A Multicenter Study Of Accurate Preoperative Diagnosis Of Primary Hepatocellular Carcinoma Using Ultrasomics

Clinical value of machine learning-based ultrasomics in preoperative differentiation between hepatocellular carcinoma and intrahepatic cholangiocarcinoma: A multi-center studyObjective: To explore the clinical value of machine learning-based ultrasomics in the preoperative noninvasive differentiation between hepatocellular carcinoma(HCC)and intrahepatic cholangiocarcinoma(ICC).Methods: Clinical data and ultrasound images of 226 patients from three hospitals were retrospectively collected and eventually included,divided into training set(n=149)test set(n=38)and independent validation set(n=39).Manual segmentation of tumor lesion was performed with ITK-SNAP,the ultrasomics features were extracted by the Pyradiomics,and ultrasomics signatures were generated using variance filtering and lasso regression.The prediction models for preoperative differentiation between HCC and ICC were established by using support vector machine(SVM).The performance of the three models was evaluated by the area under curve(AUC),sensitivity,specificity and accuracy.Results: The ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between HCC and ICC(p < 0.05).The combined model had a better performance than either the clinical model or the ultrasomics model.In addition to stability,the combined model also had a stronger generalization ability(p < 0.05).The AUC(along with 95% CI)of the combined model on test set and the independent validation set was 0.936(0.806-0.989)and 0.874(0.733-0.961),respectively.Conclusion: The ultrasomics signatures could facilitate the preoperative noninvasive differentiation between HCC and ICC.The combined model integrating ultrasomics signatures and clinical features had a higher clinical value and a stronger generalization ability.Preoperative prediction of pathological grading of hepatocellular carcinoma using machine learning-based ultrasomics: A multicenter studyPurpose: The present study investigated the value of ultrasomics signatures in the preoperative prediction of the pathological grading of hepatocellular carcinoma(HCC)via machine learning.Methods: A total of 193 patients with HCC diagnosed by pathology from three hospitals were collected and finally included as the research object.The patients from two hospitals(n=160)were randomly divided into training set(n=128)and test set(n=32)at a 8:2 ratio.The patients from a third hospital were used as an independent validation set(n=33).The ultrasomics features were extracted from the tumor lesions on the ultrasound images.Support vector machine(SVM)was used to construct three preoperative pathological grading models for HCC on each dataset.The performance of the three models was evaluated by area under the receiver operating characteristic curve(AUC),sensitivity,specificity,and accuracy.Results: The ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between high-and low-grade HCC lesions on the training set,test set,and the independent validation set(p<0.05).On the test set and the validation set,the combined model’s performance was the highest,followed by the ultrasomics model and the clinical model successively(p<0.05).Their AUC(along with 95%CI)of these models was 0.874(0.709-0.964),0.789(0.608-0.912),0.720(0.534-0.863)and 0.849(0.682-0.949),0.825(0.654-0.935),0.770(0.591-0.898),respectively.Conclusion: Machine learning-based ultrasomics signatures could be used for noninvasive preoperative prediction of pathological grading of HCC.The combined model displayed a better predictive performance for pathological grading of HCC and had a stronger generalization ability.