The clinical value of distinguishing pathological subtypes of lung adenocarcinoma based on Nomogram maps

WANG Zhaohui; YUE Junyan

Abstract：Objective To explore the value of constructing a Nomogram model based on LASSO regression analysis for pre-dicting adenocarcinoma in situ(AIS),minimally invasive adenocarcinoma(MIA),and invasive adenocarcinoma(IAC).Meth-ods In the First Affiliated Hospital of Xinxiang Medical University,Henan Province,97 patients with adenocarcinoma of the lung confirmed by surgery and pathology and with definite pathological subtype were retrospectively analyzed.AIS and MIA were classified as group one,IAC as group two.The differences of clinical medical characteristics between the two groups,such as age,gender,smoking history,long diameter,short diameter and immunohistochemical Ki-67,were compared.The characteristics were extracted by 3Dslicer software.By using the LASSO algorithm to reduce the dimensionality of features and to filter out imag-ing omics features,a prediction model was constructed.Then,the rms toolkit of R software was used to draw the nomogram and to calculate the area under the curve(AUC)of the receiver operating characteristic to evaluate the efficacy of the nomogram in identi-fying the pathological subtypes of ground glass nodules of the lung.Results 1)Clinical medical characteristics such as gender,smoking history,history of primary tumors,long and short diameters,and immunohistochemical Ki-67 were not statistically sig-nificant(P>0.05);2)This study screened 7 CT imaging omics features in terms of original_shape_Flatness,original_gldm_Large Dependence Low Gray Level Emphasis,wavelet LHL_first order_10 Percentile,wavelet HLL_first order_10 Percentile,wavelet HLL_first order_Minimum,wavelet HHL_firstorder_Mean,and wavelet HHH_gldm_Small Dependence Low Gray Level Empha-sis.Based on this,a predictive model for differentiating pathological subtypes of pulmonary ground glass nodules was established(P>0.05);and 3)According to the nomogram of predicting pathological subtypes of pulmonary ground glass nodules based on the characteristics of CT imaging,the AUC in the training set was 0.863,the accuracy was 87.9%,the sensitivity was 67.9%,and the specificity was 91.1%.The validation set AUC was 0.792,with an accuracy of 75.0%,sensitivity of 66.7%,and specificity of 90.5%,indicating that this column chart had good predictive performance.Conclusion Nomogram model has obvious advan-tages in predicting the invasion degree of adenocarcinoma of the lung and can be used as a means of differentiation.