Granular cell tumor of the tongue in a 6-year-old girl : A case report

Granular cell tumor is a relatively uncommon benign hamartomatous lesion occurring in almost any part of the body. The tongue and the buccal mucosa are common intra oral sites. Granular cell lesions may be found in other diverse sites such as the jaw, skin, gastro intestinal tract and respiratory tract. The histogenesis of the lesion still remains unknown. However, histochemical and ultra structural studies propose the origin of the lesion from schwann cells, striated muscle, mesenchymal cells, histiocytes and epithelial cells. The tumor generally occurs in middle or older aged adults. The lesion is typically seen as an uninflammed asymptomatic mass measuring about two cms in diameter with a yellowish surface coloration. As most of the granular cell tumors are benign, surgical excision of the lesion is the treatment of choice. We describe a case of granular cell tumor of the tongue in a 6 year old girl along with a brief review of literature on granular cell tumors

Multiple myeloma of the jaw: A case report

Multiple myeloma is a systemic B-cell lymphoproliferative disease that causes osteolytic lesions in the vertebra, ribs, pelvic bone, skull and jaw. Rarely jaw lesions are seen as the first sign in multiple myeloma. This is a case report with follow up of a 57-year-old female patient, previously treated for osteoporosis, who presented with a swelling of the jaw. On radiographic examination, she was found to have osteolytic lesions in the mandible and skull bones. These conventional aids led to the diagnosis of multiple myeloma thereby proving that the osteoporotic lesions were a part of the spectrum of multiple myeloma. The patient underwent chemotherapy and is currently on follow-up. This case report emphasizes the importance of early diagnosis of multiple myeloma in the jaw using readily available technologies and illustrates the contribution that oral assessment can provide

Validation of a Point-of-Care Optical Coherence Tomography Device with Machine Learning Algorithm for Detection of Oral Potentially Malignant and Malignant Lesions.

Non-invasive strategies that can identify oral malignant and dysplastic oral potentially-malignant lesions (OPML) are necessary in cancer screening and long-term surveillance. Optical coherence tomography (OCT) can be a rapid, real time and non-invasive imaging method for frequent patient surveillance. Here, we report the validation of a portable, robust OCT device in 232 patients (lesions: 347) in different clinical settings. The device deployed with algorithm-based automated diagnosis, showed efficacy in delineation of oral benign and normal (n = 151), OPML (n = 121), and malignant lesions (n = 75) in community and tertiary care settings. This study showed that OCT images analyzed by automated image processing algorithm could distinguish the dysplastic-OPML and malignant lesions with a sensitivity of 95% and 93%, respectively. Furthermore, we explored the ability of multiple (n = 14) artificial neural network (ANN) based feature extraction techniques for delineation high grade-OPML (moderate/severe dysplasia). The support vector machine (SVM) model built over ANN, delineated high-grade dysplasia with sensitivity of 83%, which in turn, can be employed to triage patients for tertiary care. The study provides evidence towards the utility of the robust and low-cost OCT instrument as a point-of-care device in resource-constrained settings and the potential clinical application of device in screening and surveillance of oral cancer

Validation of a Point-of-Care Optical Coherence Tomography Device with Machine Learning Algorithm for Detection of Oral Potentially Malignant and Malignant Lesions.

Non-invasive strategies that can identify oral malignant and dysplastic oral potentially-malignant lesions (OPML) are necessary in cancer screening and long-term surveillance. Optical coherence tomography (OCT) can be a rapid, real time and non-invasive imaging method for frequent patient surveillance. Here, we report the validation of a portable, robust OCT device in 232 patients (lesions: 347) in different clinical settings. The device deployed with algorithm-based automated diagnosis, showed efficacy in delineation of oral benign and normal (n = 151), OPML (n = 121), and malignant lesions (n = 75) in community and tertiary care settings. This study showed that OCT images analyzed by automated image processing algorithm could distinguish the dysplastic-OPML and malignant lesions with a sensitivity of 95% and 93%, respectively. Furthermore, we explored the ability of multiple (n = 14) artificial neural network (ANN) based feature extraction techniques for delineation high grade-OPML (moderate/severe dysplasia). The support vector machine (SVM) model built over ANN, delineated high-grade dysplasia with sensitivity of 83%, which in turn, can be employed to triage patients for tertiary care. The study provides evidence towards the utility of the robust and low-cost OCT instrument as a point-of-care device in resource-constrained settings and the potential clinical application of device in screening and surveillance of oral cancer