Descriptive terms for mammographic abnormalities: Observer variation in application

Simpson, Wendy; Neilson, F.; Kelly, Peter

doi:10.1016/s0009-9260(96)80244-3

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…A difficulty in the development of a mammographic knowledge base is the variability amongst mammogram readers in the use of descriptors, which has led some authors to conclude that the standardization of descriptive terms for mammogram interpretation may be an unattainable goal [25]. However, various studies of radiologists' judgments have positively contributed to the definition of useful sets of mammographic features for inclusion in decision aids [26][27].…”

Section: Acquisition Of Radiological Knowledgementioning

confidence: 99%

Elicitation and Representation of Expert Knowledge for Computer Aided Diagnosis in Mammography

Taylor¹,

Lee²,

Alberdi³

2004

Methods Inf Med

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. We used these data to design a scheme with 50 descriptors. In Study 2, ten radiologists used the scheme to describe 40 sets of calcifications. We assessed the capacity of the terms to discriminate between benign and malignant calcifications, testing them against radiologists' assessments of malignancy and follow-up data. Permanent repository link Results:The descriptors that were found to be the most discriminating in Study 2 were included in CADIMUM II's knowledge base. They were represented as arguments for either a benign or a malignant diagnosis. These arguments are the central component of the decision support provided by the system. Other components are: image processing algorithms for the detection and measurement of calcifications and a set of rules that use the measures to decide which of the arguments apply to a given set of calcifications.. Conclusions:Preliminary evaluations of the CADMIUM II prototype reinforce the value of representing explicitly decision making processes in computer aided mammography and of deriving these processes from image processing measurements. Decision support is presented here at a level of description that is both relevant and meaningful to the user.

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Section: Acquisition Of Radiological Knowledgementioning

confidence: 99%