Computer-Assisted Nuclear Atypia Scoring of Breast Cancer: a Preliminary Study.
Inter-pathologist agreement for nuclear atypia scoring of breast cancer is poor. To address this problem, previous studies suggested some criteria for describing the variations appearance of tumor cells relative to normal cells. However, these criteria were still assessed subjectively by pathologist...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 5; pp. 702 - 713 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138543067&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138543067 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2019 vid: 32 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138543067 138543067 143995941 138543067 10.1007/s10278-019-00181-8 138543067 ppf: 702 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Computer-Assisted Nuclear Atypia Scoring of Breast Cancer: a Preliminary Study. aug: au: Gandomkar, Ziba Brennan, Patrick C. Mello-Thoms, Claudia affil: Discipline of Medical Imaging and Radiation Sciences, Medical Image Optimisation and Perception Group (MIOPeG), The University of Sydney, 512/Block M, Cumberland Campus, Sydney, NSW, Australia sug: subj: Breast Neoplasms Pathology Neoplasm Grading Methods Image Processing, Computer Assisted Pathologists Human Machine Learning Descriptive Statistics Scanners Microscopy Cytological Techniques Image Interpretation, Computer Assisted ab: Inter-pathologist agreement for nuclear atypia scoring of breast cancer is poor. To address this problem, previous studies suggested some criteria for describing the variations appearance of tumor cells relative to normal cells. However, these criteria were still assessed subjectively by pathologists. Previous studies used quantitative computer-extracted features for scoring. However, application of these tools is limited as further improvement in their accuracy is required. This study proposes COMPASS (COMputer-assisted analysis combined with Pathologist's ASSessment) for reproducible nuclear atypia scoring. COMPASS relies on both cytological criteria assessed subjectively by pathologists as well as computer-extracted textural features. Using machine learning, COMPASS combines these two sets of features and output nuclear atypia score. COMPASS's performance was evaluated using 300 images for which expert-consensus derived reference nuclear pleomorphism scores were available, and they were scanned by two scanners from different vendors. A personalized model was built for three pathologists who gave scores to six atypia-related criteria for each image. Leave-one-out cross validation (LOOCV) was used. COMPASS was trained and tested for each pathologist separately. Percentage agreement between COMPASS and the reference nuclear scores was 93.8%, 92.9%, and 93.1% for three pathologists. COMPASS's performance in nuclear grading was almost identical for both scanners, with Cohen's kappa ranging from 0.80 to 0.86 for different pathologists and different scanners. Independently, the images were also assessed by two experienced senior pathologists. Cohen's kappa of COMPASS was comparable to the Cohen's kappa for two senior pathologists (0.79 and 0.68). pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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