Gain of hTERC: a genetic marker of malignancy in oral potentially malignant lesions.

Oral squamous cell carcinoma (OSCC) is the most common oral cancer, and major efforts is being made to identify molecular markers capable to differentiate oral potentially malignant lesions (OPMLs) with indolent course from lesions with aggressive behavior. We undertook a study to evaluate if gain of the human telomerase RNA component (hTERC) gene in OPMLs could indicate lesions at high risk of developing OSCC. The study was performed on 30 OPMLs with long-term follow-up using a dual-color interphase fluorescence in situ hybridization (FISH) for hTERC status. Progression to malignancy was observed in 9 of 10 cases harboring hTERC gain and in 1 of 20 cases retaining a normal copy number of hTERC (P < .0001). Combining morphological grading and FISH analysis, all the cases with high-grade squamous intraepithelial lesion or carcinoma in situ harboring hTERC amplification progressed to OSCC, whereas none of the low-grade squamous intraepithelial lesions without hTERC gain progressed. Intermediate situations occurred. The data suggest that precise morphological evaluation together with FISH assessment for hTERC gain might pave the way to stratify OPMLs into high-risk and low-risk categories and could be helpful in selecting the most appropriate treatment.

Primary angiosarcoma of the parotid gland arising from benign congenital hemangioma.

A case of malignant transformation of a benign congenital hemangioma of the parotid gland is presented. The malignant tumor occurred in a woman with a history of congenital hemangioma surgically removed 8 years previously. No radiotherapy had been administered at the time of primary excision. The recurrent tumor consisted of a large lesion occupying nearly all the parotid gland and infiltrating the surrounding soft tissues and overlying skin. Its histopathologic features were typical of epithelioid angiosarcoma. The vast majority of vascular lesions of major salivary glands are benign. However, pathologists should be aware of the remote possibility of malignant transformation in these lesions.

Classification and prediction of the progression of thyroid-associated ophthalmopathy by an artificial neural network.

OBJECTIVE: We have used an artificial neural network in an attempt to classify and predict the progression of thyroid-associated ophthalmopathy (TAO) at the first clinical examination. DESIGN: This retrospective comparative case series included a group of patients examined by the ophthalmologist only once because of the absence of signs of progressive disease (GR1), as subsequently monitored by an endocrinologist, and a group of patients on follow-up because of progressive disease (GR2). PARTICIPANTS AND METHODS: We examined 242 patients, of whom 207 were women and 35 were men. GR1 included 129 patients (257 eyes) who, on ophthalmologic assessment, were further classified as having no TAO (n = 53; GR1a) and only lid signs or inactive, stable TAO (n = 76; GR1b). GR2 included 113 patients (219 eyes). One hundred three normal subjects (205 eyes), 50 women and 53 men, were tested to provide normal ranges for proptosis values. We applied a model of back propagation neural network with 17 input variables, a training matrix of 414 observations, a randomly selected test group of 115 observations, and, as output, the progression of disease. The ophthalmologic assessment included (1) lid fissure measurement, (2) Hertel, (3) color vision, (4) cover test and Hess screen, (5) visual acuity, (6) tonometry, (7) fundus examination, (8) visual field, and (9) orbital computed tomography scan or ultrasonography. Other parameters included in the neural analysis were gender and age of the patients, their cigarette smoking, and the interval between follow-up visits. RESULTS: The prevalence of smokers among patients without TAO was significantly lower than that among those with TAO (P < 0.03). Mean proptosis values (Hertel) were significantly different in GR1, in GR2, and in a group of normal eyes (P < 0.0001), and the changes of values in consecutive measurements were associated with progression of the disease (P < 0.01). Differences of the proptosis values in the two groups of patients were not related to smoking. The neural network correctly classified 78.3% of 115 eyes (87 patients) and predicted TAO progression in 69.2% of 39 eyes (28 patients). CONCLUSIONS: In our opinion, neural network analysis can be successfully applied for classifying TAO and predicting progression at the first clinical examination.

Prediction of the progression of thyroid-associated ophthalmopathy at first ophthalmologic examination: use of a neural network.

In the present work we analyzed patients with thyroid-associated ophthalmopathy (TAO) at various clinical stages of disease progression and implemented a model of neural analysis for disease classification and prediction of progression. We studied 246 patients (group 1), seen only once because they had absent, minimal, or inactive TAO and 152 patients (group 2), seen two or more times because of active and/or progressive TAO. The ophthalmologic assessment included: (1) lid fissure measurement; (2) Hertel; (3) color vision; (4) cover test and Hess screen; (5) visual acuity; (6) tonometry; (7) fundus examination; (8) visual field; (9) orbital computed tomography (CT) scan or ultrasound. A back propagation model of neural network was based on the relative variations of 13 clinical eye signs (input variables) for classification and prediction of disease progression (output variable). Approximately 300 eyes (20%) were randomly selected as a test group. Correlation between expected and calculated patients' classification was highly significant (p < 0.00001). Concordance between clinical assessment and the neural network prediction was obtained in 78 of 117 eyes (67%). We have developed a neural model that allows classification of TAO and preliminary prediction of disease progression at the first clinical examination. The results are validating the classification into the two groups on which our initial assumption was based.