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A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients

PURPOSE: Distant metastasis is the main cause of treatment failure in locally advanced rectal cancer (LARC) patients, despite the recent improvement in treatment strategies. This study aims to evaluate the “delta radiomics” approach in patients undergoing neoadjuvant chemoradiotherapy (nCRT) treated...

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Autores principales: Chiloiro, Giuditta, Boldrini, Luca, Preziosi, Francesco, Cusumano, Davide, Yadav, Poonam, Romano, Angela, Placidi, Lorenzo, Lenkowicz, Jacopo, Dinapoli, Nicola, Bassetti, Michael F., Gambacorta, Maria Antonietta, Valentini, Vincenzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907443/
https://www.ncbi.nlm.nih.gov/pubmed/35280799
http://dx.doi.org/10.3389/fonc.2022.831712
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author Chiloiro, Giuditta
Boldrini, Luca
Preziosi, Francesco
Cusumano, Davide
Yadav, Poonam
Romano, Angela
Placidi, Lorenzo
Lenkowicz, Jacopo
Dinapoli, Nicola
Bassetti, Michael F.
Gambacorta, Maria Antonietta
Valentini, Vincenzo
author_facet Chiloiro, Giuditta
Boldrini, Luca
Preziosi, Francesco
Cusumano, Davide
Yadav, Poonam
Romano, Angela
Placidi, Lorenzo
Lenkowicz, Jacopo
Dinapoli, Nicola
Bassetti, Michael F.
Gambacorta, Maria Antonietta
Valentini, Vincenzo
author_sort Chiloiro, Giuditta
collection PubMed
description PURPOSE: Distant metastasis is the main cause of treatment failure in locally advanced rectal cancer (LARC) patients, despite the recent improvement in treatment strategies. This study aims to evaluate the “delta radiomics” approach in patients undergoing neoadjuvant chemoradiotherapy (nCRT) treated with 0.35-T magnetic resonance-guided radiotherapy (MRgRT), developing a logistic regression model able to predict 2-year disease-free-survival (2yDFS). METHODS: Patients affected by LARC were enrolled in this multi-institutional study. A predictive model of 2yDFS was developed taking into account both clinical and radiomics variables. Gross tumour volume (GTV) was delineated on the magnetic resonance (MR) images acquired during MRgRT, and 1,067 radiomic features (RF) were extracted using the MODDICOM platform. The performance of RF in predicting 2yDFS was investigated in terms of the Wilcoxon–Mann–Whitney test and area under receiver operating characteristic (ROC) curve (AUC). RESULTS: 48 patients have been retrospectively enrolled, with 8 patients (16.7%) developing distant metastases at the 2-year follow-up. A total of 1,099 variables (1,067 RF and 32 clinical variables) were evaluated in two different models: radiomics and radiomics/clinical. The best-performing 2yDFS predictive model was a delta radiomics one, based on the variation in terms of area/surface ratio between biologically effective doses (BED) at 54 Gy and simulation (AUC of 0.92). CONCLUSIONS: The results of this study suggest a promising role of delta radiomics analysis on 0.35-T MR images in predicting 2yDFS for LARC patients. Further analyses including larger cohorts of patients and an external validation are needed to confirm these preliminary results.
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spelling pubmed-89074432022-03-11 A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients Chiloiro, Giuditta Boldrini, Luca Preziosi, Francesco Cusumano, Davide Yadav, Poonam Romano, Angela Placidi, Lorenzo Lenkowicz, Jacopo Dinapoli, Nicola Bassetti, Michael F. Gambacorta, Maria Antonietta Valentini, Vincenzo Front Oncol Oncology PURPOSE: Distant metastasis is the main cause of treatment failure in locally advanced rectal cancer (LARC) patients, despite the recent improvement in treatment strategies. This study aims to evaluate the “delta radiomics” approach in patients undergoing neoadjuvant chemoradiotherapy (nCRT) treated with 0.35-T magnetic resonance-guided radiotherapy (MRgRT), developing a logistic regression model able to predict 2-year disease-free-survival (2yDFS). METHODS: Patients affected by LARC were enrolled in this multi-institutional study. A predictive model of 2yDFS was developed taking into account both clinical and radiomics variables. Gross tumour volume (GTV) was delineated on the magnetic resonance (MR) images acquired during MRgRT, and 1,067 radiomic features (RF) were extracted using the MODDICOM platform. The performance of RF in predicting 2yDFS was investigated in terms of the Wilcoxon–Mann–Whitney test and area under receiver operating characteristic (ROC) curve (AUC). RESULTS: 48 patients have been retrospectively enrolled, with 8 patients (16.7%) developing distant metastases at the 2-year follow-up. A total of 1,099 variables (1,067 RF and 32 clinical variables) were evaluated in two different models: radiomics and radiomics/clinical. The best-performing 2yDFS predictive model was a delta radiomics one, based on the variation in terms of area/surface ratio between biologically effective doses (BED) at 54 Gy and simulation (AUC of 0.92). CONCLUSIONS: The results of this study suggest a promising role of delta radiomics analysis on 0.35-T MR images in predicting 2yDFS for LARC patients. Further analyses including larger cohorts of patients and an external validation are needed to confirm these preliminary results. Frontiers Media S.A. 2022-02-24 /pmc/articles/PMC8907443/ /pubmed/35280799 http://dx.doi.org/10.3389/fonc.2022.831712 Text en Copyright © 2022 Chiloiro, Boldrini, Preziosi, Cusumano, Yadav, Romano, Placidi, Lenkowicz, Dinapoli, Bassetti, Gambacorta and Valentini https://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
Boldrini, Luca
Preziosi, Francesco
Cusumano, Davide
Yadav, Poonam
Romano, Angela
Placidi, Lorenzo
Lenkowicz, Jacopo
Dinapoli, Nicola
Bassetti, Michael F.
Gambacorta, Maria Antonietta
Valentini, Vincenzo
A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients
title A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients
title_full A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients
title_fullStr A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients
title_full_unstemmed A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients
title_short A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients
title_sort predictive model of 2ydfs during mr-guided rt neoadjuvant chemoradiotherapy in locally advanced rectal cancer patients
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907443/
https://www.ncbi.nlm.nih.gov/pubmed/35280799
http://dx.doi.org/10.3389/fonc.2022.831712
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