Watermelon Stomach, Hemorrhagic Pericarditis, Small Cell Carcinoma of the Lung and Synchronous Squamous Cell Carcinoma of the Tongue Base

Based on a case of gastric antral vascular ectasia (watermelon stomach) that was associated with hemorrhagic pericarditis, small cell lung carcinoma with mediastinal lymph node metastases and a synchronous squamous cell carcinoma of the base of the tongue, the authors made a review of the clinical, endoscopic and histopathological aspects of this type of gastropathy, and its association with other diseases, and of the results of its endoscopic therapy. The causes of hemorrhagic pericarditis are considered, emphasizing the necessity to know if the effusion has a malignant etiology. To the best of our knowledge the association of watermelon stomach to small cell lung carcinoma and squamous cell carcinoma of the base of the tongue has not yet been described. Extensive metastases to mediastal lymph nodes are common to small cell lung carcinoma.

Automatic Small Bowel Tumor Diagnosis by Using Multi-Scale Wavelet-Based Analysis in Wireless Capsule Endoscopy Images

BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.