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.
Highlights
Lung cancer affects approximately 1.6 million people worldwide every year [1]
In order to assess the robustness of 3D-Slicer segmentation on computed tomography (CT) imaging for quantitative image feature extraction, we assessed fifty-six 3D-radiomic features quantifying I) tumor intensity, II) tumor shape, and III) tumor texture (Fig. 1 and Supplement S1)
We calculated the intraclass correlation coefficient (ICC) for the radiomic features extracted from these two sets of three 3D-Slicer segmentations and five manual delineations
Summary
Lung cancer affects approximately 1.6 million people worldwide every year [1]. The majority of lung cancer cases are non-small cell lung cancer (NSCLC), which has substantially poor prognosis and low survival rates [2].Medical imaging is one of the major disciplines involved in oncologic science and treatment. Due to the emergence of personalized medicine and targeted treatment, the requirement of quantitative image analysis has risen along with the increasing availability of medical data. Radiomics addresses this issue, and refers to the high throughput extraction of a large number of quantitative and minable imaging features, assuming that these features convey prognostic and predictive information [3,4]. Refers to the high throughput extraction of a large number of quantitative and minable imaging features, assuming that these features convey prognostic and predictive information [3,4] It focuses on optimizing quantitative imaging feature extraction through computational approaches and developing decision support systems, to accurately estimate patient risk and improve individualized treatment selection and monitoring
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