Laura K Ferris1, Jan A Harkes2, Benjamin Gilbert2, Daniel G Winger3, Kseniya Golubets4, Oleg Akilov4, Mahadev Satyanarayanan2. 1. Department of Dermatology, University of Pittsburgh, Pittsburgh, Pennsylvania. Electronic address: ferrislk@upmc.edu. 2. Carnegie Mellon University, Computer Science Department, Pittsburgh, Pennsylvania. 3. Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania. 4. Department of Dermatology, University of Pittsburgh, Pittsburgh, Pennsylvania.
Abstract
BACKGROUND: Computer-assisted diagnosis of dermoscopic images of skin lesions has the potential to improve melanoma early detection. OBJECTIVE: We sought to evaluate the performance of a novel classifier that uses decision forest classification of dermoscopic images to generate a lesion severity score. METHODS: Severity scores were calculated for 173 dermoscopic images of skin lesions with known histologic diagnosis (39 melanomas, 14 nonmelanoma skin cancers, and 120 benign lesions). A threshold score was used to measure classifier sensitivity and specificity. A reader study was conducted to compare the sensitivity and specificity of the classifier with those of 30 dermatology clinicians. RESULTS: The classifier sensitivity for melanoma was 97.4%; specificity was 44.2% in a test set of images. In the reader study, the classifier's sensitivity to melanoma was higher (P < .001) and specificity was lower (P < .001) than that of clinicians. LIMITATIONS: This is a retrospective study using existing images primarily chosen for biopsy by a dermatologist. The size of the test set is small. CONCLUSIONS: Our classifier may aid clinicians in deciding if a skin lesion should be biopsied and can easily be incorporated into a portable tool (that uses no proprietary equipment) that could aid clinicians in noninvasively evaluating cutaneous lesions.
BACKGROUND: Computer-assisted diagnosis of dermoscopic images of skin lesions has the potential to improve melanoma early detection. OBJECTIVE: We sought to evaluate the performance of a novel classifier that uses decision forest classification of dermoscopic images to generate a lesion severity score. METHODS: Severity scores were calculated for 173 dermoscopic images of skin lesions with known histologic diagnosis (39 melanomas, 14 nonmelanoma skin cancers, and 120 benign lesions). A threshold score was used to measure classifier sensitivity and specificity. A reader study was conducted to compare the sensitivity and specificity of the classifier with those of 30 dermatology clinicians. RESULTS: The classifier sensitivity for melanoma was 97.4%; specificity was 44.2% in a test set of images. In the reader study, the classifier's sensitivity to melanoma was higher (P < .001) and specificity was lower (P < .001) than that of clinicians. LIMITATIONS: This is a retrospective study using existing images primarily chosen for biopsy by a dermatologist. The size of the test set is small. CONCLUSIONS: Our classifier may aid clinicians in deciding if a skin lesion should be biopsied and can easily be incorporated into a portable tool (that uses no proprietary equipment) that could aid clinicians in noninvasively evaluating cutaneous lesions.
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Authors: Cristian Navarrete-Dechent; Stephen W Dusza; Konstantinos Liopyris; Ashfaq A Marghoob; Allan C Halpern; Michael A Marchetti Journal: J Invest Dermatol Date: 2018-06-01 Impact factor: 8.551
Authors: Michael A Marchetti; Noel C F Codella; Stephen W Dusza; David A Gutman; Brian Helba; Aadi Kalloo; Nabin Mishra; Cristina Carrera; M Emre Celebi; Jennifer L DeFazio; Natalia Jaimes; Ashfaq A Marghoob; Elizabeth Quigley; Alon Scope; Oriol Yélamos; Allan C Halpern Journal: J Am Acad Dermatol Date: 2017-09-29 Impact factor: 11.527
Authors: Jacqueline Dinnes; Jonathan J Deeks; Naomi Chuchu; Rubeta N Matin; Kai Yuen Wong; Roger Benjamin Aldridge; Alana Durack; Abha Gulati; Sue Ann Chan; Louise Johnston; Susan E Bayliss; Jo Leonardi-Bee; Yemisi Takwoingi; Clare Davenport; Colette O'Sullivan; Hamid Tehrani; Hywel C Williams Journal: Cochrane Database Syst Rev Date: 2018-12-04
Authors: Jacqueline Dinnes; Jonathan J Deeks; Naomi Chuchu; Lavinia Ferrante di Ruffano; Rubeta N Matin; David R Thomson; Kai Yuen Wong; Roger Benjamin Aldridge; Rachel Abbott; Monica Fawzy; Susan E Bayliss; Matthew J Grainge; Yemisi Takwoingi; Clare Davenport; Kathie Godfrey; Fiona M Walter; Hywel C Williams Journal: Cochrane Database Syst Rev Date: 2018-12-04
Authors: Lavinia Ferrante di Ruffano; Yemisi Takwoingi; Jacqueline Dinnes; Naomi Chuchu; Susan E Bayliss; Clare Davenport; Rubeta N Matin; Kathie Godfrey; Colette O'Sullivan; Abha Gulati; Sue Ann Chan; Alana Durack; Susan O'Connell; Matthew D Gardiner; Jeffrey Bamber; Jonathan J Deeks; Hywel C Williams Journal: Cochrane Database Syst Rev Date: 2018-12-04