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©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
Radiomics in colorectal cancer patients
Riccardo Inchingolo, Cesare Maino, Roberto Cannella, Federica Vernuccio, Francesco Cortese, Michele Dezio, Antonio Rosario Pisani, Teresa Giandola, Marco Gatti, Valentina Giannini, Davide Ippolito, Riccardo Faletti
Riccardo Inchingolo, Francesco Cortese, Michele Dezio, Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
Cesare Maino, Teresa Giandola, Davide Ippolito, Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
Roberto Cannella, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
Federica Vernuccio, Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
Antonio Rosario Pisani, Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
Marco Gatti, Valentina Giannini, Riccardo Faletti, Department of Surgical Sciences, University of Turin, Turin 10126, Italy
Author contributions: Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, and Faletti R equally contributed to this paper with conception and design of the study, literature review and analysis, drafting and critical revision and editing; and all authors gave final approval of the final version.
Conflict-of-interest statement: All the authors are aware of the content of the manuscript and have no conflict of interest.
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:
https://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Riccardo Inchingolo, MD, Director, Doctor, Unit of Interventional Radiology, F. Miulli Hospital, Sp per Santeramo, Acquaviva delle Fonti 70021, Italy. riccardoin@hotmail.it
Received: February 16, 2023
Peer-review started: February 16, 2023
First decision: March 24, 2023
Revised: April 7, 2023
Accepted: April 25, 2023
Article in press: April 25, 2023
Published online: May 21, 2023
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named “radiomics”. Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
Core Tip: Stratifying colorectal cancer patients with high-risk disease and the evaluation of the overall chemotherapy benefit are a clinical challenge. Radiomics through radiological images analysis using automated computer-based techniques allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT), magnetic resonance imaging, and positron emission tomography/CT, in particular to understand tumor biology, to develop imaging biomarkers for diagnosis, staging, and prognosis, to predict treatment response and to monitor disease.
