Archives

  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • br INTRODUCTION br B reast cancer

    2020-03-24


    INTRODUCTION
    B reast cancer is a significant health concern for women, with one in eight expected to be diagnosed with the disease during their lifetime (1). Development of image-based computer-assisted diagnosis of breast cancer can
    Funding information: This work was supported by the National Institutes of Health grant U01 CA195564 and the Aldeen Memorial Fund at Wheaton College.
    © 2018 The Association of University Radiologists. Published by Elsevier Inc.
    All rights reserved.
    support medical decision-making in diagnosis and treatment. Quantitative features of breast lesions can be extracted from medical images acquired using modalities such as mammogra-phy, ultrasound, computed tomography, and magnetic reso-nance imaging (MRI). Machine learning methods can be used to classify lesions by using these features to predict the probability of the lesion's status in a classification task, such as likelihood of malignancy. This process is known as computer-aided diagnosis
    (2) or, more recently, radiomics (3,4). In addition to the value of radiomics in diagnosis and the prognosis of breast cancer, the use of radiomics in clinical decision-making may also reduce overdi-agnosis (5) and assist in pre- and post-treatment assessment. Radiomic features, such as those describing size, morphology, and texture, have been shown to be useful in the classification of lesions as benign vs malignant (2,6 8). Other studies have inves-tigated the correlation of radiomic features extracted from MRI
    Academic Radiology, Vol 26, No 2, February 2019 RADIOMICS IN DIAGNOSING LUMINAL A CANCERS
    Figure 1. Postcontrast dynamic contrast-enhanced magnetic resonance images of a benign lesion (left) and a luminal A cancer (right). The position of the lesion and the cancer is indicated by a white arrow. Each image was acquired at 1.5 T and is 125 £ 125 mm2 in size, cropped from the full image. Slice thickness for each image was 2 mm. The maximum linear size for the benign lesion is 20.1 mm and the irregularity is 0.50. The maximum linear size for the luminal A cancer is 13.4 mm and the irregularity is 0.78.
    with lesion status, such as cancer stage and Rottlerin node involve-ment (9); luminal B-type cancers (10); human epidermal growth factor 2 (HER2) (11); and luminal A, luminal B, HER2+, and basal-like classifications (12). Additionally, other studies have used these methods to classify lesions according to status as ductal carcinoma in situ and invasive ductal carcinoma (13), triple nega-tive status vs other subtype (14), and all molecular subtypes (15).
    In 2012, 74% of diagnosed breast cancers were type lumi-nal A (16), the most of any molecular subtype. Therefore, Zygote is of particular interest to identify radiomic signatures that aid in the diagnosis and prognosis of luminal A breast cancers. Luminal A lesions typically present on images as irregular and spiculated (17,18). Examples of figures from the dataset used in this work are shown, with their radiomic feature values for maximum linear size and irregularity (Fig 1).
    Our investigation was motivated by the frequency of lumi-nal A breast cancers diagnosed, and we aimed to develop a quantitative radiomic method to distinguish between benign lesions and luminal A subtype cancer. We evaluated the clas-sification of a clinical dataset of lesions as benign vs luminal A using three variations of radiomic signatures: using maximum linear size alone, using feature selection from a full set of radiomic features, and using feature selection from radiomic features excluding those describing size. To the best of our knowledge, these methods have not been previously used to evaluate radiomic classification performance for distinguish-ing between benign lesions and luminal A cancers.
    MATERIALS AND METHODS
    A large clinical dataset of 654 breast lesions imaged with MRI was used in the present study (Table 1). Dynamic contrast-enhanced magnetic resonance images were collected retro-spectively under Health Insurance Portability and Account-ability Act (HIPAA) and institutional review board compliance. The benign lesions were either biopsy proven or 
    TABLE 1. Overview of the Magnetic Resonance Imaging Database of Benign Lesions and Luminal A Cancers
    Age of
    Number Mean Age Subjects Type of of Unique of Subjects (Minimum, Lesion Lesions (y)* Maximum) (y)
    * For some subjects, only the decade of age was available (eg, “40s” or “60s”) as part of the patient information deidentification pro-cess. In these situations, the middle of the decade was used for the calculation of the mean subject age. For example, if a subject's age was given as 40s, the subject's age was entered as “45” for the cal-culation. For the benign lesions, the age of 9 subjects was adjusted, whereas for the luminal A lesions, the age of 21 subjects was adjusted in this way. Subject age was not available for 40 benign lesions and for 11 luminal A cancers.