Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms

被引:14
|
作者
Rana, Rich S. [1 ]
Jiang, Yulei [1 ]
Schmidt, Robert A. [1 ]
Nishikawa, Robert M. [1 ]
机构
[1] Univ Chicago, Dept Radiol, Chicago, IL 60637 USA
关键词
computer-aided diagnosis; full-field digital mammography; diagnostic mammography; breast calcifications; breast cancer diagnosis;
D O I
10.1016/j.acra.2006.12.012
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Rationale and Objectives. To evaluate whether a computer-aided diagnosis (CADx) technique can accurately classify breast calcifications in full-field digital mammograms (FFDMs) as malignant or benign. The computer technique was developed previously on screen-film mammograms (SFMs) in which individual calcifications were identified manually. The present study evaluated the computer technique independently on a new database of FFDM images with automatic detection of the individual calcifications. Materials and Methods. We analyzed 49 consecutive FFDM cases (19 cancers) that showed suspicious calcifications. Four mammography radiologists read soft-copy mammograms retrospectively and electronically indicated the region of calcifications in each image. The computer then automatically detected the individual calcifications within the indicated region and analyzed eight features of calcification morphology and distribution to arrive at an estimated likelihood of malignancy. The radiologists entered Breast Imaging Report and Data System assessments before and after seeing the computer results. Performance was analyzed using receiver operating characteristic analysis. Results. Despite variability in radiologist-indicated regions of calcifications, the computer achieved consistently high performance taking input from the four radiologists (receiver operating characteristic curve area, A(z): 0.80, 0.80, 0.78, and 0.77; differences not statistically significant). Previous results showed that the computer technique achieved an A, value of 0.80 on SFMs, which improved radiologists' performance significantly. Conclusions. The computer technique appears to maintain consistently high performance in classifying calcifications in FFDMs as malignant or benign without requiring substantial modification from its initial development on SFMs. The computer performance appears to be robust with respect to variations in radiologists' input.
引用
收藏
页码:363 / 370
页数:8
相关论文
共 50 条
  • [1] Effect of radiologists' variability on the performance of computer classification of malignant and benign calcifications in mammograms
    Jiang, YL
    Salfity, MF
    Chen, V
    Nishikawa, RM
    Papaioannou, J
    Edwards, AV
    Paquerault, S
    MEDICAL IMAGING 2003: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, 2003, 5034 : 42 - 47
  • [2] YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms
    Aly, Ghada Hamed
    Marey, Mohammed
    El-Sayed, Safaa Amin
    Tolba, Mohamed Fahmy
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2021, 200 (200)
  • [3] Classification of Breast Micro-calcifications as Benign or Malignant Using Subtraction of Temporally Sequential Digital Mammograms and Machine Learning
    Loizidou, Kosmia
    Skouroumouni, Galateia
    Savvidou, Gabriella
    Constantinidou, Anastasia
    Nikolaou, Christos
    Pitris, Costas
    COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2023, PT II, 2023, 14185 : 109 - 118
  • [4] Evaluation of Computer-aided Diagnosis on a Large Clinical Full-field Digital Mammographic Dataset
    Li, Hui
    Giger, Maryellen L.
    Yuan, Yading
    Chen, Weijie
    Horsch, Karla
    Lan, Li
    Jamieson, Andrew R.
    Sennett, Charlene A.
    Jansen, Sanaz A.
    ACADEMIC RADIOLOGY, 2008, 15 (11) : 1437 - 1445
  • [5] Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms
    Dounis, Anastasios
    Avramopoulos, Andreas-Nestor
    Kallergi, Maria
    APPLIED SCIENCES-BASEL, 2023, 13 (18):
  • [6] Classification of benign and malignant masses in breast mammograms
    Serifovic-Trbalic, A.
    Trbalic, A.
    Demirovic, D.
    Prljaca, N.
    Cattin, P. C.
    2014 37TH INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO), 2014, : 228 - 233
  • [7] Detection of Breast Cancer with Full-Field Digital Mammography and Computer-Aided Detection
    The, Juliette S.
    Schilling, Kathy J.
    Hoffmeister, Jeffrey W.
    Friedmann, Euvondia
    McGinnis, Ryan
    Holcomb, Richard G.
    AMERICAN JOURNAL OF ROENTGENOLOGY, 2009, 192 (02) : 337 - 340
  • [8] Breast composition measurements from Full-Field Digital Mammograms using generative adversarial networks
    Garcia, E.
    Llado, X.
    Mann, R. M.
    Osuala, R.
    Marti, R.
    17TH INTERNATIONAL WORKSHOP ON BREAST IMAGING, IWBI 2024, 2024, 13174
  • [9] Considering breast density for the classification of benign and malignant mammograms
    Huang, Mei-Ling
    Lin, Ting-Yu
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2021, 67
  • [10] Gray-scale and geometric registration of full-field digital and film-screen mammograms
    Snoeren, Peter R.
    Karssmeijer, Nico
    MEDICAL IMAGE ANALYSIS, 2007, 11 (02) : 146 - 156