Advances in Early Detection of Melanoma and the Future of At-Home Testing

被引：15

作者：

Garrison, Zachary R. ^{[1
]}

Hall, Connor M. ^{[1
]}

Fey, Rosalyn M. ^{[1
]}

Clister, Terri ^{[1
]}

Khan, Nabeela ^{[1
]}

Nichols, Rebecca ^{[1
]}

Kulkarni, Rajan P. ^{[1
,2
,3
,4
]}

机构：

[1] Oregon Hlth & Sci Univ, Dept Dermatol, Portland, OR 97239 USA

[2] Canc Early Detect Adv Res Ctr CEDAR, Portland, OR 97239 USA

[3] Oregon Hlth & Sci Univ, Knight Canc Inst, Portland, OR 97239 USA

[4] US Dept Vet Affairs Portland Hlth Care Syst, Operat Care Div, Portland, OR 97239 USA

来源：

LIFE-BASEL | 2023年 / 13卷 / 04期

关键词：

melanoma; early-detection; self-screening; primary-care; artificial intelligence; SKIN SELF-EXAMINATION; MALIGNANT-MELANOMA; PRIMARY-CARE; COST-EFFECTIVENESS; EARLY-DIAGNOSIS; PIGMENTED LESIONS; 7-POINT CHECKLIST; DERMOSCOPY; EDUCATION; CANCER;

D O I：

10.3390/life13040974

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The past decade has seen numerous advancements in approaches to melanoma detection, each with the common goal to stem the growing incidence of melanoma and its mortality rate. These advancements, while well documented to increase early melanoma detection, have also garnered considerable criticism of their efficacy for improving survival rates. In this review, we discuss the current state of such early detection approaches that do not require direct dermatologist intervention. Our findings suggest that a number of at-home and non-specialist methods exist with high accuracy for detecting melanoma, albeit with a few notable concerns worth further investigation. Additionally, research continues to find new approaches using artificial intelligence which have promise for the future.

引用

页数：14

共 105 条

[1]

AIM at Melanoma Foundation, SKIN SELF EX

[2] Novice Identification of Melanoma: Not Quite as Straightforward as the ABCDs [J].

Aldridge, R. Benjamin ;

Zanotto, Matteo ;

Ballerini, Lucia ;

Fisher, Robert B. ;

Rees, Jonathan L. .

ACTA DERMATO-VENEREOLOGICA, 2011, 91 (02) :125-130

[3]

Alexandrescu DT, 2009, J INVEST DERMATOL, V129, pS140

[4] Sex and Gender Disparities in Melanoma [J].

Bellenghi, Maria ;

Puglisi, Rossella ;

Pontecorvi, Giada ;

De Feo, Alessandra ;

Care, Alessandra ;

Mattia, Gianfranco .

CANCERS, 2020, 12 (07) :1-23

[5]

BORLAND R, 1992, AUST J PUBLIC HEALTH, V16, P370

[6] Deep neural networks are superior to dermatologists in melanoma image classification [J].

Brinker, Titus J. ;

Hekler, Achim ;

Enk, Alexander H. ;

Berking, Carola ;

Haferkamp, Sebastian ;

Hauschild, Axel ;

Weichenthal, Michael ;

Klode, Joachim ;

Schadendorf, Dirk ;

Holland-Letz, Tim ;

von Kalle, Christof ;

Froehling, Stefan ;

Schilling, Bastian ;

Utikal, Jochen S. .

EUROPEAN JOURNAL OF CANCER, 2019, 119 :11-17

[7] Melanoma in skin of color: Part II. Racial disparities, role of UV, and interventions for earlier detection [J].

Brunsgaard, Elise K. ;

Jensen, Jakob ;

Grossman, Douglas .

JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY, 2023, 89 (03) :459-468

[8]

Cancer Research, UK MEL SKIN CANC INC

[9] Melanoma Detection by Non-Specialists: An Untapped Potential for Triage? [J].

Cantisani, Carmen ;

Ambrosio, Luca ;

Cucchi, Carlotta ;

Meznerics, Fanni Adel ;

Kiss, Norbert ;

Banvolgyi, Andras ;

Rega, Federica ;

Grignaffini, Flavia ;

Barbuto, Francesco ;

Frezza, Fabrizio ;

Pellacani, Giovanni .

DIAGNOSTICS, 2022, 12 (11)

[10] Evaluating a game-based randomized experiment to increase melanoma identification among adults living in the US [J].

Carcioppolo, Nick ;

Kim, Soyoon ;

Sanchez, Margaret ;

Mao, Bingjing ;

Malova, Ekaterina ;

Ryan, Ashley ;

Lun, Di ;

Ewing, Clay ;

Hu, Shasa .

SOCIAL SCIENCE & MEDICINE, 2022, 310

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