Prediction Of The Gallbladder Cancer Pathological Type And The Serosal Layer Involvement With Machine Learning Model Of Enhanced CT Radiomics

Purpose:Gallbladder cancer is a malignant tumor with a low incidence but a very poor prognosis.Early diagnosis of the tumor and accurate assessment of the tumor are of great significance for improving the prognosis of patients.However,the early diagnosis and tumor evaluation based on traditional imaging examinations have been very difficult.As a recently emerging research method,radiomics has played an excellent diagnostic effect in lung cancer,breast cancer,glioma and other tumor diseases,such as predicting pathological types,tumor conditions,patient prognosis,tumors Genotype even the effect of drug treatment.At present,there is no research using radiomics to predict the pathological type and serosal infiltration of gallbladder cancer.Therefore,this research plans to analyze enhanced CT radiomics feature of gallbladder cancer and construct a machine learning model to determine the pathological type and serous membrane involvement.Methods:This study is a retrospective study.A total of 80 patients diagnosed as gallbladder cancer by paraffin pathology after surgery were enrolled,and the preoperative enhanced CT images and pathological results were collected.After preprocessing the image data,the radiologist delineated the ROI.The radiomics feature parameters is extracted by Python.Grouping by pathological types,Kluvoskar-Wallis test was used to determine the parameters with significant differences.After that,based on the involvement of the serosal layer,the Mann-Whitney U test was used to determine the parameters related to the serous layer.All the different parameters were integrated and the Tensorflow linear structure was used to build a 3-layer neural network model with 20 neural nodes in each layer.It was the standard of the well-trained model that the area under the curve of the training set and the validation set was stable and higher than 0.75.Finally,a randomly selected validation set is used to evaluate the effect of the model.Results:This study found a total of 127 radiomics parameters with significant differences(p<0.05)between different pathological types.The difference parameters were mainly distributed in the shape feature group(41)and the texture feature group(75).After pairwise comparison between different pathological types,it is found that the difference in radiomics parameters mainly comes from well-differentiated and poorly differentiated adenocarcinoma(67),well-differentiated adenocarcinoma and moderately-poorly differentiated adenocarcinoma(77).Using the above difference parameters,a machine learning model can be constructed to distinguish well-differentiated adenocarcinoma,poorly-differentiated adenocarcinoma and other adenocarcinomas,and the area under the curve can be stabilized above 0.8.There are a total of 75 radiomic parameters with significant differences(p<0.05)between the involvement of the serosal layer or not.Using these difference parameters,a model for predicting the involvement of the serosal layer can be constructed,and the area under the curve can be stabilized above 0.75.Conclusion:There are significant differences in radiomics characteristic parameters of enhanced CT among different pathological types of gallbladder cancer and different serosal layer involvement.Based on these difference parameters,a machine learning model of enhanced CT radiomics can be constructed to distinguish the pathological type of gallbladder cancer and the involvement of the serosal layer.