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

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World Journal of Gastrointestinal Oncology 2022年第3期14卷 703-715页

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 Department of Radiation Oncology University of Verona Hospital TrustVerona 37126Italy Department of Medical Physics University of Verona Hospital TrustVerona 37126Italy Department of General and Pancreatic Surgery Pancreas InstituteUniversity of Verona Hospital TrustVerona 37126Italy

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 *** 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 *** To assess the capability of radiomic features to predict surgical resection in LAPC treated with neoadjuvant chemotherapy and *** Patients with LAPC treated with intensive chemotherapy followed by ablative radiation therapy were retrospectively *** thousand six hundred and fifty-five radiomic features were extracted from planning CT inside the gross tumour *** extracted features and clinical data contribute to create and validate the predictive model of resectability *** were repeatedly divided into training and validation *** 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 *** Seventy-one patients were included in the *** age was 65 years and 57.8%of patients were *** patients underwent induction chemotherapy followed by ablative radiotherapy,and 19(26.8%)ultimately received surgical *** 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),respectivel

关键词： Computed tomography Radiomics Predictive model Resectability Locally advanced pancreatic cancer Radiation oncology

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