainly on scheduled ward rounds during hospitalization and outpatient follow-up after discharge. However, they have limitations such as delayed symptom recognition, lack of post-discharge monitoring, and non-quantitative symptom assessment, which often lead to delayed interventions and low patient satisfaction. In recent years, the symptom management model based on electronic patient-reported outcomes (ePRO) has been increasingly valued in clinical practice. Existing high-level evidence from both domestic and international studies indicates that, through proactive monitoring, real-time alerts, and remote interventions, this model enables dynamic and continuous symptom management and helps improve patient recovery and healthcare experience. As a supplement to routine medical care, the ePRO-based symptom management model aims to enhance the quality of care rather than replace existing medical processes. To promote the standardized application of this model in perioperative lung cancer care, this consensus integrates domestic and international evidence. After multiple rounds of voting by more than 50 experts, it formulates 12 consensus statements covering the three core components, symptom monitoring, alerting, and intervention, to provide scientific and practical recommendations for clinical practice.

Laparoscopic right hemicolectomy is the standard surgical procedure for treating right-sided colon cancer,yet its complex anatomical layers and technically demanding operations pose significant challenges to surgeons'expertise.Currently,surgical skill assessment in clinical practice still predominantly relies on subjective expert evaluation,an approach inherently limited by low efficiency and inconsistent standards.With the rapid advancement of artificial intelligence(AI)technologies(particularly breakthroughs in computer vision and deep learning algorithms),a revolutionary technical foundation has been established for developing objective and precise automated surgical evaluation systems.This article comprehensively reviewed the current clinical application status of right hemicolectomy in China,thoroughly examined the clinical necessity and technical feasibility of constructing an intelligent surgical assessment system,and systematically presented our center's cutting-edge research achievements in this field.Looking ahead,AI-driven intelligent evaluation systems are expected to standardize and quantify surgical skill assessment.Such innovation will not only transform surgical training programs and enhance the quality of multicenter clinical research,but also promote the standardization of precision surgical protocols.This advancement holds significant clinical value and societal importance by improving long-term patient outcomes and fostering more equitable distribution of medical resources.

Objective To investigate the changes of diameter of the main trunk of the extracranial segment of the facial nerve,local hemodynamics and facial muscle thickness in patients with peripheral facial palsy(PFP)at acute stage by high frequency ultrasound(HFUS).Methods Sixty patients with acute PFP were enrolled as the PFP group,and 30 healthy people were selected as the control group.The following facial param-eters were scanned and recorded using HFUS:diameter of the main trunk of the extracranial segment of the facial nerve(FN-d),facial artery diameter(FA-d),facial artery systolic velocity(FA-Vs),facial artery diastolic velocity(FA-Vd),facial artery resistance index(FA-RI),frontalis muscle thickness(FM-t),depressor anguli oris muscle thickness(DAO-t),and depressor labii inferioris muscle thickness(DLI-t).The feasibility of facial param-eters in the diagnosis of acute-phase peripheral facial palsy(PFP)was evaluated,with intra-group and inter-group variability comparisons performed.Pearson correlation analysis was conducted to evaluate the relationship between acute-phase facial parameters and House-Brackmann(H-B)scale scores in PFP patients.Results(1)The H-B score of the PFP group was significantly lower than that of the control group,and the difference was statistically significant(P＜0.05).(2)The reliability of repeated measurements of facial parameters by different testers is good(ICC＞0.75).(3)The FN-d and FA-RI values on the affected side of PFP group were higher than those on the healthy side and control group,and FA-Vs,FA-Vd,DAO-t and DLI-t values were lower than those on the healthy side and control group,with statistical significance(P＜0.05).(4)The FN-d and FA-RI values of the affected side in the PFP group were negatively correlated with the H-S scale score(r=-0.847,P＜0.05;r=-0.863,P＜0.05);FA Vs,FA Vd,DAO-t,DLI-t are positively correlated with H-S scale scores(r=0.808,P＜0.05;r=0.757,P＜0.05;r=0.836,P＜0.05;r=0.694,P＜0.05).Conclusions HFUS can effectively detect the characteristic changes such as the increase in the diameter of the extrcranial facial nerve trunk,local microcircula-tion disturbance and facial muscle atrophy in patients with PFP in the acute stage,and the increase and decrease of FN-d,FA Vs,FA Vd,FA-RI,DAO-t,and DLI-t can accurately reflect the improvement or progress of PFP,which has important guiding significance for clinical disease monitoring and efficacy evaluation.

Laparoscopic right hemicolectomy is the standard surgical procedure for treating right-sided colon cancer,yet its complex anatomical layers and technically demanding operations pose significant challenges to surgeons'expertise.Currently,surgical skill assessment in clinical practice still predominantly relies on subjective expert evaluation,an approach inherently limited by low efficiency and inconsistent standards.With the rapid advancement of artificial intelligence(AI)technologies(particularly breakthroughs in computer vision and deep learning algorithms),a revolutionary technical foundation has been established for developing objective and precise automated surgical evaluation systems.This article comprehensively reviewed the current clinical application status of right hemicolectomy in China,thoroughly examined the clinical necessity and technical feasibility of constructing an intelligent surgical assessment system,and systematically presented our center's cutting-edge research achievements in this field.Looking ahead,AI-driven intelligent evaluation systems are expected to standardize and quantify surgical skill assessment.Such innovation will not only transform surgical training programs and enhance the quality of multicenter clinical research,but also promote the standardization of precision surgical protocols.This advancement holds significant clinical value and societal importance by improving long-term patient outcomes and fostering more equitable distribution of medical resources.

Objective To investigate the changes of diameter of the main trunk of the extracranial segment of the facial nerve,local hemodynamics and facial muscle thickness in patients with peripheral facial palsy(PFP)at acute stage by high frequency ultrasound(HFUS).Methods Sixty patients with acute PFP were enrolled as the PFP group,and 30 healthy people were selected as the control group.The following facial param-eters were scanned and recorded using HFUS:diameter of the main trunk of the extracranial segment of the facial nerve(FN-d),facial artery diameter(FA-d),facial artery systolic velocity(FA-Vs),facial artery diastolic velocity(FA-Vd),facial artery resistance index(FA-RI),frontalis muscle thickness(FM-t),depressor anguli oris muscle thickness(DAO-t),and depressor labii inferioris muscle thickness(DLI-t).The feasibility of facial param-eters in the diagnosis of acute-phase peripheral facial palsy(PFP)was evaluated,with intra-group and inter-group variability comparisons performed.Pearson correlation analysis was conducted to evaluate the relationship between acute-phase facial parameters and House-Brackmann(H-B)scale scores in PFP patients.Results(1)The H-B score of the PFP group was significantly lower than that of the control group,and the difference was statistically significant(P＜0.05).(2)The reliability of repeated measurements of facial parameters by different testers is good(ICC＞0.75).(3)The FN-d and FA-RI values on the affected side of PFP group were higher than those on the healthy side and control group,and FA-Vs,FA-Vd,DAO-t and DLI-t values were lower than those on the healthy side and control group,with statistical significance(P＜0.05).(4)The FN-d and FA-RI values of the affected side in the PFP group were negatively correlated with the H-S scale score(r=-0.847,P＜0.05;r=-0.863,P＜0.05);FA Vs,FA Vd,DAO-t,DLI-t are positively correlated with H-S scale scores(r=0.808,P＜0.05;r=0.757,P＜0.05;r=0.836,P＜0.05;r=0.694,P＜0.05).Conclusions HFUS can effectively detect the characteristic changes such as the increase in the diameter of the extrcranial facial nerve trunk,local microcircula-tion disturbance and facial muscle atrophy in patients with PFP in the acute stage,and the increase and decrease of FN-d,FA Vs,FA Vd,FA-RI,DAO-t,and DLI-t can accurately reflect the improvement or progress of PFP,which has important guiding significance for clinical disease monitoring and efficacy evaluation.

In recent years, with continuous advancements in molecular biology and gene testing technologies, the diagnosis and treatment of colorectal cancer have been rapidly transitioning toward precision medicine. The application of molecular classification, target detection, and liquid biopsy technologies has driven ongoing updates to clinical guidelines. Multidisciplinary team colla-boration, innovations in precision surgical techniques, and the widespread adoption of neoadjuvant combination therapies have collectively promoted more individualized and scientific management of colorectal cancer. Looking ahead,the authors believe that as multi-omics biomarkers, organoid models, and artificial intelligence are increasingly integrated into clinical practice, precision diagnosis and treatment of colorectal cancer will deepen further, offering patients more efficient and personalized therapeutic options.

Objective:To analyze the clinical characteristics of locally advanced rectal cancer patients with pathological complete response (pCR) after neoadjuvant chemoradiotherapy combined with immunotherapy.Methods:The retrospective cohort study was conducted. The clinicopatholo-gical data of 46 patients with locally advanced rectal cancer who were admitted to 6 medical centers, including Beijing Friendship Hospital of Capital Medical University et al, from June 2021 to November 2022 were collected. There were 29 males and 17 females, aged (61±4)years. Patients received neoadjuvant chemoradiotherapy combined with immune checkpoint inhibitor therapy, and under-went radical total mesorectal excision during 6-12 weeks after radiotherapy. Observation indicators: (1) comparison of clinical characteristics between pCR and non-pCR patients;(2) postoperative complications and adverse reactions of pCR and non-pCR patients. Comparison of measurement data with normal distribution between groups was conducted using the 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 or Fisher exact probability. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Results:(1) Comparison of clinical characteristics between pCR and non-pCR patients. Before neoadjuvant therapy, there were 14 cases aged ≥50 years and 6 cases aged <50 years in pCR patients, versus 25 cases and 1 case in non-pCR patients, showing a significant difference between the two groups ( P<0.05). After neoadjuvant therapy, cases in clinical stage T0, T1, T2, T3, T4 were 11, 1, 5, 3, 0 for pCR patients versus 7, 4, 2, 11, 2 for non-pCR patients, cases of tumor regression grade 1, 2, 3, 4 were 11, 8, 1, 0 for pCR patients versus 7, 14, 4, 1 for non-pCR patients, cases in low-risk, medium-risk, high-risk of neoadjuvant rectal scoring and grading were 20, 0, 0 for pCR patients versus 4, 18, 4 for non-pCR patients, respectively, showing significant differences in above indicators between the two groups ( Z=-2.256, -2.104, -5.458, P<0.05). (2) Postoperative complications and adverse reactions of pCR and non-pCR patients. Postoperative complications occurred in 2 cases of pCR patients and 5 cases of non-pCR patients, postoperative adverse reactions occurred in 11 cases of pCR patients and 10 cases of non-pCR patients, showing no significant difference between the two groups ( P>0.05). Conclusion:Compared with locally advanced rectal cancer patients aged ≥50 years, those aged <50 years have significant benefits from neoadjuvant chemoradiotherapy combined with immunotherapy. Clinical T staging and magnetic resonance imaging-detected tumor regression grade after neoadjuvant therapy have predictive value for patients with pCR .

OBJECTIVES: To investigate the expression of parathyroid hormone-like hormone (PTHLH) in hepatocellular carcinoma (HCC) and analyze its correlation with clinical prognosis, its regulatory effects on HCC cell behaviors, and the signaling pathways mediating its effects. METHODS: We analyzed the differential expression of PTHLH in HCC and adjacent tissues and its association with patient prognosis based on data from TCGA and GEO databases and from 70 HCC patients treated in our hospital. The effects of PTHLH knockdown and overexpression on proliferation, migration, and invasion of cultured HCC cells were investigated using CCK-8 assay, colony formation assay, Transwell migration and invasion assays, and the signaling pathways activated by PTHLH were detected using Western blotting. RESULTS: TCGA and GEO database analysis showed significant overexpression of PTHLH mRNA in HCC tissues, which was associated with poor prognosis of the patients (P<0.05). High PTHLH mRNA expression was a probable independent prognostic risk factor for HCC (P<0.05). In the clinical samples, PTHLH mRNA and protein expressions were significantly higher in HCC tissues than in the adjacent tissues (P<0.001 or 0.01). Univariate and multivariate Cox regression analyses suggested that high PTHLH mRNA expression was an independent risk factor to affect postoperative disease-free survival of HCC patients (P<0.05). The prognostic prediction model based on PTHLH mRNA expression showed an improved accuracy for predicting the risk of postoperative recurrence in HCC patients. In cultured HCC cells, PTHLH overexpression significantly promoted cell proliferation, colony formation, migration and invasion, and caused activation of the ERK/JNK signaling pathway in Huh7 and Hep3B cells. CONCLUSIONS High PTHLH expression promotes HCC progression and is associated with poor patient prognosis. Its pro-tumor effects may be mediated by activation of the ERK/JNK signaling pathway.
Humans ; Carcinoma, Hepatocellular/metabolism* ; Liver Neoplasms/metabolism* ; Prognosis ; Cell Proliferation ; Parathyroid Hormone-Related Protein/genetics* ; Cell Line, Tumor ; Cell Movement ; Disease Progression ; Signal Transduction ; Male ; RNA, Messenger/genetics* ; Female

OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assessment. METHODS: Elderly critically ill patients aged ≥ 65 years who were hospitalized in the intensive care unit (ICU) of Tacheng People's Hospital of Ili Kazak Autonomous Prefecture from June 2017 to May 2020 were retrospectively selected. Basic parameters including demographic characteristics, basic vital signs and fluid intake and output within 24 hours after admission, as well acute physiology and chronic health evaluation II (APACHE II), Glasgow coma score (GCS) and sequential organ failure assessment (SOFA) were also collected. According to outcomes in hospital, patients were divided into survival group and death group. Four datasets were constructed respectively, namely baseline dataset (B), including age, body temperature, heart rate, pulse oxygen saturation, respiratory rate, mean arterial pressure, urine output volume, infusion volume, and crystal solution volume; B+APACHE II dataset (BA), B+GCS dataset (BG), and B+SOFA dataset (BS). Then three machine learning algorithms, Logistic regression (LR), extreme gradient boosting (XGboost) and gradient boosting decision tree (GBDT) were used to develop the corresponding mortality predictive models within four datasets. The feature importance histogram of each prediction model was drawn by SHapley additive explanation (SHAP) method. The area under curve (AUC), accuracy and F1 score of each model were compared to determine the optimal prediction model and then illuminate the nomogram. RESULTS: A total of 392 patients were collected, including 341 in the survival group and 51 in the death group. There were statistically significant differences in heart rate, pulse oxygen saturation, mean arterial pressure, infusion volume, crystal solution volume, and etiological distribution between the two groups. The top three causes of death were shock, cerebral hemorrhage, and chronic obstructive pulmonary disease. Among the 12 prognostic models trained by three machine learning algorithms, overall performance of prognostic models based on B dataset was behind, whereas the LR model trained by BA dataset achieved the best performance than others with AUC of 0.767 [95% confidence interval (95%CI) was 0.692-0.836], accuracy of 0.875 (95%CI was 0.837-0.903) and F1 score of 0.190. The top 3 variables in this model were crystal solution volume with first 24 hours, heart rate and mean arterial pressure. The nomogram of the model showed that the total score between 150 and 230 were advisable. CONCLUSION The interpretable machine learning model including simple bedside parameters combined with APACHE II score could effectively identify the risk of death in elderly patients with critically illness.
Humans ; Critical Illness ; Machine Learning ; Aged ; Algorithms ; Intensive Care Units ; Retrospective Studies ; APACHE ; Prognosis ; Organ Dysfunction Scores ; Hospital Mortality ; Male ; Female