title: 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.
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America; Department of Radiation Oncology (MAASTRO), Maastricht University, Maastricht, The Netherlands; Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.
authors.forename: Chintan
authors.initials: C
authors.lastname: Parmar
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America; Department of Radiation Oncology (MAASTRO), Maastricht University, Maastricht, The Netherlands.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Rios Velazquez
authors.affiliation: Department of Radiation Oncology (MAASTRO), Maastricht University, Maastricht, The Netherlands.
authors.forename: Ralph
authors.initials: R
authors.lastname: Leijenaar
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America; University of Massachusetts, Lowell, Massachusetts, United States of America.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Jermoumi
authors.affiliation: Department of Radiation Oncology (MAASTRO), Maastricht University, Maastricht, The Netherlands.
authors.forename: Sara
authors.initials: S
authors.lastname: Carvalho
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Raymond H
authors.initials: RH
authors.lastname: Mak
authors.affiliation: Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.
authors.forename: Sushmita
authors.initials: S
authors.lastname: Mitra
authors.affiliation: Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.
authors.forename: B Uma
authors.initials: BU
authors.lastname: Shankar
authors.affiliation: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Ron
authors.initials: R
authors.lastname: Kikinis
authors.affiliation: Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Haibe-Kains
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Philippe
authors.initials: P
authors.lastname: Lambin
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America; Department of Radiation Oncology (MAASTRO), Maastricht University, Maastricht, The Netherlands; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Hugo J W L
authors.initials: HJ
authors.lastname: Aerts
mesh_terms: D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis*; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008175:Lung Neoplasms / Q000175:diagnosis*; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D049268:Positron-Emission Tomography; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods