title: Region of interest identification and diagnostic agreement in breast pathology.
abstract: A pathologist's accurate interpretation relies on identifying relevant histopathological features. Little is known about the precise relationship between feature identification and diagnostic decision making. We hypothesized that greater overlap between a pathologist's selected diagnostic region of interest (ROI) and a consensus derived ROI is associated with higher diagnostic accuracy. We developed breast biopsy test cases that included atypical ductal hyperplasia (n=80); ductal carcinoma in situ (n=78); and invasive breast cancer (n=22). Benign cases were excluded due to the absence of specific abnormalities. Three experienced breast pathologists conducted an independent review of the 180 digital whole slide images, established a reference consensus diagnosis and marked one or more diagnostic ROIs for each case. Forty-four participating pathologists independently diagnosed and marked ROIs on the images. Participant diagnoses and ROI were compared with consensus reference diagnoses and ROI. Regression models tested whether percent overlap between participant ROI and consensus reference ROI predicted diagnostic accuracy. Each of the 44 participants interpreted 39-50 cases for a total of 1972 individual diagnoses. Percent ROI overlap with the expert reference ROI was higher in pathologists who self-reported academic affiliation (69 vs 65%, P=0.002). Percent overlap between participants' ROI and consensus reference ROI was then classified into ordinal categories: 0, 1-33, 34-65, 66-99 and 100% overlap. For each incremental change in the ordinal percent ROI overlap, diagnostic agreement increased by 60% (OR 1.6, 95% CI (1.5-1.7), P<0.001) and the association remained significant even after adjustment for other covariates. The magnitude of the association between ROI overlap and diagnostic agreement increased with increasing diagnostic severity. The findings indicate that pathologists are more likely to converge with an expert reference diagnosis when they identify an overlapping diagnostic image region, suggesting that future computer-aided detection systems that highlight potential diagnostic regions could be a helpful tool to improve accuracy and education.
authors.affiliation: Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA.
authors.forename: Dilip B
authors.initials: DB
authors.lastname: Nagarkar
authors.affiliation: Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
authors.forename: Ezgi
authors.initials: E
authors.lastname: Mercan
authors.affiliation: Department of Pathology and UVM Cancer Center, University of VT, Burlington, VT, USA.
authors.forename: Donald L
authors.initials: DL
authors.lastname: Weaver
authors.affiliation: Department of Psychology, Tufts University, Medford, MA, USA.
authors.forename: Tad T
authors.initials: TT
authors.lastname: Brunyé
authors.affiliation: Department of Family Medicine, Oregon Health and Science University, Portland, OR, USA.
authors.forename: Patricia A
authors.initials: PA
authors.lastname: Carney
authors.affiliation: Department of Pathology, University of Washington School of Medicine, Seattle, WA, USA.
authors.forename: Mara H
authors.initials: MH
authors.lastname: Rendi
authors.affiliation: Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
authors.forename: Andrew H
authors.initials: AH
authors.lastname: Beck
authors.affiliation: Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA.
authors.forename: Paul D
authors.identifier: 0000-0002-7936-5488
authors.initials: PD
authors.lastname: Frederick
authors.affiliation: Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
authors.forename: Linda G
authors.initials: LG
authors.lastname: Shapiro
authors.affiliation: Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA.
authors.forename: Joann G
authors.initials: JG
authors.lastname: Elmore
mesh_terms: D000328:Adult; D001706:Biopsy; D001943:Breast Neoplasms / Q000473:pathology*; D002277:Carcinoma / Q000473:pathology*; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000473:pathology*; D032921:Consensus; D005260:Female; D006801:Humans; D006965:Hyperplasia; D008297:Male; D008875:Middle Aged; D009361:Neoplasm Invasiveness; D015588:Observer Variation; D000072142:Pathologists; D010865:Pilot Projects; D011237:Predictive Value of Tests; D011379:Prognosis; D014481:United States