Computationally-guided histologic hypothesis generation. (A) Left: unprocessed H&E tissue microarray (TMA) image of lung squamous cell carcinoma. Middle-left: using labeled examples of tumor stroma and epithelia provided by one author, the software learned to divide regions of all TMA images into epithelia (orange) or stroma (blue). The computational classification was correct in many instances, but did have trouble distinguishing inflamed epithelium from inflamed stroma (typically calling all such areas stroma). Middle-right: epithelial objects (yellow = small; orange = medium; brown = large objects); while nearly every tumor nuclei is correctly accounted for, the cytoplasmic borders of some tumors were not appropriately captured, thereby underestimating the extent of at least some tumor cells (i.e. in solid tumor nests [orange areas], there should few to no gaps between epithelial objects). Right: stromal objects. 768 epithelial and 768 stromal features were quantified by the software for each image. After combining with clinical data, four computationally-measured stromal (i.e. from the blue regions) features were found to be significantly associated with overall survival at a cut-off false discovery rate of <0.05. No features from the epithelia (orange region) was significant at this cutoff. (B) The four significant features and representative images from the highest and lowest ranked cases (for illustration, only one of the two images for each case) is shown. Three of the four significant features were associated with the amount of stromal lymphoplasmacytic inflammation by visual review, where more inflammation was associated with better prognosis. A more complete manual review of the four features is available in the Supplementary Data (for Features 1–4).