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Pathologists' Agreement With Experts and Reproducibility of Breast Ductal Carcinoma-in-Situ Classification Schemes

2000·58 Zitationen·The American Journal of Surgical Pathology
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58

Zitationen

5

Autoren

2000

Jahr

Abstract

Several histologic classifications for breast ductal carcinoma in situ (DCIS) have been proposed. This study assessed the diagnostic agreement and reproducibility of three DCIS classifications (Holland [HL], modified Lagios [LA], and Van Nuys [VN]) by comparing the interpretations of pathologists without expertise in breast pathology with those of three breast pathology experts, each a proponent of one classification. Seven nonexpert pathologists in New Hampshire and three experts evaluated 40 slides of DCIS according to the three classifications. Twenty slides were reinterpreted by each nonexpert pathologist. Diagnostic accuracy (nonexperts compared with experts) and reproducibility were evaluated using inter- and intrarater techniques (kappa statistic). Final DCIS grade and nuclear grade were reported most accurately among nonexpert pathologists using HL (kappa = 0.53 and 0.49, respectively) compared with LA and VN (kappa = 0.29 and 0.35, respectively, for both classifications). An intermediate DCIS grade was assessed most accurately using HL and LA, and a high grade (group 3) was assessed most accurately using VN. Diagnostic reproducibility was highest using HL (kappa = 0.49). The VN interpretation of necrosis (present or absent) was reported more accurately than the LA criteria (extensive, focal, or absent; kappa = 0.59 and 0.45, respectively), but reproducibility of each was comparable (kappa = 0.48 and 0.46, respectively). Intrarater agreement was high overall. Comparing all three classifications, final DCIS grade was reported best using HL. Nuclear grade (cytodifferentiation) using HL and the presence or absence of necrosis were the criteria diagnosed most accurately and reproducibly. Establishing one internationally approved set of interpretive definitions, with acceptable accuracy and reproducibility among both pathologists with and without expertise in breast pathology interpretation, will assist researchers in evaluating treatment effectiveness and characterizing the natural history of DCIS breast lesions.

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AI in cancer detectionBreast Lesions and CarcinomasGlobal Cancer Incidence and Screening
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