Differential diagnosis of thyroid nodule capsules using random forest guided selection of image features

Abstract

Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital pathology is gaining momentum providing the pathologist with additional cues to traditional routes when placing a diagnosis, therefore it is extremely important to develop new image analysis methods that can extract image features with diagnostic potential. In this work, we use histogram and texture analysis to extract features from microscopic images acquired on thin thyroid nodule capsules sections and demonstrate how they enable the differential diagnosis of thyroid nodules. Targeted thyroid nodules are benign (i.e., follicular adenoma) and malignant (i.e., papillary thyroid carcinoma and its sub-type arising within a follicular adenoma). Our results show that the considered image features can enable the quantitative characterization of the collagen capsule surrounding thyroid nodules and provide an accurate classification of the latter’s type using random forest.

Deep learning-based diagnosis of thyroid tumors using histopathology images from thyroid nodule capsule

“…Studies have shown the usefulness of histopathology images from thyroid nodule capsules for the differential diagnosis of thyroid tumors. 1 However, there is a lack of studies on the utility of deep learning methods for the analysis of histopathology images from thyroid nodule capsules. Also, the relative usefulness of such histopathology images over the histopathology images from thyroid nodules has not been investigated.…”