Learning discriminative classification models for grading anal intraepithelial neoplasia

Grading intraepithelial neoplasia is crucial to derive an accurate estimate of pre-cancerous stages and is currently performed by pathologists assessing histopathological images. Inter- and intra-observer variability can significantly be reduced, when reliable, quantitative image analysis is introduced into diagnostic processes. On a challenging dataset, we evaluated the potential of learning a classifier to grade anal intraepitelial neoplasia. Support vector machines were trained on images represented by fractal and statistical features. We show that pursuing a learning-based grading strategy yields highly reliable results. Compared to existing methods, the proposed method outperformed them by a significant margin

Acute dyspnea caused by a giant spindle cell lipoma of the larynx

Lipomas are common benign mesenchymal neoplasms. Although 13% of lipomas are found in the head and neck, only 0.6% have been reported in the larynx. Of all lipomas, the spindle cell variant is the least common. In the present study, we report a case of supraglottic spindle cell lipoma and review the literature of laryngeal spindle cell lipoma. A 35-year-old male presented with dysphagia and dyspnea and was found to have bilateral supraglottic lesions causing airway obstruction. The masses were resected endoscopically. Final pathology demonstrated mature adipocytes and spindle cells, with immunohistochemical patterns supportive of spindle cell lipoma. Spindle cell lipomas have rarely been reported in the upper airway. To our knowledge, this is the youngest patient reported to date. These lipomas are uncommon benign neoplasms and should be distinguished from aggressive mesenchymal neoplasms such as liposarcoma variants to guide appropriate conservative but curative therapy