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©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
ARTICLE HIGHLIGHTS
Research background
Radiomics is emerging as a promising tool in oncology, potentially improving, through the development of predictive and prognostic models, the therapeutic decision-making process. To date, however, few data are available regarding the use of radiomics in pancreatic cancer (PC). Since computed tomography (CT) misestimate the resectability of locally advanced PC (LAPC) after neoadjuvant treatment, the role of radiomics could be decisive to integrate traditional morphological parameters in predicting surgical resection.
Research motivation
To explore the potential role of CT-radiomic features to integrate clinical and morphological data to predict surgical resection in LAPC treated with neoadjuvant chemotherapy and radiotherapy.
Research objectives
To create and validate a predictive model to predict LAPC resectability, throughout the application of machine learning algorithms to planning CT-radiomic features.
Research methods
A total of 1655 radiomic features were extracted from planning CT inside the gross tumour volume. Resectability status predictive model was build starting from these radiomic features and clinical data. A first step of variable selection and a training/validation step to find the model that better predicted the outcome was adopted. Subsequently, the validated model was applied to the whole dataset. The discriminating performance of each model was assessed with the area under the receiver operating characteristic curve (AUC).
Research results
Seventy-one LAPC patients were included in the analysis. After neoadjuvant chemotherapy and radiotherapy, 19 (26.8%) patients underwent surgical resection. The training and validation steps resulted in a predictive model of resectability with a median AUC of 0.862 (95%CI: 0.792-0.921) and 0.853 (95%CI: 0.706-0.960), respectively. This model applied to the entire dataset allowed to select 4 radiomic features that predict the respectability status with an AUC of 0.944 (95%CI: 0.892-0.996). No clinical data contributed to the predictive model.
Research conclusions
The present radiomic model could help predict resectability in LAPC treated with neoadjuvant therapy, suggesting a promising role in the context of a complex long-course downstaging and a challenging indication to surgery.
Research perspectives
The analysis of the change of radiomic features during or after treatment (delta radiomics) and the correlation with tumour response (e.g., tumour regression grade) represent another intriguing application of radiomics that needs further exploration.
