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Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy

Background: Response to induction chemotherapy (IC) has been predicted in patients with sinonasal cancer using early delta radiomics obtained from T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps, comparing results with early radiological evaluation by RECIST. Methods: Fifty...

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Autores principales: Corino, Valentina D. A., Bologna, Marco, Calareso, Giuseppina, Resteghini, Carlo, Sdao, Silvana, Orlandi, Ester, Licitra, Lisa, Mainardi, Luca, Bossi, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8877083/
https://www.ncbi.nlm.nih.gov/pubmed/35200748
http://dx.doi.org/10.3390/jimaging8020046
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author Corino, Valentina D. A.
Bologna, Marco
Calareso, Giuseppina
Resteghini, Carlo
Sdao, Silvana
Orlandi, Ester
Licitra, Lisa
Mainardi, Luca
Bossi, Paolo
author_facet Corino, Valentina D. A.
Bologna, Marco
Calareso, Giuseppina
Resteghini, Carlo
Sdao, Silvana
Orlandi, Ester
Licitra, Lisa
Mainardi, Luca
Bossi, Paolo
author_sort Corino, Valentina D. A.
collection PubMed
description Background: Response to induction chemotherapy (IC) has been predicted in patients with sinonasal cancer using early delta radiomics obtained from T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps, comparing results with early radiological evaluation by RECIST. Methods: Fifty patients were included in the study. For each image (at baseline and after the first IC cycle), 536 radiomic features were extracted as follows: semi-supervised principal component analysis components, explaining 97% of the variance, were used together with a support vector machine (SVM) to develop a radiomic signature. One signature was developed for each sequence (T1-, T2-weighted and ADC). A multiagent decision-making algorithm was used to merge multiple signatures into one score. Results: The area under the curve (AUC) for mono-modality signatures was 0.79 (CI: 0.65–0.88), 0.76 (CI: 0.62–0.87) and 0.93 (CI: 0.75–1) using T1-, T2-weighted and ADC images, respectively. The fuse signature improved the AUC when an ADC-based signature was added. Radiological prediction using RECIST criteria reached an accuracy of 0.78. Conclusions: These results suggest the importance of early delta radiomics and of ADC maps to predict the response to IC in sinonasal cancers.
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spelling pubmed-88770832022-02-26 Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy Corino, Valentina D. A. Bologna, Marco Calareso, Giuseppina Resteghini, Carlo Sdao, Silvana Orlandi, Ester Licitra, Lisa Mainardi, Luca Bossi, Paolo J Imaging Article Background: Response to induction chemotherapy (IC) has been predicted in patients with sinonasal cancer using early delta radiomics obtained from T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps, comparing results with early radiological evaluation by RECIST. Methods: Fifty patients were included in the study. For each image (at baseline and after the first IC cycle), 536 radiomic features were extracted as follows: semi-supervised principal component analysis components, explaining 97% of the variance, were used together with a support vector machine (SVM) to develop a radiomic signature. One signature was developed for each sequence (T1-, T2-weighted and ADC). A multiagent decision-making algorithm was used to merge multiple signatures into one score. Results: The area under the curve (AUC) for mono-modality signatures was 0.79 (CI: 0.65–0.88), 0.76 (CI: 0.62–0.87) and 0.93 (CI: 0.75–1) using T1-, T2-weighted and ADC images, respectively. The fuse signature improved the AUC when an ADC-based signature was added. Radiological prediction using RECIST criteria reached an accuracy of 0.78. Conclusions: These results suggest the importance of early delta radiomics and of ADC maps to predict the response to IC in sinonasal cancers. MDPI 2022-02-15 /pmc/articles/PMC8877083/ /pubmed/35200748 http://dx.doi.org/10.3390/jimaging8020046 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Corino, Valentina D. A.
Bologna, Marco
Calareso, Giuseppina
Resteghini, Carlo
Sdao, Silvana
Orlandi, Ester
Licitra, Lisa
Mainardi, Luca
Bossi, Paolo
Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy
title Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy
title_full Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy
title_fullStr Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy
title_full_unstemmed Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy
title_short Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy
title_sort refining tumor treatment in sinonasal cancer using delta radiomics of multi-parametric mri after the first cycle of induction chemotherapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8877083/
https://www.ncbi.nlm.nih.gov/pubmed/35200748
http://dx.doi.org/10.3390/jimaging8020046
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