Abstract
The Odontogenic keratocyst (OKC) is a cystic lesion of the jaws, which has high growth and recurrence rates compared to other cysts of the jaws (for instance, radicular cyst, which is the most common jaw cyst type). For this reason OKCs are considered by some to be benign neoplasms. There exist two sub-types of OKCs (sporadic and syndromic) and the ability to discriminate between these sub-types, as well as other jaw cysts, is an important task in terms of disease diagnosis and prognosis. With the development of digital pathology, computational algorithms have become central to addressing this type of problem. Considering that only basic feature-based methods have been investigated in this problem before, we propose to use a different approach (the Bouligand–Minkowski descriptors) to assess the success rates achieved on the classification of a database of histological images of the epithelial lining of these cysts. This does not require the level of abstraction necessary to extract histologically-relevant features and therefore has the potential of being more robust than previous approaches. The descriptors were obtained by mapping pixel intensities into a three dimensional cloud of points in discrete space and applying morphological dilations with spheres of increasing radii. The descriptors were computed from the volume of the dilated set and submitted to a machine learning algorithm to classify the samples into diagnostic groups. This approach was capable of discriminating between OKCs and radicular cysts in 98% of images (100% of cases) and between the two sub-types of OKCs in 68% of images (71% of cases). These results improve over previously reported classification rates reported elsewhere and suggest that Bouligand–Minkowski descriptors are useful features to be used in histopathological images of these cysts.
Highlights
Cysts are pathological cavities containing fluid or semi-fluid content and lined by epithelial tissue
The Odontogenic keratocyst (OKC) is a cystic lesion of the jaws, which has high growth and recurrence rates compared to other cysts of the jaws
Considering that only basic feature-based methods have been investigated in this problem before, we propose to use a different approach to assess the success rates achieved on the classification of a database of histological images of the epithelial lining of these cysts
Summary
Cysts are pathological cavities containing fluid or semi-fluid content and lined by epithelial tissue. OKCs are less frequent (12% of odontogenic cysts [10]), they are not associated with dental disease and have certain characteristics common with neoplasms (e.g. active epithelial growth [21,14,15] and higher recurrence rates). There are two OKC sub-types; they can be solitary cysts (sporadic), or they can be multiple (synchronous or metachronous) as part of a rare autosomal dominant disease, the Gorlin-Goltz or Basal Cell Naevus Syndrome (BCNS) [11,20]. About 85% of syndromic and 30% of sporadic cases have mutations of the PTCH1 gene [13], ( to basal cell carcinomas of skin) indicating a potential common pathogenesis across OKC sub-types. There seem to be differences in the behaviour of syndromic and sporadic OKCs too, and any morphological evidence that could help differentiating between the two sub-types is of diagnostic and predictive importance
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