ABSTRACT
Objective:To investigate the prognostic value of baseline peripheral blood inflammatory biomarkers for prognosis in patients with advanced hepatocellular carcinoma (HCC) receiving immunotherapy combined with targeted therapy.Methods:The clinical data of a total of 120 patients with advanced HCC who received immunotherapy combined with targeted therapy at Cancer Center of Renmin Hospital of Wuhan University from December 2019 to March 2022 were analyzed retrospectively. Receiver operating characteristic (ROC) curve was used to calculate the optimal cut-off values of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune inflammation index (SII) and prognostic nutritional index (PNI). According to the optimal cut-off values, the study objects were divided into high value group and low value group. The Kaplan-Meier method was used for survival analysis. Cox proportional hazard regression model was applied to analyze the factors associated with prognosis.Results:By the end of follow-up, 74 patients died and 46 survived. The median follow-up time was 23.0 months, the median overall survival (mOS) was 15.6 months, and the median progression-free survival (mPFS) was 13.1 months. ROC curve analysis showed that the optimal cut-off values of NLR, PLR, SII, LMR and PNI were 3.45, 131.87, 626.21, 2.12 and 43.30, respectively. The mPFS (18.3 months vs. 8.7 months) and mOS (26.6 months vs. 10.9 months) of patients in the low-NLR group ( n=75) were longer than those of the high-NLR group ( n=45), and there were statistically significant differences ( χ2=55.64, P<0.001; χ2=64.14, P<0.001). The mPFS (17.9 months vs. 10.9 months) and mOS (24.5 months vs. 13.5 months) of patients in the low-PLR group ( n=55) were longer than those of the high-PLR group ( n=65), and there were statistically significant differences ( χ2=5.27, P=0.023; χ2=11.84, P<0.001). The mPFS (18.0 months vs. 10.7 months) and mOS (25.7 months vs. 12.8 months) of patients in the low-SII group ( n=75) were longer than those of the high-SII group ( n=45), and there were statistically significant differences ( χ2=24.46, P<0.001; χ2=25.42, P<0.001). The mPFS (18.2 months vs. 10.9 months) and mOS (26.6 months vs. 13.2 months) of patients in the high-LMR group ( n=56) were longer than those of the low-LMR group ( n=64), and there were statistically significant differences ( χ2=19.25, P<0.001; χ2=19.92, P<0.001). The mPFS (17.9 months vs. 10.9 months) and mOS (25.4 months vs. 13.4 months) of patients in the high-PNI group ( n=62) were longer than those of the low-PNI group ( n=58), and there were statistically significant differences ( χ2=13.69, P<0.001; χ2=19.07, P<0.001). Univariate analysis showed that Barcelona clinic liver cancer (BCLC) stage ( HR=1.83, 95% CI: 1.17-2.87, P=0.008), Child-Pugh grade ( HR=2.21, 95% CI: 1.47-3.34, P<0.001), modified albumin-bilirubin (mALBI) grade ( HR=1.35, 95% CI: 1.01-1.81, P=0.045), extrahepatic metastases ( HR=2.18, 95% CI: 1.47-3.25, P<0.001), NLR ( HR=1.40, 95% CI: 1.28-1.54, P<0.001), PLR ( HR=1.00, 95% CI: 1.00-1.01, P=0.001), SII ( HR=1.00, 95% CI: 1.00-1.00, P<0.001), LMR ( HR=0.64, 95% CI: 0.51-0.79, P<0.001) and PNI ( HR=0.95, 95% CI: 0.93-0.98, P=0.001) were correlated with PFS; BCLC stage ( HR=2.18, 95% CI: 1.21-3.91, P=0.009), Child-Pugh grade ( HR=2.57, 95% CI: 1.61-4.09, P<0.001), Eastern Cooperative Oncology Group performance status score ( HR=1.59, 95% CI: 1.01-2.51, P=0.044), mALBI grade ( HR=1.60, 95% CI: 1.17-2.17, P=0.003), extrahepatic metastasis ( HR=2.51, 95% CI: 1.59-3.96, P<0.001), NLR ( HR=1.45, 95% CI: 1.32-1.60, P<0.001), PLR ( HR=1.01, 95% CI: 1.01-1.01, P<0.001), SII ( HR=1.01, 95% CI: 1.01-1.01, P<0.001), LMR ( HR=0.57, 95% CI: 0.40-0.72, P<0.001) and PNI ( HR=0.92, 95% CI: 0.89-0.96, P<0.001) were correlated with OS. Multivariate analysis showed that extrahepatic metastasis ( HR=1.78, 95% CI: 1.10-2.87, P=0.018) and NLR ( HR=1.46, 95% CI: 1.24-1.73, P<0.001) were independent influencing factors for PFS; extrahepatic metastasis ( HR=2.09, 95% CI: 1.21-3.61, P=0.009), NLR ( HR=1.56, 95% CI: 1.29-1.88, P<0.001), SII ( HR=1.00, 95% CI: 1.00-1.00, P=0.025), LMR ( HR=0.59, 95% CI: 0.45-0.78, P=0.008) and PNI ( HR=0.93, 95% CI: 0.88-0.99, P=0.013) were independent influencing factors for OS. Conclusion:NLR and extrahepatic metastasis can be regarded as important indicators to predict PFS in patients with advanced HCC receiving immunotherapy combined with targeted therapy, and NLR, SII, LMR, PNI and extrahepatic metastasis can be regarded as important indicators to predict OS in patients with advanced HCC receiving immunotherapy combined with targeted therapy. High NLR, high SII, low LMR, low PNI and extrahepatic metastasis indicate poor prognosis of HCC patients.
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Objective To evaluate the application value of an intelligent fundus assisted diagnosis system for detecting retinopathy of prematurity ( ROP) based on deep learning. Methods A total of 38895 fundus images for premature infants screening were collected from Renmin Hospital of Wuhan University Eye Center and were labeled by 10 licensed ophthalmologists. A deep learning network model was established to acquire automatic classification of disease stages and plus disease. The accuracy,sensitivity and specificity of the algorithm were calculated to evaluate the performance of the artificial intelligence system for ROP automatic diagnosis. This study protocol was approved by Ethic Committee of Renmin Hospital of Wuhan University ( No. WDRY2019-K032 ) . Written informed consent was obtained from the guardians of the children before entering the study cohort. Results The intelligent system achieved an accuracy of 0. 931. Specifically,the accuracies in detecting demarcation line (stageⅠ) was 0. 876,ridge (stage Ⅱ) was 0. 942,ridge with extra retinal fibrovascular (stageⅢ) was 0. 968,subtotal retinal detachment (stageⅣ) was 0. 998,total retinal detachment (stage Ⅴ) was 0. 999,vascular tortuosity and dilatation (plus disease) was 0. 896,optic disc was 0. 954,macular was 0. 781,and laser scars were 0. 974,respectively. Conclusions Deep learning algorithm can detect the stages and plus disease of ROP with excellent accuracy,and it provides the feasibility of applying the algorithm for ROP automated screening in clinical.
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Background Choroidal thickness is closely associated with ametropia,and to study the influence of subfoveal choroidal thickness (SFCT) on refraction is very important for understanding the mechanism of refractive error more clearly.Objective This study was to investigate the relationship between SFCT and refraction.Methods A retrospective serial cases analysis was performed.Forty anisometrope patients were recruited in Zhongshan Ophthalmic Center from June 2012 to August 2012.The subjects were divided into 6-13 years group and 14-21 years group.Vision acuity was tested by the EDTRS visual acuity chart and ocular anterior segment was examined under the slit lamp microscope,and the fundus examination was performed by direct ophthalmoscope.Subjective and objective optometry was performed after pupils were dilated.EDI OCT was used to illuminate choroidal image.Ocular axial length (AL) was obtained by Lenstar 900.The difference in SFCT between myopic eyes and hyperopic eyes was compared using Student t test,and the correlations between SFCT and refraction were analyzed by Pearson linear analysis and linear regression analysis.Results The average SFCT was (307.82±88.47) μm in all the tested eyes,and the SFCT of myopic eyes and hyperopic eyes was (270.60±70.57) μm and (376.95±76.59) μm,respectively,with a significant difference between them (P =0.000).In the 6-13 years group,positive correlations were found between SFCT and diopters with the regression coefficient 18.60 and regression equation Y =18.60X +310.79 (r=0.345,F=21.110,P=0.000) as well as between SFCT and AL with the regression coefficient -31.76 and regression equation Y =-31.76X+1 039.97 (r=0.262,F=17.320,P=0.000).In the 14-21 years group,SFCT showed positive correlation with diopters,with the regression coefficient 23.38 and regression equation Y=23.38X+353.17 (r =0.430,F =27.210,P =0.000) and negative correlation with AL,with the regression coefficient-35.82 and regression equation Y =-35.82X+1 132.75 (r=0.237,F=15.650,P=0.000).Conclusions SFCT seems to be positive correlated with diopter.When the diopter shifts toward positive value,SFCT value increases,and whenever diopter increases-1 D,SFCT decreases 20 μm approximately.SFCT decreases with the increase of AL.