Prediction without Pigment: a decision algorithm for non-pigmented skin malignancy
Cliff Rosendahl1, Alan Cameron1, Philipp Tschandl2
1School of Medicine, The University of Queensland, Australia.
Abstract:
While there are several published comprehensive stepwise algorithmic methods for diagnosing pigmented skin malignancy, only limited material has been published for the stepwise assessment of non-pigmented lesions. We present a method based on pattern analysis, with a stepwise assessment, first, for ulceration, second, for white clues (defined as white lines, or in the case of a raised lesion any of the keratin clues: dermatoscopic white circles, dermatoscopic white structureless areas or surface keratin), and third, if no ulceration or white clues are present, proceed to vessel pattern analysis. This is a novel method, and apart from the assessment of white clues in raised lesions, it has not been formally tested. The priority of keratin clues in raised lesions over vessel pattern analysis has, however, been verified. It is conceded that this method is less specific than methods which have clues of pigmented structures, and accepting these limitations, Prediction without Pigment is a decision algorithm intended to guide the clinician in the decision as to whether to perform a biopsy rather than consistently leading to a specific diagnosis. Reaching a more specific diagnosis at the end of our flowchart can be achieved by weighing of clues both clinical and dermatoscopic, and that ability can be expected to improve with both knowledge and experience, but no diagnostic method, including this one, can be 100% sensitive in diagnosing malignancy, in particular, melanoma. Taking these limitations into account, any non-pigmented lesion, regardless of pattern analysis, which is raised and firm (nodular) and for which a confident, specific benign diagnosis cannot be made, should be excised to exclude the nodular variant of amelanotic melanoma.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
06:08Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
Published on: May 5, 2011
Related Concept Videos
Skin Cancer
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
Pigmentation
Melanin occurs in two primary forms: eumelanin that provides black and brown pigment and pheomelanin that provides red color. Dark-skinned individuals produce more melanin than those with pale...
Changes in Skin Color: Clinical Perspectives
Albinism
Albinism is a genetic disorder that affects (completely or partially) the coloring of skin, hair, and eyes. The defect is primarily...
