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Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure
PURPOSE: Distant metastases are currently the main cause of treatment failure in locally advanced rectal cancer (LARC) patients. The aim of this research is to investigate a correlation between the variation of radiomics features using pre- and post-neoadjuvant chemoradiation (nCRT) magnetic resonan...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744725/ https://www.ncbi.nlm.nih.gov/pubmed/33344243 http://dx.doi.org/10.3389/fonc.2020.595012 |
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author | Chiloiro, Giuditta Rodriguez-Carnero, Pablo Lenkowicz, Jacopo Casà, Calogero Masciocchi, Carlotta Boldrini, Luca Cusumano, Davide Dinapoli, Nicola Meldolesi, Elisa Carano, Davide Damiani, Andrea Barbaro, Brunella Manfredi, Riccardo Valentini, Vincenzo Gambacorta, Maria Antonietta |
author_facet | Chiloiro, Giuditta Rodriguez-Carnero, Pablo Lenkowicz, Jacopo Casà, Calogero Masciocchi, Carlotta Boldrini, Luca Cusumano, Davide Dinapoli, Nicola Meldolesi, Elisa Carano, Davide Damiani, Andrea Barbaro, Brunella Manfredi, Riccardo Valentini, Vincenzo Gambacorta, Maria Antonietta |
author_sort | Chiloiro, Giuditta |
collection | PubMed |
description | PURPOSE: Distant metastases are currently the main cause of treatment failure in locally advanced rectal cancer (LARC) patients. The aim of this research is to investigate a correlation between the variation of radiomics features using pre- and post-neoadjuvant chemoradiation (nCRT) magnetic resonance imaging (MRI) with 2 years distant metastasis (2yDM) rate in LARC patients. METHODS AND MATERIALS: Diagnostic pre- and post- nCRT MRI of LARC patients, treated in a single institution from May 2008 to June 2015 with an adequate follow-up time, were retrospectively collected. Gross tumor volumes (GTV) were contoured by an abdominal radiologist and blindly reviewed by a radiation oncologist expert in rectal cancer. The dataset was firstly randomly split into 90% training data, for features selection, and 10% testing data, for the validation. The final set of features after the selection was used to train 15 different classifiers using accuracy as target metric. The models’ performance was then assessed on the testing data and the best performing classifier was then selected, maximising the confusion matrix balanced accuracy (BA). RESULTS: Data regarding 213 LARC patients (36% female, 64% male) were collected. Overall 2yDM was 17%. A total of 2,606 features extracted from the pre- and post- nCRT GTV were tested and 4 features were selected after features selection process. Among the 15 tested classifiers, logistic regression proved to be the best performing one with a testing set BA, sensitivity and specificity of 78.5%, 71.4% and 85.7%, respectively. CONCLUSIONS: This study supports a possible role of delta radiomics in predicting following occurrence of distant metastasis. Further studies including a consistent external validation are needed to confirm these results and allows to translate radiomics model in clinical practice. Future integration with clinical and molecular data will be mandatory to fully personalized treatment and follow-up approaches. |
format | Online Article Text |
id | pubmed-7744725 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77447252020-12-18 Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure Chiloiro, Giuditta Rodriguez-Carnero, Pablo Lenkowicz, Jacopo Casà, Calogero Masciocchi, Carlotta Boldrini, Luca Cusumano, Davide Dinapoli, Nicola Meldolesi, Elisa Carano, Davide Damiani, Andrea Barbaro, Brunella Manfredi, Riccardo Valentini, Vincenzo Gambacorta, Maria Antonietta Front Oncol Oncology PURPOSE: Distant metastases are currently the main cause of treatment failure in locally advanced rectal cancer (LARC) patients. The aim of this research is to investigate a correlation between the variation of radiomics features using pre- and post-neoadjuvant chemoradiation (nCRT) magnetic resonance imaging (MRI) with 2 years distant metastasis (2yDM) rate in LARC patients. METHODS AND MATERIALS: Diagnostic pre- and post- nCRT MRI of LARC patients, treated in a single institution from May 2008 to June 2015 with an adequate follow-up time, were retrospectively collected. Gross tumor volumes (GTV) were contoured by an abdominal radiologist and blindly reviewed by a radiation oncologist expert in rectal cancer. The dataset was firstly randomly split into 90% training data, for features selection, and 10% testing data, for the validation. The final set of features after the selection was used to train 15 different classifiers using accuracy as target metric. The models’ performance was then assessed on the testing data and the best performing classifier was then selected, maximising the confusion matrix balanced accuracy (BA). RESULTS: Data regarding 213 LARC patients (36% female, 64% male) were collected. Overall 2yDM was 17%. A total of 2,606 features extracted from the pre- and post- nCRT GTV were tested and 4 features were selected after features selection process. Among the 15 tested classifiers, logistic regression proved to be the best performing one with a testing set BA, sensitivity and specificity of 78.5%, 71.4% and 85.7%, respectively. CONCLUSIONS: This study supports a possible role of delta radiomics in predicting following occurrence of distant metastasis. Further studies including a consistent external validation are needed to confirm these results and allows to translate radiomics model in clinical practice. Future integration with clinical and molecular data will be mandatory to fully personalized treatment and follow-up approaches. Frontiers Media S.A. 2020-12-03 /pmc/articles/PMC7744725/ /pubmed/33344243 http://dx.doi.org/10.3389/fonc.2020.595012 Text en Copyright © 2020 Chiloiro, Rodriguez-Carnero, Lenkowicz, Casà, Masciocchi, Boldrini, Cusumano, Dinapoli, Meldolesi, Carano, Damiani, Barbaro, Manfredi, Valentini and Gambacorta http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Chiloiro, Giuditta Rodriguez-Carnero, Pablo Lenkowicz, Jacopo Casà, Calogero Masciocchi, Carlotta Boldrini, Luca Cusumano, Davide Dinapoli, Nicola Meldolesi, Elisa Carano, Davide Damiani, Andrea Barbaro, Brunella Manfredi, Riccardo Valentini, Vincenzo Gambacorta, Maria Antonietta Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure |
title | Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure |
title_full | Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure |
title_fullStr | Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure |
title_full_unstemmed | Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure |
title_short | Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure |
title_sort | delta radiomics can predict distant metastasis in locally advanced rectal cancer: the challenge to personalize the cure |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744725/ https://www.ncbi.nlm.nih.gov/pubmed/33344243 http://dx.doi.org/10.3389/fonc.2020.595012 |
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