Abstract
Cancer involves histological changes in tissue, which is of primary importance in pathological diagnosis and research. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue with all its variables. On the other hand, understanding connections between genetic alterations and histological attributes requires development of enhanced analysis methods suitable also for small sample sizes. Here, we set out to develop computational methods for early detection and distinction of prostate cancer-related pathological alterations. We use analysis of features from HE stained histological images of normal mouse prostate epithelium, distinguishing the descriptors for variability between ventral, lateral, and dorsal lobes. In addition, we use two common prostate cancer models, Hi-Myc and Pten+/− mice, to build a feature-based machine learning model separating the early pathological lesions provoked by these genetic alterations. This work offers a set of computational methods for separation of early neoplastic lesions in the prostates of model mice, and provides proof-of-principle for linking specific tumor genotypes to quantitative histological characteristics. The results obtained show that separation between different spatial locations within the organ, as well as classification between histologies linked to different genetic backgrounds, can be performed with very high specificity and sensitivity.
Highlights
Characteristics of the tissue itself, or technical variation due to e.g. orientation of the histological section cut relative to tissue structures
While computational image informatics can provide a plethora of quantified descriptors of a given image, the challenge in histology is to sort out the relevant characteristics which can be presented in the form of useful feature representations
With a computational separation of hundreds of features from the whole slide images of histological tissue sections and a random forest based machine learning approach, we find a combination of tissue features able to distinguish between 1) normal spatial heterogeneity in the prostate tissue, 2) early Pten+/−or Hi-Myc-induced neoplasms from normal tissue, and 3) the two types of neoplasms from each other
Summary
Characteristics of the tissue itself, or technical variation due to e.g. orientation of the histological section cut relative to tissue structures. Feature-based analysis combined with supervised learning has been a common approach in decision support systems and computer aided diagnosis based on whole slide images1,5,6,. Such approaches have been successfully used for quantitatively describing characteristics of prostate histology in neoplastic lesions both for a mouse model[7] and for human tissue[8]. With a computational separation of hundreds of features from the whole slide images of histological tissue sections and a random forest based machine learning approach, we find a combination of tissue features able to distinguish between 1) normal spatial heterogeneity in the prostate tissue, 2) early Pten+/−or Hi-Myc-induced neoplasms from normal tissue, and 3) the two types of neoplasms from each other. Our study serves as the first step towards developing tools for automated analysis of early neoplastic changes in prostate tissue and their linkage to different genetic groups
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