Book chapter
Lower Recall Rates Reduced Readers’ Sensitivity in Screening Mammography
Breast Imaging, pp.116-121
Lecture Notes in Computer Science, Springer International Publishing
06/17/2016
DOI: 10.1007/978-3-319-41546-8_15
Abstract
Higher recall rates have been related to increased false positive decisions, causing significant psychological and economical costs for both screened women and the mammography screening service respectively. This study compares breast readers’ performance in a laboratory setting under varying levels of recall rates. Four experienced radiologists volunteered to read a single test set of 200 mammographic cases over three separate conditions. The test set contained of 180 normal and 20 abnormal cases and the participants were asked to identify each case that required to be recalled in line with three different target recall rates: control (unspecified or free recall (first read)), 15 % (second read) and 10 % (third read). Readers were required to mark the location of any malignancies using custom made detection software. The recall rates for the control condition ranged between 18.5 % and 34 %. Statistically significant differences were observed in sensitivity for control (median = 0.85) vs 15 % (median = 0.65, z = -2.381, P = 0.017), 15 % vs 10 % (median = 0.55, z = -2.428, P = 0.015) and control vs 10 % (z = -2.381, P = 0.017). ROC AUC was significantly different for control (median = 0.84) vs 15 % (median = 0.79, z = -2.381, P = 0.017) and 15 % vs 10 % (median = 0.75, z = -2.381, P = 0.017). Specificity significantly improved at lower recall rate of 10 % (median = 0.95) vs 15 % (median = 0.92, z = -2.428, P = 0.017). Setting specific target recall rates for readers significantly limited their performance in correctly identifying cancers. In this study, decreasing the number of recalled cases down to 10 %, significantly reduced cancer detection, with a significant improvement in specificity (P ≤ 0.05).
Details
- Title: Subtitle
- Lower Recall Rates Reduced Readers’ Sensitivity in Screening Mammography
- Creators
- Norhashimah Mohd Norsuddin - Diagnostic Imaging and Radiotherapy Programme, Faculty of Health Sciences, The National University of Malaysia (UKM), Kuala Lumpur, MalaysiaClaudia Mello-Thoms - Medical Imaging Optimision and Perception Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Science, The University of Sydney, Lidcombe, AustraliaWarren Reed - Medical Imaging Optimision and Perception Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Science, The University of Sydney, Lidcombe, AustraliaPatrick C Brennan - Medical Imaging Optimision and Perception Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Science, The University of Sydney, Lidcombe, AustraliaSarah Lewis - Medical Imaging Optimision and Perception Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Science, The University of Sydney, Lidcombe, Australia
- Resource Type
- Book chapter
- Publication Details
- Breast Imaging, pp.116-121
- Publisher
- Springer International Publishing; Cham
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-319-41546-8_15
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 06/17/2016
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984051544302771
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