基于MRI多病灶生境影像组学预测肝富血供转移瘤的原发灶来源OA

Objective:To develop and validate a multi-lesion habitat radiomics(ML-HR)model based on late arterial phase MRI and evaluate its value in non-invasively predicting the gastrointestinal(GI)versus non-GI origin of hypervascular liver metastases(HLM).Materials and Methods:The clinical and contrast-enhanced MRI Data of 111 HLM patients from two centers were retrospectively included and randomly divided into the training set and the validation set in a 7∶3 ratio.The volume of interest(VOI)of all lesions was delineated on the late-stage arterial images.Local radiomics features were extracted and subregions were divided.Fourteen machine learning algorithms were adopted to respectively construct the traditional single-lesion radiomics(SLR)model,the traditional multi-lesion radiomics(MLR)model and the multi-lesion habitat radiomics(ML-HR)model.To identify whether HLM originates from the GI.The optimal algorithm is screened and the best model is determined through the receiver operating characteristic curve.Results:A total of 111 patients(241 lesions)were included,among which the training set(n=77)and the validation set(n=34)were included.Decision tree(DT),radial basis function support vector machine(rbf_SVM),and eXtreme Gradient Boosting(XGBoost)were identified as the optimal algorithms for SLR,MLR,and ML-HR models,respectively.The ML-HR model has the best performance.The AUC of the training set is 0.952(95％confidence interval:0.904 to 0.988),and that of the validation set is 0.901(95％confidence interval:0.765 to 0.997),which is significantly better than the traditional model(P＜0.05).Conclusions:The ML-HR model can effectively and non-invasively predict the GI versus non-GI origin of HLM,providing a reliable imaging basis for clinical personalized medicine.