Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images.
Quantitative histomorphometry (QH) is the process of computerized feature extraction from digitized tissue slide images to predict disease presence, behavior, and outcome. Feature stability between sites may be compromised by laboratory-specific variables including dye batch, slice thickness, and the whole slide scanner used. We present two new measures, preparation-induced instability score and latent instability score, to quantify feature instability across and within datasets. In a use case involving prostate cancer, we examined QH features which may detect cancer on whole slide images. Using our method, we found that five feature families (graph, shape, co-occurring gland tensor, sub-graph, and texture) were different between datasets in 19.7% to 48.6% of comparisons while the values expected without site variation were 4.2% to 4.6%. Color normalizing all images to a template did not reduce instability. Scanning the same 34 slides on three scanners demonstrated that Haralick features were most substantively affected by scanner variation, being unstable in 62% of comparisons. We found that unstable feature families performed significantly worse in inter- than intrasite classification. Our results appear to suggest QH features should be evaluated across sites to assess robustness, and class discriminability alone should not represent the benchmark for digital pathology feature selection.
Case Western Reserve University , Department of Biomedical Engineering, 2071 Martin Luther King Jr. Drive, Cleveland, Ohio 44106, United States.
Patrick
P
Leo
Case Western Reserve University , Department of Biomedical Engineering, 2071 Martin Luther King Jr. Drive, Cleveland, Ohio 44106, United States.
George
G
Lee
University of Pennsylvania , Department of Pathology, 3400 Spruce Street, Philadelphia, Pennsylvania 19104, United States.
Natalie N C
NN
Shih
Case Western Reserve University , Department of Pathology, 11100 Euclid Avenue, Cleveland, Ohio 44106, United States.
Robin
R
Elliott
University of Pennsylvania , Department of Pathology, 3400 Spruce Street, Philadelphia, Pennsylvania 19104, United States.
Michael D
MD
Feldman
Case Western Reserve University , Department of Biomedical Engineering, 2071 Martin Luther King Jr. Drive, Cleveland, Ohio 44106, United States.
Anant
A
Madabhushi
digital pathology; feature stability; machine learning; prognosis; prostate cancer; quantitative histomorphometry; site variation; stain variability