RT Journal Article
T1 Association of visual and quantitative heterogeneity of 18F-FDG PET images with treatment response in locally advanced rectal cancer: A feasibility study.
A1 Martín-González, Paula
A1 Gómez de Mariscal, Estíbaliz
A1 Martino, M. Elena
A1 Macías Gordaliza, Pedro
A1 Peligros, Isabel
A1 Carreras, José Luis
A1 Calvo, Felipe A
A1 Pascau González-Garzón, Javier
A1 Desco Menéndez, Manuel
A1 Muñoz Barrutia, María Arrate
AB 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.
PB Jason Chia-Hsun Hsieh, Chang Gung Memorial Hospital at Linkou, TAIWAN
SN 1932-6203
YR 2020
FD 2020-11-30
LK https://hdl.handle.net/10016/32064
UL https://hdl.handle.net/10016/32064
LA eng
NO 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).
DS e-Archivo
RD 19 may. 2024