Spatial resolution requirements for acquisition of the virtual screening slide for digital whole-specimen breast histopathology.
We examined the effect of lateral spatial resolution and reader specialty on the accuracy of detection of breast cancer. The motivation for this pilot study was the need to acquire and display very large data sets in whole-specimen 3D digital breast histopathology imaging. The ultimate goal is to determine the minimum resolution adequate for detection of malignancy. Twenty-three histologic slides were selected from breast pathology cases and digitized at 2 sampling distances (3.2 and 1.9 microm pixels). Images were viewed by 14 pathologists, of whom 5 had breast pathology as their primary specialty. The readers assessed the likelihood of malignancy on a 5-point Likert scale, and provided a provisional diagnosis. For the detection task, sensitivity, specificity, overall accuracy of detection, and area under the receiver-operator curve were calculated. An overall diagnostic score, and scores grouped by malignancy type, were also computed. Outcome measures were examined for significant resolution and specialty effects. Increasing the lateral resolution significantly improved accuracy in diagnosis (P=.004) but no effect was found for detection. Breast specialists achieved significantly higher scores for all outcome measures except specificity. Differences in performance between the 2 groups of readers tended to be greater for the diagnostic task compared to detection, especially at the higher resolution. However, specimen coverage may also be a significant factor. Factors related to the readers may have also affected performance in this study. Based on these results, a more comprehensive study should examine pixel sizes between 0.7 and 1.9 microm.
Clarke
Gina M
GM
Imaging Research, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada M4N 3M5. gina.clarke@sunnybrook.ca
Zubovits
Judit T
JT
Katic
Marko
M
Peressotti
Chris
C
Yaffe
Martin J
MJ
D001943:Breast Neoplasms / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008511:Medicine; D015588:Observer Variation; D010820:Physicians; D010865:Pilot Projects; D012189:Retrospective Studies; D013038:Specialization