Skip to main content

Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors.

Publication ,  Journal Article
Saha, A; Harowicz, MR; Mazurowski, MA
Published in: Med Phys
July 2018

PURPOSE: To review features used in MRI radiomics of breast cancer and study the inter-reader stability of the features. METHODS: We implemented 529 algorithmic features that can be extracted from tumor and fibroglandular tissue (FGT) in breast MRIs. The features were identified based on a review of the existing literature with consideration of their usage, prognostic ability, and uniqueness. The set was then extended so that it comprehensively describes breast cancer imaging characteristics. The features were classified into 10 groups based on the type of data used to extract them and the type of calculation being performed. For the assessment of inter-reader variability, four fellowship-trained readers annotated tumors on preoperative dynamic contrast-enhanced MRIs for 50 breast cancer patients. Based on the annotations, an algorithm automatically segmented the image and extracted all features resulting in one set of features for each reader. For a given feature, the inter-reader stability was defined as the intraclass correlation coefficient (ICC) computed using the feature values obtained through all readers for all cases. RESULTS: The average inter-reader stability for all features was 0.8474 (95% CI: 0.8068-0.8858). The mean inter-reader stability was lower for tumor-based features (0.6348, 95% CI: 0.5391-0.7257) than FGT-based features (0.9984, 95% CI: 0.9970-0.9992). The feature group with the highest inter-reader stability quantifies breast and FGT volume. The feature group with the lowest inter-reader stability quantifies variations in tumor enhancement. CONCLUSIONS: Breast MRI radiomics features widely vary in terms of their stability in the presence of inter-reader variability. Appropriate measures need to be taken for reducing this variability in tumor-based radiomics.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

July 2018

Volume

45

Issue

7

Start / End Page

3076 / 3085

Location

United States

Related Subject Headings

  • Observer Variation
  • Nuclear Medicine & Medical Imaging
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Humans
  • Breast Neoplasms
  • Algorithms
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Saha, A., Harowicz, M. R., & Mazurowski, M. A. (2018). Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors. Med Phys, 45(7), 3076–3085. https://doi.org/10.1002/mp.12925
Saha, Ashirbani, Michael R. Harowicz, and Maciej A. Mazurowski. “Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors.Med Phys 45, no. 7 (July 2018): 3076–85. https://doi.org/10.1002/mp.12925.
Saha, Ashirbani, et al. “Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors.Med Phys, vol. 45, no. 7, July 2018, pp. 3076–85. Pubmed, doi:10.1002/mp.12925.

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

July 2018

Volume

45

Issue

7

Start / End Page

3076 / 3085

Location

United States

Related Subject Headings

  • Observer Variation
  • Nuclear Medicine & Medical Imaging
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Humans
  • Breast Neoplasms
  • Algorithms
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis