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TU‐A‐12A‐10: Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
Author(s) -
Rios E,
Parmar C,
Jermoumi M,
Aerts H
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4889256
Subject(s) - radiomics , segmentation , artificial intelligence , computer science , robustness (evolution) , medical imaging , pattern recognition (psychology) , feature (linguistics) , image segmentation , reproducibility , computer vision , mathematics , statistics , linguistics , philosophy , biochemistry , chemistry , gene
Purpose: Recent advances in medical imaging technologies provide opportunities to quantify the tumor phenotype throughout the course of treatment non‐invasively. The emerging field of Radiomics addresses this by converting medical images into minable data by applying a large number of quantitative imaging algorithms. Accurate tumor segmentation is one of the main challenges of Radiomics. It has been shown that semiautomatic segmentation approaches efficiently reduce inter‐observer variability as compared to the time consuming manual delineations. In this study, a semiautomatic volumetric segmentation algorithm, implemented in the free and publicly available 3D‐Slicer platform, was investigated in terms of its robustness for Radiomics features quantification. Methods: Fifty‐six 3D‐Radiomics features, quantifying phenotypic differences based on the tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These Radiomics 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. Results: Radiomics 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, the features extracted from 3D‐Slicer based segmentations, spread over significantly smaller range across observers as compared to those of the manual delineations (p= 3.819e‐07). Moreover, the features derived from 3D‐Slicer segmentations overlapped in range with those of the manual delineations, as the lower(higher) limit(s) being significantly higher(lower) for the 3D‐Slicer features (p= 0.007, p= 5.863e‐06). Conclusion: 3D‐Slicer based semiautomatic segmentation significantly improves the robustness of radiomics features and thus could serve as a potential alternative to the time consuming manual segmentation process. So 3D‐Slicer can have a large application in Radiomics, and be employed for high‐throughput data mining research of medical imaging in clinical oncology.