Literature DB >> 25756148

Identifying predictive morphologic features of malignancy in eyelid lesions.

Christina Leung, Davin Johnson, Renee Pang, Vladimir Kratky.   

Abstract

OBJECTIVE: To determine features of eyelid lesions most predictive of malignancy, and to design a key to assist general practitioners in the triaging of such lesions.
DESIGN: Prospective observational study.
SETTING: Department of Ophthalmology at Queen’s University in Kingston, Ont. PARTICIPANTS: A total of 199 consecutive periocular lesions requiring biopsy or excision were included. MAIN OUTCOME MEASURES: First, potential features suggestive of malignancy for eyelid lesions were identified based on a survey sent to Canadian oculoplastic surgeons. The sensitivity, specificity, and odds ratios (ORs) of these features were then determined using 199 consecutive photographed eyelid lesions of patients who presented to the Department of Ophthalmology and underwent biopsy or excision. A triage key was then created based on the features with the highest ORs, and it was pilot-tested by a group of medical students.
RESULTS: Of the 199 lesions included, 161 (80.9%) were benign and 38 (19.1%) were malignant. The 3 features with the highest ORs in predicting malignancy were infiltration (OR = 18.2, P < .01), ulceration (OR = 14.7, P < .01), and loss of eyelashes (OR = 6.0, P < .01). The acronym LUI (loss of eyelashes, ulceration, infiltration) was created to assist in memory recall. After watching a video describing the LUI triage key, the mean total score of a group of medical students for correctly identifying malignant lesions increased from 46% to 70% (P < .001).
CONCLUSION: Differentiating benign from malignant eyelid lesions can be difficult even for experienced physicians. The LUI triage key provides physicians with an evidence-based, easy-to-remember system for assisting in the triaging of these lesions.

Entities:  

Mesh:

Year:  2015        PMID: 25756148      PMCID: PMC4301785     

Source DB:  PubMed          Journal:  Can Fam Physician        ISSN: 0008-350X            Impact factor:   3.275


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