Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Mar 15, 2022; 14(3): 703-715
Published online Mar 15, 2022. doi: 10.4251/wjgo.v14.i3.703
Computed tomography-based radiomic to predict resectability in locally advanced pancreatic cancer treated with chemotherapy and radiotherapy
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
Gabriella Rossi, Nicola Simoni, Roberto Rossi, Martina Venezia, Renzo Mazzarotto, Department of Radiation Oncology, University of Verona Hospital Trust, Verona 37126, Italy
Luisa Altabella, Giulio Benetti, Stefania Guariglia, Carlo Cavedon, Department of Medical Physics, University of Verona Hospital Trust, Verona 37126, Italy
Salvatore Paiella, Giuseppe Malleo, Roberto Salvia, Claudio Bassi, Department of General and Pancreatic Surgery, Pancreas Institute, University of Verona Hospital Trust, Verona 37126, Italy
Author contributions: Rossi G, Altabella L, and Simoni N designed the research; Rossi G, Benetti G, Rossi R, Venezia M, Paiella S, and Malleo G collected data; Rossi G and Simoni N analysed clinical and radiation data; Altabella L and Benetti G performed the radiomic features extraction, machine learning algorithm implementation, and statistical analysis; Rossi G, Altabella L and Simoni N wrote the manuscript; Benetti G, Rossi R, Venezia M, Paiella S, Malleo G, Salvia R, Guariglia S, Bassi C, Cavedon C, and Mazzarotto R reviewed the manuscript; All authors approved the final version of the manuscript.
Institutional review board statement: The Institutional Review Board (IRB) approved the prospective collection of patient data, No. PAD-R n.1101 CESC.
Informed consent statement: All study participants or their legal guardian provided informed written consent about personal and medical data collection prior to study enrolment.
Conflict-of-interest statement: We have no financial relationships to disclose.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Nicola Simoni, MD, Doctor, Department of Radiation Oncology, University of Verona Hospital Trust, Piazzale Stefani 1, Verona 37126, Italy. nicolasimoni81@gmail.com
Received: May 17, 2021
Peer-review started: May 17, 2021
First decision: July 14, 2021
Revised: August 6, 2021
Accepted: February 11, 2022
Article in press: February 11, 2022
Published online: March 15, 2022
Processing time: 296 Days and 20.2 Hours
Core Tip

Core Tip: The present study proposes a computed tomography (CT)-based radiomics model to predict resectability in locally advanced pancreatic cancer (LAPC) treated with intensive chemotherapy followed by ablative radiation therapy. The model was built, tested, and validated in a homogeneous cohort of LAPC patients, using clinical data and radiomic features extracted from the simulation-CT, and showed a reliable performance to predict surgical resection. If further confirmed, the results of this study may allow integrating radiomic information into the pool of clinical and morphological parameters to consider when a LAPC patient is candidate for surgical exploration after neoadjuvant therapy.