Reliability of PET/CT shape and heterogeneity features in functional and morphological components of Non-Small Cell Lung Cancer tumors: a repeatability analysis in a prospectiv

Purpose: The main purpose of this study was to assess the reliability of shape and heterogeneity features in both Positron Emission Tomography (PET) and low-dose Computed Tomography (CT) components of PET/CT. A secondary objective was to investigate the impact of image quantization.Material and methods: A Health Insurance Portability and Accountability Act -compliant secondary analysis of deidentified prospectively acquired PET/CT test-retest datasets of 74 patients from multi-center Merck and ACRIN trials was performed. Metabolically active volumes were automatically delineated on PET with Fuzzy Locally Adaptive Bayesian algorithm. 3DSlicerTM was used to semi-automatically delineate the anatomical volumes on low-dose CT components. Two quantization methods were considered: a quantization into a set number of bins (quantizationB) and an alternative quantization with bins of fixed width (quantizationW). Four shape descriptors, ten first-order metrics and 26 textural features were computed. Bland-Altman analysis was used to quantify repeatability. Features were subsequently categorized as very reliable, reliable, moderately reliable and poorly reliable with respect to the corresponding volume variability. Results: Repeatability was highly variable amongst features. Numerous metrics were identified as poorly or moderately reliable. Others were (very) reliable in both modalities, and in all categories (shape, 1st-, 2nd- and 3rd-order metrics). Image quantization played a major role in the features repeatability. Features were more reliable in PET with quantizationB, whereas quantizationW showed better results in CT.Conclusion: The test-retest repeatability of shape and heterogeneity features in PET and low-dose CT varied greatly amongst metrics. The level of repeatability also depended strongly on the quantization step, with different optimal choices for each modality. The repeatability of PET and low-dose CT features should be carefully taken into account when selecting metrics to build multiparametric models.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/18/2023

BRONCO: Automated modelling of the bronchovascular bundle using the Computed Tomography Images

Segmentation of the bronchovascular bundle within the lung parenchyma is...
research
04/30/2021

Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Study

Radiomics is an active area of research in medical image analysis, the l...
research
09/06/2021

Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study

Radiomics is an active area of research focusing on high throughput feat...
research
04/02/2021

Low Dose Helical CBCT denoising by using domain filtering with deep reinforcement learning

Cone Beam Computed Tomography(CBCT) is a now known method to conduct CT ...
research
08/14/2019

Are Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters?

Consistency and duplicability in Computed Tomography (CT) output is esse...
research
12/23/2019

CBCT-to-CT synthesis with a single neural network for head-and-neck, lung and breast cancer adaptive radiotherapy

Purpose: CBCT-based adaptive radiotherapy requires daily images for accu...
research
10/29/2022

2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study

Objective: Radiomics, an emerging tool for medical image analysis, is po...

Please sign up or login with your details

Forgot password? Click here to reset