BACKGROUND: In vivo confocal laser scanning microscopy (CLSM) represents a novel imaging tool that allows the noninvasive examination of skin cancer morphology in real time at a 'quasi-histopathological' resolution viewing microanatomical structures and individual cells. OBJECTIVES: To validate diagnostic confocal examination of melanocytic skin tumours using unselected tumour images. METHODS: In the present study, we used a total of 3709 unselected CLSM tumour images obtained from 20 malignant melanomas and 50 benign naevi. The entire set of images derived from each tumour was evaluated by independent observers. Classification tree analysis based on a subsample of 857 tumour images was performed to develop a diagnostic algorithm. RESULTS: Overall, sensitivity and specificity of 97.5% and 99% could be achieved by the independent observers (positive predictive value 97.5%, negative predictive value 99%). Classification tree analysis yielded a three-step algorithm based on only three morphological CLSM features, facilitating a correct classification in 92.4% of the benign naevus images and 97.6% of melanoma images. CONCLUSIONS: In vivo CLSM augurs a sea change in the way we will view skin tumour processes clinically at the bedside and merits application for use as a screening tool in skin oncology.
BACKGROUND: In vivo confocal laser scanning microscopy (CLSM) represents a novel imaging tool that allows the noninvasive examination of skin cancer morphology in real time at a 'quasi-histopathological' resolution viewing microanatomical structures and individual cells. OBJECTIVES: To validate diagnostic confocal examination of melanocytic skin tumours using unselected tumour images. METHODS: In the present study, we used a total of 3709 unselected CLSM tumour images obtained from 20 malignant melanomas and 50 benign naevi. The entire set of images derived from each tumour was evaluated by independent observers. Classification tree analysis based on a subsample of 857 tumour images was performed to develop a diagnostic algorithm. RESULTS: Overall, sensitivity and specificity of 97.5% and 99% could be achieved by the independent observers (positive predictive value 97.5%, negative predictive value 99%). Classification tree analysis yielded a three-step algorithm based on only three morphological CLSM features, facilitating a correct classification in 92.4% of the benign naevus images and 97.6% of melanoma images. CONCLUSIONS: In vivo CLSM augurs a sea change in the way we will view skin tumour processes clinically at the bedside and merits application for use as a screening tool in skin oncology.
Authors: Milind Rajadhyaksha; Ashfaq Marghoob; Anthony Rossi; Allan C Halpern; Kishwer S Nehal Journal: Lasers Surg Med Date: 2016-10-27 Impact factor: 4.025
Authors: Lynn Dengel; Kristin Turza; Mary-Margaret B Noland; James W Patterson; Craig L Slingluff Journal: Ann Surg Oncol Date: 2008-09-12 Impact factor: 5.344