title: Automatic detection of melanoma progression by histological analysis of secondary sites.
abstract: We present results from machine classification of melanoma biopsies sectioned and stained with hematoxylin/eosin (H&E) on tissue microarrays (TMA). The four stages of melanoma progression were represented by seven tissue types, including benign nevus, primary tumors with radial and vertical growth patterns (stage I) and four secondary metastatic tumors: subcutaneous (stage II), lymph node (stage III), gastrointestinal and soft tissue (stage IV). Our experiment setup comprised 14,208 image samples based on 164 TMA cores. In our experiments, we constructed an HE color space by digitally deconvolving the RGB images into separate H (hematoxylin) and E (eosin) channels. We also compared three different classifiers: Weighted Neighbor Distance (WND), Radial Basis Functions (RBF), and k-Nearest Neighbors (kNN). We found that the HE color space consistently outperformed other color spaces with all three classifiers, while the different classifiers did not have as large of an effect on accuracy. This showed that a more physiologically relevant representation of color can have a larger effect on correct image interpretation than downstream processing steps. We were able to correctly classify individual fields of view with an average of 96% accuracy when randomly splitting the dataset into training and test fields. We also obtained a classification accuracy of 100% when testing entire cores that were not previously used in training (four random trials with one test core for each of 7 classes, 28 tests total). Because each core corresponded to a different patient, this test more closely mimics a clinically relevant setting where new patients are evaluated based on training with previous cases. The analysis method used in this study contains no parameters or adjustments that are specific to melanoma morphology, suggesting it can be used for analyzing other tissues and phenotypes, as well as potentially different image modalities and contrast techniques.
authors.affiliation: National Institution on Aging, NIH, Laboratory of Genetics, Baltimore, Maryland, USA. norlov@nih.gov
authors.forename: Nikita V
authors.initials: NV
authors.lastname: Orlov
authors.forename: Ashani T
authors.initials: AT
authors.lastname: Weeraratna
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Hewitt
authors.forename: Christopher E
authors.initials: CE
authors.lastname: Coletta
authors.forename: John D
authors.initials: JD
authors.lastname: Delaney
authors.forename: D
authors.initials: D
authors.lastname: Mark Eckley
authors.forename: Lior
authors.initials: L
authors.lastname: Shamir
authors.forename: Ilya G
authors.initials: IG
authors.lastname: Goldberg
mesh_terms: D018450:Disease Progression; D006652:Histological Techniques; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000473:pathology* / Q000556:secondary*; D009362:Neoplasm Metastasis; D009367:Neoplasm Staging; D010363:Pattern Recognition, Automated / Q000379:methods; D012878:Skin Neoplasms / Q000473:pathology* / Q000556:secondary*; D013194:Staining and Labeling; D046888:Tissue Array Analysis