Classification of Background Parenchymal Uptake on Molecular Breast Imaging Using a Convolutional Neural Network

JCO Clinical Cancer Informatics
Rickey E CarterCarrie B Hruska

Abstract

Background parenchymal uptake (BPU), which describes the level of radiotracer uptake in normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor. Our objective was to develop and validate a deep learning model using image convolution to automatically categorize BPU on MBI. MBI examinations obtained for clinical and research purposes from 2004 to 2015 were reviewed to classify the BPU pattern using a standardized five-category scale. Two expert radiologists provided interpretations that were used as the reference standard for modeling. The modeling consisted of training and validating a convolutional neural network to predict BPU. Model performance was summarized in data reserved to test the performance of the algorithm at the per-image and per-breast levels. Training was performed on 24,639 images from 3,133 unique patients. The model performance on the withheld testing data (6,172 images; 786 patients) was evaluated. Using direct matching on the predicted classification resulted in an accuracy of 69.4% (95% CI, 67.4% to 71.3%), and if prediction within one category was considered, accuracy increased to 96.0% (95% CI, 95.2% to 96.7%). When considering the breast-level pr...Continue Reading

References

Mar 19, 2011·BMC Bioinformatics·Xavier RobinMarkus Müller
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Nov 22, 2012·AJR. American Journal of Roentgenology·Amy Lynn ConnersWendie A Berg
Oct 30, 2013·Annals of Oncology : Official Journal of the European Society for Medical Oncology·M C Pike, C L Pearce
Jan 17, 2014·EJNMMI Research·Carrie B HruskaMichael K O'Connor
Apr 27, 2016·Breast Cancer Research : BCR·Carrie B HruskaCeline M Vachon
Oct 21, 2016·AJR. American Journal of Roentgenology·Carrie B Hruska
Nov 14, 2017·Radiographics : a Review Publication of the Radiological Society of North America, Inc·Gabriel ChartrandAn Tang
Oct 20, 2018·Current Cardiology Reports·Matthew E Dilsizian, Eliot L Siegel

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Citations

Aug 7, 2020·AJR. American Journal of Roentgenology·Carrie B HruskaCeline M Vachon

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