Incidental detection of an occult oral malignancy with autofluorescence imaging: a case report

BACKGROUND: Autofluorescence imaging is used widely for diagnostic evaluation of various epithelial malignancies. Cancerous lesions display loss of autofluorescence due to malignant changes in epithelium and subepithelial stroma. Carcinoma of unknown primary site presents with lymph node or distant metastasis, for which the site of primary tumour is not detectable. We describe here the use of autofluorescence imaging for detecting a clinically innocuous appearing occult malignancy of the palate which upon pathological examination was consistent with a metastatic squamous cell carcinoma. CASE DESCRIPTION: A submucosal nodule was noted on the right posterior hard palate of a 59-year-old white female during clinical examination. Examination of this lesion using a multispectral oral cancer screening device revealed loss of autofluorescence at 405 nm illumination. An excisional biopsy of this nodule, confirmed the presence of a metastatic squamous cell carcinoma. Four years ago, this patient was diagnosed with metastatic squamous cell carcinoma of the right mid-jugular lymph node of unknown primary. She was treated with external beam irradiation and remained disease free until current presentation. CONCLUSION: This case illustrates the important role played by autofluorescence tissue imaging in diagnosing a metastatic palatal tumour that appeared clinically innocuous and otherwise would not have been biopsied

Model-based clustering and classification with non-normal mixture distributions

Non-normal mixture distributions have received increasing attention in recent years. Finite mixtures of multivariate skew-symmetric distributions, in particular, the skew normal and skew -mixture models, are emerging as promising extensions to the traditional normal and -mixture models. Most of these parametric families of skew distributions are closely related, and can be classified into four forms under a recently proposed scheme, namely, the restricted, unrestricted, extended, and generalised forms. In this paper, we consider some of these existing proposals of multivariate non-normal mixture models and illustrate their practical use in several real applications. We first discuss the characterizations along with a brief account of some distributions belonging to the above classification scheme, then references for software implementation of EM-type algorithms for the estimation of the model parameters are given. We then compare the relative performance of restricted and unrestricted skew mixture models in clustering, discriminant analysis, and density estimation on six real datasets from flow cytometry, finance, and image analysis. We also compare the performance of mixtures of skew normal and -component distributions with other non-normal component distributions, including mixtures with multivariate normal-inverse-Gaussian distributions, shifted asymmetric Laplace distributions and generalized hyperbolic distributions

Multitude of Laplace distributions

Application areas, Laplace distributions, Maximum likelihood estimation,