Skin Cancer Screening: Recommendations for Data-Driven Screening Guidelines and a review of the US Preventive Services Task Force Controversy

Johnson, Mariah; Leachman, Sancy A.; Aspinwall, Lisa G.; Cranmer, Lee D.; Curiel‐Lewandrowski, Clara; Sondak, Vernon K.; Stemwedel, Clara E.; Swetter, Susan M.; Vetto, John T.; Bowles, Tawnya L.; Dellavalle, Robert P.; Geskin, Larisa J.; Grossman, Douglas; Grossmann, Kenneth F.; Hawkes, Jason E.; Jeter, Joanne; Kim, Caroline C.; Kirkwood, John M.; Mangold, Aaron R.; Meyskens, Frank L.; Ming, Michael E.; Nelson, Kelly C.; Piepkorn, Michael; Pollack, Brian P.; Robinson, June K.; Sober, Arthur J.; Trotter, Shannon C.; Venna, Suraj; Agarwala, Sanjiv S.; Alani, Rhoda M.; Averbook, Bruce J.; Bar, Anna; Becevic, Mirna; Box, Neil F.; Carson, William E.; Cassidy, Pamela B.; Chen, Shuo; Chu, Emily Y.; Ellis, Darrel L.; Ferris, Laura K.; Fisher, David E.; Kendra, Kari; Lawson, David H.; Leming, Philip D.; Margolin, Kim; Markovic, Svetomir; Martini, Mary C.; Miller, Debbie; Sahni, Debjani; Sharfman, William H.; Stein, Jennifer A.; Stratigos, Alexander J.; Tarhini, Ahmad A.; Taylor, Matthew H.; Wisco, Oliver J.; Wong, Michael K.

doi:10.2217/mmt-2016-0022

Cited by 106 publications

(84 citation statements)

References 60 publications

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“…For a user checking the skin lesions with this SA, it may prove difficult to evaluate all relevant skin lesions, as lay users may not know which lesions could be dangerous and/or which the user cannot see (namely for users taking an image without a partner and/or if the lesions are located in places which are hard to reach). On the other hand, this may also occur in current clinical practice as previous studies suggested GPs usually do not perform a full‐body skin examination …”

Section: Discussionmentioning

confidence: 99%

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Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

Udrea

Mitra

Costea

et al. 2019

Acad Dermatol Venereol

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Background Machine learning algorithms achieve expert‐level accuracy in skin lesion classification based on clinical images. However, it is not yet shown whether these algorithms could have high accuracy when embedded in a smartphone app, where image quality is lower and there is high variability in image taking scenarios by users. In the past, these applications were criticized due to lack of accuracy. Objective In this study, we evaluate the accuracy of the newest version of a smartphone application (SA) for risk assessment of skin lesions. Methods This SA uses a machine learning algorithm to compute a risk rating. The algorithm is trained on 131 873 images taken by 31 449 users in multiple countries between January 2016 and August 2018 and rated for risk by dermatologists. To evaluate the sensitivity of the algorithm, we use 285 histopathologically validated skin cancer cases (including 138 malignant melanomas), from two previously published clinical studies (195 cases) and from the SA user database (90 cases). We calculate the specificity on a separate set from the SA user database containing 6000 clinically validated benign cases. Results The algorithm scored a 95.1% (95% CI, 91.9–97.3%) sensitivity in detecting (pre)malignant conditions (93% for malignant melanoma and 97% for keratinocyte carcinomas and precursors). This level of sensitivity was achieved with a 78.3% (95% CI, 77.2–79.3%) specificity. Conclusions This SA provides a high sensitivity to detect skin cancer; however, there is still room for improvement in terms of specificity. Future studies are needed to assess the impact of this SA on the health systems and its users.

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Section: Discussionmentioning

confidence: 99%

“…On the other hand, this may also occur in current clinical practice as previous studies suggested GPs usually do not perform a full-body skin examination. 20,21 These risks are mitigated by the SA in several ways. First, every pair of image and corresponding risk rating gets a quality control by a dermatologist.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

Udrea

Mitra

Costea

et al. 2019

Acad Dermatol Venereol

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“…We thus advocate for skin self‐awareness and individuals should seek consultation with a medical professional for unusual spots or itching, bleeding, or changing moles. Finally, we support clinical evaluations with total body skin examinations for adults 35 to 75 years of age with personal history of skin cancer, family history of melanoma or hereditary predisposition to melanoma, light skin phototypes and red hair phenotypes, and/or a history of sun overexposure . Further evaluations and interval of follow up are dependent on the risk of the individual in consultation with a dermatologist.…”

Section: Discussionmentioning

confidence: 56%

Melanoma

O’Neill

Scoggins

2019

Journal of Surgical Oncology

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Current recommendations by the United States Preventive Services Task Force donot support screening for skin cancer. Melanoma is unique among cancers because detection is through visual inspection. Development of technologies that aid visual inspection have supported screening strategies in high-risk populations such as older fair skinned males with personal or family history of melanoma. Clearly delineating these populations and appropriate utilization of these newer technologies will be imperative in future screening paradigms.

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“…Melanoma is the fifth most common invasive cancer in the USA, and its incidence is increasing around the world [27,28]. Melanomas are also responsible for the vast majority of skin cancer-related mortalities [28]. Skin cancer is screened visually with a total body skin examination.…”

Section: Melanomamentioning

confidence: 99%

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

165

108

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Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

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Skin Cancer Screening: Recommendations for Data-Driven Screening Guidelines and a review of the US Preventive Services Task Force Controversy

Cited by 106 publications

References 60 publications

Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

Melanoma

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

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