A giant ectopic hidradenoma papilliferum in a Niger delta region of Nigeria

Hidradenoma papilliferum is a known example of adnexal skin tumours with apocrine differentiation. It is a rare benign tumour which tends to arise from areas with rich concentration of aporine glands such as anogenital region, vulval, perineal, axillae, and periumbilical areas. In this report, the tumour was found in the upper outer quadrant of left breast, being one of the ectopic sites for this tumour. Contrary to most reports where male preponderance was popular for ectopic hidradenoma papilliferum, the patient in this report is a 71-year-old female. Considering the location of this tumour in this report, the likely histopathological differential diagnoses such as tubular apocrine adenoma, clear cell (apocrine) adenoma, lipoma, intraductal papilloma and papillary carcinoma of the breast should be considered for exclusion. This is the first reported case of a giant ectopic hidradenoma papilliferum of the breast in a Niger Delta region of Nigeria which also highlights the role of fine needle aspiration and cytology in the diagnosis of breast lesions

Diagnosis of breast cancer using elastic-scattering spectroscopy: preliminary clinical results

We report on the first stages of a clinical study designed to test elastic-scattering spectroscopy, mediated by fiberoptic probes, for three specific clinical applications in breast-tissue diagnosis: (1) a transdermal-needle (interstitial) measurement for instant diagnosis with minimal invasiveness similar to fine-needle aspiration but with sensitivity to a larger tissue volume, (2) a hand-held diagnostic probe for use in assessing tumor/resection margins during open surgery, and (3) use of the same probe for real-time assessment of the `sentinel' node during surgery to determine the presence or absence of tumor (metastatic). Preliminary results from in vivo measurements on 31 women are encouraging. Optical spectra were measured on 72 histology sites in breast tissue, and 54 histology sites in sentinel nodes. Two different artificial intelligence methods of spectral classification were studied. Artificial neural networks yielded sensitivities of 69% and 58%, and specificities of 85% and 93%, for breast tissue and sentinel nodes, respectively. Hierarchical cluster analysis yielded sensitivities of 67% and 91%, and specificities of 79% and 77%, for breast tissue and sentinel nodes, respectively. These values are expected to improve as the data sets continue to grow and more sophisticated data preprocessing is employed. The study will enroll up to 400 patients over the next two years