Value of joint model based on Gd-EOB-DTPA-enhanced MRI radiomics and clinical characteristics to the prediction of malignant cirrhotic nodules
Objective To analyze the clinical characteristics and radiomics features of Gd-EOB-DTPA-enhanced MRI scanning in patients with cirrhotic nodules,and to construct a joint predictive model for the malignant cirrhotic nodules in order to investigate its predictive value.Methods From April 2021 to April 2023,140 patients with cirrhotic nodules underwent Gd-EOB-DTPA-enhanced MRI scanning in Xinxiang Central Hospital,and were randomly divided into training set(n=98)and testing set(n=42)in a 7∶3 ratio.All patients underwent surgical tissue or biopsy tissue pathological examinations to determine the benign and malignant natures of the nodules.There were 15 patients with malignant nodules and 27 patients with benign nodules in the testing set.The training set was divided into malignant nodules group(n=33)and benign nodules group(n=65).The percentages of patients with age≥60 years,smoking habits,alcohol consumption history,family history of liver cancer,fatty liver and diabetes,male ratio,liver hardness value,body mass index,and laboratory indexes one week before histopathological examination including platelet count,serum albumin,prothrombin time,glutamic-pyruvic transaminase,glutamic-oxaloacetic transaminase,total bilirubin,alpha fetoprotein,and HBV-DNA positive rate were compared between the training set and the testing set,and between malignant nodules group and benign nodules group.Multivariate logistic regression analysis was done to assess the clinical influencing factors of malignant cirrhotic nodules,and to construct a clinical predictive model.Pyradiomics software was used to extract quantitative features from liver and gallbladder MRI images in the training set,and 1 423 radiomics features were extracted for each patient.Totally 823 features were removed through repeatability analysis,and the feature variables were screened in the remaining 600 image features by a 10-fold cross validation lasso regression algorithm.Multivariate logistic regression analysis was used to construct a radiomics predictive model for the malignant cirrhotic nodules,and the radiomics score was calculated.MRI radiomics scores and clinical features were integrated into multivariate logistic regression analysis to construct a joint predictive model.ROC curves were plotted to evaluate the efficiencies of clinical predictive model,radiomics predictive model,and jointed predictive model on predicting malignant nodules in the testing set.The calibration curves and clinical decision curves were used to evaluate the values of three models to the prediction of malignant nodules in the testing set.Results(1)There were no significant differences in the percentages of patients with age ≥60 years,smoking habits,alcohol consumption history,family history of liver cancer,fatty liver and diabetes,male ratio,HBV-DNA positive rate,liver hardness value,body mass index,platelet count,serum albumin,prothrombin time,glutamic-pyruvic transaminase,glutamic-oxaloacetic transaminase,total bilirubin and alpha fetoprotein between the training set and the testing set(P＞0.05).(2)The percentages of patients with age ≥60 years,alcohol consumption history and family history of liver cancer,male ratio,HBV-DNA positive rate,liver hardness value,serum albumin,and alpha fetoprotein levels were higher in malignant nodules group than those in benign nodules group(P＜0.05),and there were no significant differences in the percentages of patients with smoking habits,fatty liver and diabetes,body mass index,total bilirubin,glutamic-pyruvic transaminase,glutamic-oxaloacetic transaminase,platelet count and prothrombin time between two groups(P＞0.05).Age(OR=2.993,95％CI:1.681-3.341,P＜0.001),gender(OR=2.223,95％CI:1.569-3.867,P=0.002),alcohol consumption history(OR=1.298,95％CI:1.005-1.977,P=0.016),family history of liver cancer(OR=1.236,95％CI:1.005-2.112,P=0.021),and HBV-DNA(OR=3.032,95％CI:1.005-4.968,P＜0.001)were the influencing factors of malignant nodules.The clinical predictive model=1.256+1.096X Age+0.799X Gender+0.261 X Alcohol consumption history+0.212 X Family history of liver cancer+1.109X HBV-DNA.(3)The lasso regression screening results showed that when the optimal λ was 0.056,orig_shape_MALs,waves_LHH_GLSZM_GLNU,wave_HHH_GLDM_DE,wave_HLL_FO_Minimum,and wave_HHH_FO_TE were the five feature variables with the most generalization ability.orig_shape_MALs(OR=4.101,95％CI:2.321-6.297,P=0.023),waves LHH GLSZM GLNU(OR=3.568,95％CI:1.863-4.448,P=0.001),waveHHHGLDMDE(OR=2.512,95％CI:1.278-4.006,P=0.014),wave_HLL_FO_Minimum(OR=2.115,95％CI:1.119-3.238,P=0.018),and wave_HHH_FO_TE(OR=3.205,95％CI:2.009-4.317,P=0.025)were the influencing factors of malignant nodules.The radiomics score=1.360+1.411 × orig_shape_MAL+1.272 × wave_LHH_GLSZM_GLNU+0.921 × wave_HHH_GLDM_DE+0.749 × wave_HLL_FO_Minimum+1.165 × wave_HHH_FO_TE.(4)The joint predictive model=1.943+1.016 × Age+0.993 × Gender+0.310 × Alcohol consumption history+0.257 X Family history of liver cancer+1.244 × HBV-DNA+1.280 × radiomics score.(5)When the optimal cut-off values of clinical predictive model,radiomics predictive model and joint predictive model were 5.689,4.339 and 4.727,the AUCs for predicting the malignant nodules in the testing set were 0.711(95％CI:0.532-0.866,P＜0.001),0.836(95％CI:0.665-0.956,P＜0.001)and 0.937(95％CI:0.851-0.993,P＜0.001),respectively.The AUC of joint predictive model in the testing set was greater than that of radiomics predictive model and clinical predictive model(Z=2.374,P＜0.001;Z=2.124,P＜0.001),and the AUC of radiomics predictive model was greater than that of clinical predictive model(Z=1.964,P＜0.001).The calibration curve showed that the calibration curves of the clinical predictive model,radiomics predictive model,and joint predictive model in the testing set were consistent with the ideal curves.The decision curve showed that when the threshold probability was greater than 20％,the net profit of joint predictive model was higher than that of radiomics predictive model and clinical predictive model in the testing set,and the net profit of radiomics predictive model was higher than that of clinical predictive model.Conclusion The predictive model constructed by Gd-EOB-DTPA-enhanced MRI radiomics score combined with clinical characteristics has a high predictive value for malignant cirrhotic nodules.
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万方数据
