Association of visual and quantitative heterogeneity of 18F-FDG PET images with treatment response in locally advanced rectal cancer: A feasibility study.

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dc.contributor.author Martín-González, Paula
dc.contributor.author Gómez de Mariscal, Estíbaliz
dc.contributor.author Martino, M. Elena
dc.contributor.author Macías Gordaliza, Pedro
dc.contributor.author Peligros, Isabel
dc.contributor.author Carreras, José Luis
dc.contributor.author Calvo, Felipe A
dc.contributor.author Pascau González-Garzón, Javier
dc.contributor.author Desco Menéndez, Manuel
dc.contributor.author Muñoz Barrutia, María Arrate
dc.date.accessioned 2021-03-02T09:11:12Z
dc.date.available 2021-03-02T09:11:12Z
dc.date.issued 2020-11-30
dc.identifier.bibliographicCitation Martín-González, P., Mariscal E. G., Martino, M. E., Gordaliza, P.M., Peligros, I., Carreras, J.L., ..., Muñoz Barrutia, A. (2020) Association of visual and quantitative heterogeneity of 18F-FDG PET images with treatment response in locally advanced rectal cancer: A feasibility study. PLoS ONE, 15(11), e0242597.
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10016/32064
dc.description.abstract Background and purposeFew tools are available to predict tumor response to treatment. This retrospective study assesses visual and automatic heterogeneity from 18F-FDG PET images as predictors of response in locally advanced rectal cancer.MethodsThis study included 37 LARC patients who underwent an 18F-FDG PET before their neoadjuvant therapy. One expert segmented the tumor from the PET images. Blinded to the patient¿s outcome, two experts established by consensus a visual score for tumor heterogeneity. Metabolic and texture parameters were extracted from the tumor area. Multivariate binary logistic regression with cross-validation was used to estimate the clinical relevance of these features. Area under the ROC Curve (AUC) of each model was evaluated. Histopathological tumor regression grade was the ground-truth.ResultsStandard metabolic parameters could discriminate 50.1% of responders (AUC = 0.685). Visual heterogeneity classification showed correct assessment of the response in 75.4% of the sample (AUC = 0.759). Automatic quantitative evaluation of heterogeneity achieved a similar predictive capacity (73.1%, AUC = 0.815).ConclusionA response prediction model in LARC based on tumor heterogeneity (assessed either visually or with automatic texture measurement) shows that texture features may complement the information provided by the metabolic parameters and increase prediction accuracy.
dc.description.sponsorship This work was partially supported by the Spanish Ministry of Economy and Competitiveness (TEC2016–78052-R, PID2019-109820RB-I00) (to AMB) and TEC2013-48251-C2 (to JP), Instituto de Salud Carlos III and European Regional Development Fund (FEDER) Funds from the European Commission, “A way of making Europe” (PI15/02121) and a Leonardo grant to Researchers and Cultural Creators 2017, BBVA Foundation (to AMB). PMG is supported by ‘Beca de Colaboración’ of the Spanish Ministry of Education, Culture and Sports. The CNIC is supported by the Ministry of Economy, Industry and Competitiveness (MEIC) and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).
dc.format.extent 18
dc.language.iso eng
dc.publisher Jason Chia-Hsun Hsieh, Chang Gung Memorial Hospital at Linkou, TAIWAN
dc.rights © 2020 Martín-González et al.
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.title Association of visual and quantitative heterogeneity of 18F-FDG PET images with treatment response in locally advanced rectal cancer: A feasibility study.
dc.type article
dc.identifier.doi https://doi.org/10.1371/journal.pone.0242597
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2016–78052-R
dc.relation.projectID Gobierno de España. PID2019-109820RB-I00
dc.relation.projectID Gobierno de España. TEC2013-48251-C2
dc.type.version publishedVersion
dc.identifier.publicationfirstpage e0242597
dc.identifier.publicationissue 11
dc.identifier.publicationtitle PLoS One
dc.identifier.publicationvolume 15
dc.identifier.uxxi AR/0000026497
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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