Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer

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Abstract

Background

Radiomics is a promising field in oncology imaging. However, the implementation of radiomics clinically has been limited because its robustness remains unclear. Previous CT and PET studies suggested that radiomic features were sensitive to variations in pixel size and slice thickness of the images. The purpose of this study was to assess robustness of magnetic resonance (MR) radiomic features to pixel size resampling and interpolation in patients with cervical cancer.

Methods

This retrospective study included 254 patients with a pathological diagnosis of cervical cancer stages IB to IVA who received definitive chemoradiation at our institution between January 2006 and June 2020. Pretreatment MR scans were analyzed. Each region of cervical cancer was segmented on the axial gadolinium-enhanced T1- and T2-weighted images; 107 radiomic features were extracted. MR scans were interpolated and resampled using various slice thicknesses and pixel spaces. Intraclass correlation coefficients (ICCs) were calculated between the original images and images that underwent pixel size resampling (OP), interpolation (OI), or pixel size resampling and interpolation (OP+I) as well as among processed image sets with various pixel spaces (P), various slice thicknesses (I), and both (P + I).

Results

After feature standardization, ≥86.0% of features showed good robustness when compared between the original and processed images (OP, OI, and OP+I) and ≥ 88.8% of features showed good robustness when processed images were compared (P, I, and P + I). Although most first-order, shape, and texture features showed good robustness, GLSZM small-area emphasis-related features and NGTDM strength were sensitive to variations in pixel size and slice thickness.

Conclusion

Most MR radiomic features in patients with cervical cancer were robust after pixel size resampling and interpolation following the feature standardization process. The understanding regarding the robustness of individual features after pixel size resampling and interpolation could help future radiomics research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-021-00388-5.

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Most cited references 28

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Radiomics: Images Are More than Pictures, They Are Data

Robert Gillies, Paul E Kinahan, Hedvig Hricak (2015)

This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.

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Computational Radiomics System to Decode the Radiographic Phenotype

Chintan Parmar, Ahmed Raafat Hosny, Hugo J.W.L. Aerts … (2017)

Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop non-invasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics , a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D-Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung-lesions. Source code, documentation, and examples are publicly available at www.radiomics.io . With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research.

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The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah … (2020)

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

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Author and article information

Contributors

Shin-Hyung Park:

ORCID: http://orcid.org/0000-0003-0291-8985

shinhyungpark@knu.ac.kr

Journal

Journal ID (nlm-ta): Cancer Imaging

Journal ID (iso-abbrev): Cancer Imaging

Title: Cancer Imaging

Publisher: BioMed Central (London )

ISSN (Print): 1740-5025

ISSN (Electronic): 1470-7330

Publication date (Electronic): 2 February 2021

Publication date PMC-release: 2 February 2021

Publication date Collection: 2021

Volume: 21

Electronic Location Identifier: 19

Affiliations

[1 ]GRID grid.411235.0, ISNI 0000 0004 0647 192X, Department of Radiation Oncology, , School of Medicine, Kyungpook National University Hospital, ; 130 Dongduk-Ro, Jung-Gu, Daegu, 41944 Republic of Korea

[2 ]GRID grid.258803.4, ISNI 0000 0001 0661 1556, Cardiovascular Research Institute, School of Medicine, , Kyungpook National University, ; Daegu, Republic of Korea

[3 ]GRID grid.258803.4, ISNI 0000 0001 0661 1556, Department of Radiology, , School of Medicine, Kyungpook National University, ; Daegu, Republic of Korea

[4 ]GRID grid.258803.4, ISNI 0000 0001 0661 1556, Department of Obstetrics and Gynecology, , School of Medicine, Kyungpook National University, ; Daegu, Republic of Korea

[5 ]GRID grid.258803.4, ISNI 0000 0001 0661 1556, Department of Obstetrics and Gynecology, , Kyungpook National University Chilgok Hospital, ; Daegu, Republic of Korea

[6 ]GRID grid.258803.4, ISNI 0000 0001 0661 1556, Clinical Omics Research Center, School of Medicine, , Kyungpook National University, ; Daegu, Republic of Korea

[7 ]GRID grid.258803.4, ISNI 0000 0001 0661 1556, Department of Nuclear Medicine, , School of Medicine, Kyungpook National University, ; Daegu, Republic of Korea

Author information

Shin-Hyung Park http://orcid.org/0000-0003-0291-8985

Article

Publisher ID: 388

DOI: 10.1186/s40644-021-00388-5

PMC ID: 7856733

PubMed ID: 33531073

SO-VID: 7205e46c-641f-4389-8041-d674f1f02d11

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 6 October 2020

Date accepted : 27 January 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;

Award ID: 2019R1G1A1089358

Award Recipient : Shin-Hyung Park

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Keywords: radiomics,cervical cancer,magnetic resonance imaging,pixel size resampling,interpolation,robustness

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Keywords: radiomics, cervical cancer, magnetic resonance imaging, pixel size resampling, interpolation, robustness

Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer

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Most cited references 28

Radiomics: Images Are More than Pictures, They Are Data

Computational Radiomics System to Decode the Radiographic Phenotype

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

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