Manuel Piñeiro-Fiel1,2, Alexis Moscoso1,2, Virginia Pubul2, Álvaro Ruibal1,2,3, Jesús Silva-Rodríguez2, Pablo Aguiar1,2. 1. Molecular Imaging and Medical Physics Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, 15706 Santiago de Compostela, Spain. 2. Molecular Imaging Research Group, Nuclear Medicine Department, University Hospital and Health Research Institute of Santiago de Compostela (IDIS), 15706 Santiago de Compostela, Spain. 3. Fundación Tejerina, José Abascal, 40, 28003 Madrid, Spain.
Abstract
BACKGROUND: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. METHODS: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. RESULTS: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20-1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1-286). CONCLUSIONS: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
BACKGROUND: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. METHODS: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. RESULTS: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20-1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1-286). CONCLUSIONS: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.