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Investigation into diagnostic agreement using automated computer-assisted histopathology pattern recognition image analysis.

Authors :
Webster JD
Michalowski AM
Dwyer JE
Corps KN
Wei BR
Juopperi T
Hoover SB
Simpson RM
Source :
Journal of pathology informatics [J Pathol Inform] 2012; Vol. 3, pp. 18. Date of Electronic Publication: 2012 Apr 18.
Publication Year :
2012

Abstract

The extent to which histopathology pattern recognition image analysis (PRIA) agrees with microscopic assessment has not been established. Thus, a commercial PRIA platform was evaluated in two applications using whole-slide images. Substantial agreement, lacking significant constant or proportional errors, between PRIA and manual morphometric image segmentation was obtained for pulmonary metastatic cancer areas (Passing/Bablok regression). Bland-Altman analysis indicated heteroscedastic measurements and tendency toward increasing variance with increasing tumor burden, but no significant trend in mean bias. The average between-methods percent tumor content difference was -0.64. Analysis of between-methods measurement differences relative to the percent tumor magnitude revealed that method disagreement had an impact primarily in the smallest measurements (tumor burden <3%). Regression-based 95% limits of agreement indicated substantial agreement for method interchangeability. Repeated measures revealed concordance correlation of >0.988, indicating high reproducibility for both methods, yet PRIA reproducibility was superior (C.V.: PRIA = 7.4, manual = 17.1). Evaluation of PRIA on morphologically complex teratomas led to diagnostic agreement with pathologist assessments of pluripotency on subsets of teratomas. Accommodation of the diversity of teratoma histologic features frequently resulted in detrimental trade-offs, increasing PRIA error elsewhere in images. PRIA error was nonrandom and influenced by variations in histomorphology. File-size limitations encountered while training algorithms and consequences of spectral image processing dominance contributed to diagnostic inaccuracies experienced for some teratomas. PRIA appeared better suited for tissues with limited phenotypic diversity. Technical improvements may enhance diagnostic agreement, and consistent pathologist input will benefit further development and application of PRIA.

Details

Language :
English
ISSN :
2153-3539
Volume :
3
Database :
MEDLINE
Journal :
Journal of pathology informatics
Publication Type :
Academic Journal
Accession number :
22616030
Full Text :
https://doi.org/10.4103/2153-3539.95130