Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

Objective To construct a nomogram prediction model for the risk of mild cognitive impair-ment (MCI) in elderly people aged ≥ 60-year-old.Methods A total of 502 elderly permanent residents in Guangxi were selected as the research subjects by the multi-stage stratified random sampling method,and the general situation questionnaire and the Beijing edition of MoCA-BJ scale were used to investigate the elderly people,and their anthropometric indicators were collected.The minimum absolute shrinkage rate and selection operator (LASSO) regression were used to screen the characteristic variables.The MCI risk nomogram pre-diction model was constructed.The receiver operating characteristic (ROC) curve and calibration curve were adopted to conduct the fitting effect test on the prediction model.Results Among the 502 elderly people,244 cases (46.04％) had the normal cognition and 258 cases (48.68％) had MCI.The logistic regression analysis showed that the age,education background,month income,children support,calf circumference,BMI and body fat index were the influencing factors of MCI in the elderly people,and the nomogram prediction model of the MCI risk in the elderly people was constructed by these seven variables.The area under the ROC curve (AUC) of the model was 0.790 (95％CI:0.750-0.829),the sensitivity was 0.64,the specificity was 0.62,the C-index index was 0.790,and the model fitting x2=8.111,P=0.454,the predictive value was basically consistent with the actual value.Conclusion The nomogram prediction model of MCI risk in the elderly peo-ple is successfully constructed with good predictive effect.

Objective:To explore the short-term and long-term prognostic outcomes of anatomical liver resection(AR)for patients with perihilar cholangio-carcinoma. Methods:This is a retrospective study.All data were obtained from 4 centers,including The First Affiliated Hospital of Army Medical University,Eastern Hepatobiliary Hospital of Naval Medical University,Sichuan Provincial People's Hospital and Affiliated Hospital of Qinghai University,of a multi-center database.A total of 305 consecutive perihilar cholangiocarcinoma patients receiving radical resection between January 2013 and June 2021 were included in this study.According to the method of liver resection,all patients were divided into the AR group(n=205)and the non-anatomical liver resection(NAR)group(n=100).The baseline characteristics,short-term prognosis and long-term prognosis of the 2 groups were compared. Results:The perioperative transfusion rate and the 30-day complication rate were significantly lower in the AR group than those in the NAR group(P＜0.05).There was no statistically significant difference in the survival rates between the AR and the NAR groups(P＞0.05). Conclusion:The 2 hepatic resection modalities had no obvious effect on the long-term prognosis of perihilar cholangiocarcinoma patients after radical resection,but choosing AR tends to achieve a better short-term prognosis and is worth promoting in clinical practice.

OBJECTIVE:To systematically review the efficacy and safety of cinacalcet in the treatment of hemodialysis pa-tients with secondary hyperparathyroidism,and provide evidence-based reference for the clinical treatment. METHODS:Retrieved from Medline,Cochrane Library,EMBase and CBM,randomized controlled trials(RCT)about cinacalcet in the treatment of he-modialysis patients with secondary hyperparathyroidism (SHPT) were collected. Meta-analysis was performed by using Rev Man 5.3.5 software after data extract and quality evaluation by Cochrane systematic Rev Man 5.3.5. RESULTS:Totally 7 RCTs were en-rolled,involving 1 987 patients. Results of Meta-analysis showed cinacalcet can significantly reduce the rate of surgical parathyroid-ectomy[RR=0.23,95%CI(0.06,0.89),P=0.03],incidence of fracture[RR=0.26,95%CI(0.12,0.60),P=0.002] and increase the incidences of hypocalcemia[RR=9.81,95%CI(3.92,4.59),P<0.001],nausea[RR=1.97,95%CI(1.58,2.46),P<0.001] and vomit-ing[RR=1.91,95%CI(1.50,2.42),P<0.001],while it showed no significant effect on the the incidence of all-cause mortality and cardiovascular death. CONCLUSIONS:The clinical efficacy of cinacalcet in the treatment of hemodialysis patients with secondary hyperparathyroidism is good,but there are common adverse reactions such as nausea and vomiting,hypocalcemia.

Objective To compare volumetric‐modulated arc therapy(VMAT) with intensity‐modulated radiation therapy (IMRT) for brain metastases with regard to the dosimetric character .Methods Sixty patients who were diagnosed with brain me‐tastases were included in this study .The target area received two dose levels using late addition amount technique ,WBRT (30 Gy/10 F) with following addition (20 Gy/10 F) to 59 Gy .For a fair comparison ,VMAT and IMRT treatment plans were respectively designed for every patient with the same dosimetric constraints .Dosimetric comparisons between VMAT and IMRT plans were ana‐lyzed to evaluate :target coverage and homogeneity ,conformity of PTV ;sparing of OARs ;monitor units (MUs) .Results Two treatment plans all reached the treatment need .When compared with IMRT ,there was no significant difference in Dmean of eyeball , len ,optic never ,visual chiasma ,parotid ,brain stem ,and external auditory canal of VMAT (P>0 .05) .The Dmax of eyeball ,len ,pa‐rotid ,and external auditory canal of VMAT were lower than that in IMRT group (P<0 .05) .The VMAT group has the less MUs (P=0 .017) and less treatment time .Conclusion VMAT can reach the big‐dose radiotherapy need on brain metastases clinically . There are no significant diffference between VMAT and IMRT on Dmax ,Dmean ,CI ,and HI .The Dmax of eyeball ,len ,parotid ,and external auditory canal of VMAT were lower than that in IMRT group .The VMAT can reduce the radiotherapy time .