Quantifying Histological Features of Cancer Biospecimens for Biobanking Quality Assurance Using Automated Morphometric Pattern Recognition Image Analysis Algorithms

Biorepository-supported translational research depends on high-quality, well-annotated specimens. Histopathology assessment contributes insight into how representative lesions are for research objectives. Feasibility of documenting histological proportions of tumor and stroma was studied in an effort to enhance information regarding biorepository tissue heterogeneity. Using commercially available software, unique spatial-spectral algorithms were developed for applying automated pattern recognition morphometric image analysis to quantify histologic tumor and nontumor tissue areas in biospecimen tissue sections. Measurements were acquired successfully for 75/75 (100%) lymphomas, 76/77 (98.7%) osteosarcomas, and 60/70 (85.7%) melanomas. The percentage of tissue area occupied by tumor varied among patients and tumor types and was distributed around medians of 94% [interquartile range (IQR)=14%] for lymphomas, 84% for melanomas (IQR=24%), and 39% for osteosarcomas (IQR=44%). Within-patient comparisons from a subset, including multiple individual patient specimens, revealed ≤12% median coefficient of variation (CV) for lymphomas and melanomas. Phenotypic heterogeneity of osteosarcomas resulted in 33% median CV. Uniformly applied, tumor-specific pattern recognition software permits automated tissue-feature quantification. Furthermore, dispersion analyses of area measurements across collections, as well as of multiple specimens from individual patients, support using limited tissue slices to gauge features for some tumor types. Quantitative image analysis automation is anticipated to minimize variability associated with routine biorepository pathologic evaluations and enhance biomarker discovery by helping to guide the selection of study-appropriate specimens.