摘要
Background:As a systemic disease,pancreatic cancer(PC)can be treated systemically to raise the R0 resection rate and enhance patient prognosis.The best ways to assess the treatment response to systemic treatment of patients with PC are still lacking.Methods:A total of 122 PC patients were enrolled;25 of these patients were used as an independent testing set.According to the pathologic response,PC patients were classified into the responder and nonresponder groups.The whole tumor,core,edge,and peritumoral were segmented from the enhanced computed tomography(CT)images.Machine-learning models were created by extracting the variations in radionics features before and after therapy(delta radiomics features).Finally,we compared the performance of models based on radiomics features,changes in tumor markers,and radiologic evaluation.Results:The model based on the core(area under curve[AUC]=0.864)and edge features(AUC=0.853)showed better performance than that based on the whole tumor(AUC=0.847)or peritumoral area(AUC=0.846).Moreover,the tumor core_edge combination model(AUC=0.899)could better increase confidence in treatment response than using either of them alone.The accuracies of models based on changes in tumor markers and radiologic evaluation were relatively poorer than of the radiomics model.Moreover,Patients predicted to respond to therapy using the radiomics model showed a relatively longer overall survival(43 vs 27 months),although there were no significant differences(P=.063).Conclusions:The tumor core_edge combination delta radiomics model is an effective approach to evaluate pathologic response in PC patients with systemic treatment.
基金
This work is funded by the National Key Research and Development Program(2019YFC1316000 to TBL,2018YFC0116901 to JSL)
Key Research and Development Program of Zhejiang Province(2019C03019 to TBL,2020C03117 to XLB)
National Natural Science Foundation of China(U20A20378 and 81830089 to TBL,81871925,82071867 to XLB,82071916 to XL,82172859 to YWC,12101571 to PJH)
Zhejiang Provincial Traditional Chinese Medicine Key Discipline Project(2017-XK-A38 to TBL)
the Fundamental Research Funds for the Zhejiang Provincial Universities(2021XZZX031 to XLB)
Key Research Project of Zhejiang Laboratory(2022ND0AC01 to JSL).
作者简介
Xiang Li,contributed equally to the writing of the article;Na Lu,contributed equally to the writing of the article;Peijun Hu,contributed equally to the writing of the article;Corresponding author:Tingbo Liang,Department of Hepatobiliary and Pancreatic Surgery,The First Affiliated Hospital,Zhejiang University School of Medicine,No.79 Qingchun Road,Hangzhou 310009,China.E-mail:liangtingbo@zju.edu.cn;Corresponding author:Xueli Bai,e-mail:shirleybai@zju.edu.cn。
