Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

Abstract

Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

“…ID and IDM measure the level of local homogeneity within the tumor volume. Their methods of calculation are based on assuming larger values for smaller gray‐tone differences in pair elements within the gray‐level co‐occurrence matrices (GLCM) 23, 66. Also, they are formulated to have a maximum value when all elements in the image are of equal values.…”

“…Also, they are formulated to have a maximum value when all elements in the image are of equal values. Therefore, these features are characterized by high sensitivity to the presence of adjacent diagonal elements in the GLCM 65, 66. These characteristics might lead to their remarkable insensitivity toward variation of the studied parameters.…”

Population-Based Imaging and Radiomics: Rationale and Perspective of the German National Cohort MRI Study

“…While there are several functioning approaches [14,15], this aspect currently poses a challenge for radiomics.…”

“…In addition to classic statistical analytical methods, approaches such as, for example, machinebased learning, are also pursued [14,17].…”

The Research of Reproducibility and Non-redundancy Feature Selection Methods in Radiomics

“…In the previous work, it has been proposed that radiomics has an ability of capturing tumor heterogeneity and connecting with genomics. In prior work, it has been found that the reproducibility of features depend on the robust of segmentation algorithms, which should provide an accurate and reproducible results [5]. Furthermore, semi-automated segmentation have a better similarity index (SI) than manual segmentation (the SI of machine-segmented lesions SI >0.93, whereas the SI of manual segmentation SI was 0.73) [6].…”