Multiparametric quantitative and texture 18F-FDG PET/CT analysis for primary malignant tumour grade differentiation

Abstract

Background18F-FDG positron emission tomography/computed tomography (PET/CT) is a successfully used imaging modality in oncology. The aim of the study was to investigate a connection of epithelial tumour differentiation grade with both semiquantitative and quantitative metabolic PET data focusing on creation of multiparametric model of tumour grade prediction utilising both standardised uptake value-based and texture-based 18F-FDG PET parameters and to investigate an influence of different image segmentation techniques on these parameters and modelling.Methods18F-FDG PET/CT data from 84 patients with epithelial malignant tumours was retrospectively analysed to create sets of both conventional semiquantitative (based on standardised uptake values), volumetric, and quantitative texture metabolic parameters of primary tumours with four different segmentation techniques.ResultsMost of the calculated volumetric and texture parameters showed to be influenced by segmentation technique. There was no significant difference in values of only three parameters, in all four segmentation methods: homogeneity, energy, and sphericity. Almost every extracted parameter in all segmentation technique subsets showed significant ability to discriminate individual tumour grade versus the subset of remaining two tumour grades. No parameters were able to discriminate all three tumour grades separately simultaneously or without the overlapping of threshold values. Group method of data handling (GMDH) modelling included all the above-mentioned extracted parameters. The highest value to discriminate tumour grade was achieved using ITK-SNAP segmentation, with an accuracy ranging from 91 to 100%.ConclusionsMultiparametric modelling with GMDH utilising both semiquantitative and quantitative texture metabolic PET parameters seems to be an interesting tool for non-invasive malignant epithelial tumours grade differentiation.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

“…We collected 19 publications focusing on gynecological cancers [ 82 , 104 , 207 , 208 , 209 , 210 , 211 , 212 , 213 , 214 , 215 , 216 , 217 , 218 , 219 , 220 , 221 , 222 , 223 ], particularly on cervical (74%), endometrial (16%), epithelial (5%) and vulvar cancer (5%). The average number of patients was 93 (median = 84, range, 20–190).…”

“…In a similar work, Shen et al [ 212 ] concluded that homogeneity from the GLCM combined with TLG was the best combination of predictors, partially supporting these results. In a study involving 84 patients, Novikov [ 221 ] evaluated whether PET radiomic features of epithelial tumors would be able to predict the differentiation grade, reporting an accuracy between 91% and 100%. Finally, regarding prognosis, Lucia et al [ 223 ] presented one of a very few papers aiming at validating a previously developed radiomics model [ 222 ], providing compelling evidence of the ability of PET+MR radiomics to predict disease-free survival and locoregional control in locally advanced cervical cancer.…”

A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events

“…The performance of classical GMDH in the modeling of nonlinear problems has been demonstrated in various studies [33][34][35][36]. However, along with its advantages, it possesses the following limitations: (i) second-order polynomials, (ii) only two inputs for each neuron, (iii) inputs of each neuron can only be selected from the adjacent layer [37,38].…”

Prognostic Value of Textural Features Obtained from F-fluorodeoxyglucose (F-18 FDG) Positron Emission Tomography/Computed Tomography (PET/CT) in Patients with Locally Advanced Cervical Cancer Undergoing Concurrent Chemoradiotherapy

“…Another cohort revealed that distant metastasis was signi cantly associated with greater average tumor volume [38]. Although no study has reported a relationship between compacity and prognosis in cervical cancer, few studies have shown that compacity is associated with several tumor types, including pelvic malignancies [39][40][41].…”