Presentation + Paper
4 April 2022 Effect of different molecular subtype reference standards in AI training: implications for DCE-MRI radiomics of breast cancers
Author Affiliations +
Abstract
A single breast cancer lesion can have different luminal molecular subtyping when using either immunohistochemical (IHC) staining alone or the St. Gallen criteria that includes Ki-67. This may impact artificial intelligence/computer aided diagnosis (AI/CADx) for determining molecular subtype from medical images. We investigated this using 28 radiomic features extracted from DCE-MR images of 877 unique lesions segmented by a fuzzy c-means method, for three groups of lesions: (1) Luminal A lesions by both reference standards (“agreement”), (2) lesions that were Luminal A by IHC and Luminal B by St. Gallen (“disagreement”), and (3) Luminal B lesions by both reference standards (“agreement”). The Kruskal-Wallis (KW) test for statistically significant differences in groups of lesions was sequentially followed by the Mann-Whitney U test to determine pair-wise statistical difference between groups for relevant features from the KW test. Classification of lesions as Luminal A or Luminal B using all available radiomic features was conducted using three sets of lesions: (1) lesions with IHC alone molecular subtyping, (2) lesions with St. Gallen molecular subtyping, and (3) agreement lesions. Five-fold cross-validation using stepwise feature selection/linear discriminant analysis classifier classified lesions in each set, with performance measured by the area under the receiver operating characteristic curve (AUC). Six features (sphericity, irregularity, surface area to volume ratio, variance of radial gradient histogram, sum average, and volume of most enhancing voxels) were significantly different among the three groups of features with mixed difference of the disagreement group of lesions to the two agreement luminal groups. When using agreement lesions, more features were selected for classification and the AUC was significantly higher (P < 0.003) than using lesions subtyped by either reference standard. The results suggest that the disagreement of reference standards may impact the development of medical imaging AI/CADx methods for determining molecular subtype.
Conference Presentation
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Heather M. Whitney, Yu Ji, Hui Li, Peifang Liu, and Maryellen L. Giger "Effect of different molecular subtype reference standards in AI training: implications for DCE-MRI radiomics of breast cancers", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120330T (4 April 2022); https://doi.org/10.1117/12.2612205
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KEYWORDS
Breast cancer

Artificial intelligence

Cancer

Medical imaging

Magnetic resonance imaging

Computer aided diagnosis and therapy

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