Computed tomography-based radiomic to predict resectability in locally advanced pancreatic cancer treated with chemotherapy and radiotherapy  

作　　者：Gabriella Rossi Luisa Altabella Nicola Simoni Giulio Benetti Roberto Rossi Martina Venezia Salvatore Paiella Giuseppe Malleo Roberto Salvia Stefania Guariglia Claudio Bassi Carlo Cavedon Renzo Mazzarotto 

机构地区：[1]Department of Radiation Oncology,University of Verona Hospital Trust,Verona 37126,Italy [2]Department of Medical Physics,University of Verona Hospital Trust,Verona 37126,Italy [3]Department of General and Pancreatic Surgery,Pancreas Institute,University of Verona Hospital Trust,Verona 37126,Italy

出　　处：《World Journal of Gastrointestinal Oncology》2022年第3期703-715,共13页世界胃肠肿瘤学杂志（英文版）（电子版）

摘　　要：BACKGROUND Surgical resection after neoadjuvant treatment is the main driver for improved survival in locally advanced pancreatic cancer(LAPC).However,the diagnostic performance of computed tomography(CT)imaging to evaluate the residual tumour burden at restaging after neoadjuvant therapy is low due to the difficulty in distinguishing neoplastic tissue from fibrous scar or inflammation.In this context,radiomics has gained popularity over conventional imaging as a complementary clinical tool capable of providing additional,unprecedented information regarding the intratumor heterogeneity and the residual neoplastic tissue,potentially serving in the therapeutic decision-making process.AIM To assess the capability of radiomic features to predict surgical resection in LAPC treated with neoadjuvant chemotherapy and radiotherapy.METHODS Patients with LAPC treated with intensive chemotherapy followed by ablative radiation therapy were retrospectively reviewed.One thousand six hundred and fifty-five radiomic features were extracted from planning CT inside the gross tumour volume.Both extracted features and clinical data contribute to create and validate the predictive model of resectability status.Patients were repeatedly divided into training and validation sets.The discriminating performance of each model,obtained applying a LASSO regression analysis,was assessed with the area under the receiver operating characteristic curve(AUC).The validated model was applied to the entire dataset to obtain the most significant features.RESULTS Seventy-one patients were included in the analysis.Median age was 65 years and 57.8%of patients were male.All patients underwent induction chemotherapy followed by ablative radiotherapy,and 19(26.8%)ultimately received surgical resection.After the first step of variable selections,a predictive model of resectability was developed with a median AUC for training and validation sets of 0.862(95%CI:0.792-0.921)and 0.853(95%CI:0.706-0.960),respectively.The validated model was applied to the entire

关 键 词：Computed tomography Radiomics Predictive model RESECTABILITY Locally advanced pancreatic cancer Radiation oncology 

分 类 号：R735.9[医药卫生—肿瘤]