Abstract
Our goal in this paper is to build parametric models for a dictionary of histological patterns that aid in the differential diagnosis of atypical breast lesions and evaluate the inferential power of these hand-crafted features. Diagnosis of high-risk atypical breast lesions is challenging and remains a critical component of breast cancer screening, presenting even for experienced pathologists a more difficult classification problem than the binary detection task of cancer vs not-cancer. Following guidelines in the WHO classification of the tumors of the breast (an essential reference for pathologists, clinicians and researchers) and in consultation with our team of breast sub-specialists (N = 3), we assembled a visual dictionary of sixteen histological patterns (e.g., cribriform, picket-fence), a subset that pathologists frequently use in making complex diagnostic decisions of atypical breast lesions. We invoke parametric models for each pattern using a mix of unary, binary and ternary features that account for morphological and architectural tissue properties. We use 1441 ductal regions of interest (ROIs) extracted automatically from 93 whole slide images (WSIs) with a computational pathology pipeline. We collected diagnostic labels for all of the ROIs: normal and columnar cell changes (CCC) as low-risk benign lesions (= 1124), and flat epithelium atypia (FEA) and atypical ductal hyperplasia (ADH) as high-risk benign lesions (= 317). We generate likelihood maps for each dictionary pattern across a given ROI and integrate this information to determine a diagnostic label of high- or low-risk. Our method has comparable classification accuracies to the pool of breast pathology sub-specialists. Our study enables a deeper understanding of the discordance among pathologists in diagnosing atypical breast lesions.
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Acknowledgments
The grant NIH-NCI U01CA204826 to SCC supported this work. The work of AP and OC was partially supported by the sub-contracts 9F-60178 and 9F-60287 from Argonne National Laboratory (ANL) to the University of Pittsburgh from the parent grant DE-AC02-06CH1135 titled, Co-Design of Advanced Artificial Intelligence Systems for Predicting Behavior of Complex Systems Using Multimodal Datasets, from the Department of Energy to ANL.
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Parvatikar, A. et al. (2020). Modeling Histological Patterns for Differential Diagnosis of Atypical Breast Lesions. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_53
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