4.7 Article

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

期刊

RADIOLOGY
卷 295, 期 2, 页码 328-338

出版社

RADIOLOGICAL SOC NORTH AMERICA
DOI: 10.1148/radiol.2020191145

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资金

  1. Cancer Research UK
  2. Engineering and Physical Sciences Research Council
  3. Medical Research Council
  4. Department of Health and Social Care [C1519/A16463]
  5. Dutch Cancer Society [10034]
  6. EU Seventh Framework Programme [ARTFORCE 257144, REQUITE 601826]
  7. Engineering and Physical Sciences Research Council [EP/M507842/1, EP/N509449/1]
  8. European Research Council (ERC) [AdG-2015: 694812-Hypoximmuno, ERC StG-2013: 335367 bioiRT]
  9. Eurostars [DART 10116, DECIDE 11541]
  10. French National Institute of Cancer [C14020NS]
  11. French National Research Agency [ANR-10-LABX-07-01, ANR-11-IDEX-0003-02]
  12. German Federal Ministry of Education and Research (BMBF) [03Z1N52]
  13. Horizon 2020 Framework Programmme (BD2Decide) [PHC-30-689715]
  14. Horizon 2020 Framework Programmme (IMMUNOSABR) [SC1-PM-733008]
  15. Innovative Medicines Initiative [IMI JU QuIC-ConCePT 115151]
  16. Interreg V-A Euregio Meuse-Rhine (Euradiomics)
  17. National Cancer Institute [P30CA008748, U01CA187947, U24CA189523]
  18. National Institute of Neurologic Disorders and Stroke [R01NS042645]
  19. National Institutes of Health [R01CA198121, U01CA143062, U01CA190234, U24CA180918, U24CA194354]
  20. SME phase 2 [RAIL 673780]
  21. Swiss National Science Foundation [310030 173303, PZ00P2 154891]
  22. Technology Foundation STW [10696 DuCAT, P14-19 Radiomics STRaTegy]
  23. Netherlands Organization for Health Research and Development [10-10400-98-14002]
  24. Netherlands Organization for Scientific Research [14929]
  25. University of Zurich Clinical Research Priority Program
  26. Wellcome Trust [WT203148/Z/16/Z]

向作者/读者索取更多资源

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. (C) RSNA, 2020

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