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
Abstract
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 dataset and 4 features were selected to build the model with predictive performance as measured using AUC of 0.944 (95%CI: 0.892-0.996).

CONCLUSION

The present radiomic model could help predict resectability in LAPC after neoadjuvant chemotherapy and radiotherapy, potentially integrating clinical and morphological parameters in predicting surgical resection.

Keywords: Computed tomography, Radiomics, Predictive model, Resectability, Locally advanced pancreatic cancer, Radiation oncology

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.