2026-04-27T01:59:19Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/115932024-09-27T10:04:31Zcom_20.500.12105_19604com_20.500.12105_2051col_20.500.12105_19607
Repisalud
author
Martin-Gonzalez, Paula
author
de Mariscal, Estibaliz Gomez
author
Martino, M Elena
author
Gordaliza, Pedro M
author
Peligros, Isabel
author
Carreras, Jose Luis
author
Calvo, Felipe A
author
Pascau, Javier
author
Desco, Manuel
author
Muñoz-Barrutia, Arrate
funder
Ministerio de Economía y Competitividad (España)
funder
Instituto de Salud Carlos III
funder
Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF)
funder
Fundación BBVA
funder
Ministerio de Educación, Cultura y Deporte (España)
funder
Ministerio de Economía, Industria y Competitividad (España)
funder
Fundación ProCNIC
2021-01-08T16:53:29Z
2021-01-08T16:53:29Z
2020-11-30
PLoS One. 2020; 15(11):e0242597
1932-6203
http://hdl.handle.net/20.500.12105/11593
33253194
10.1371/journal.pone.0242597
PloS one
Few 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.
This 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.
Standard 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).
A 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.
eng
Association of visual and quantitative heterogeneity of 18F-FDG PET images with treatment response in locally advanced rectal cancer: A feasibility study.
journal article
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