The Future of Precision Prevention for Advanced Melanoma

被引:11
作者
Lee, Katie J. [1 ]
Betz-Stablein, Brigid [1 ]
Stark, Mitchell S. [1 ]
Janda, Monika [2 ]
McInerney-Leo, Aideen M. [1 ]
Caffery, Liam J. [2 ]
Gillespie, Nicole [3 ]
Yanes, Tatiane [1 ]
Soyer, H. Peter [1 ,4 ]
机构
[1] Univ Queensland, Univ Queensland Diamantina Inst, Dermatol Res Ctr, Brisbane, Qld, Australia
[2] Univ Queensland, Ctr Hlth Serv Res, Sch Med, Brisbane, Qld, Australia
[3] Univ Queensland, Univ Queensland Business Sch, Fac Business Econ & Law, Brisbane, Qld, Australia
[4] Princess Alexandra Hosp, Dept Dermatol, Brisbane, Qld, Australia
基金
澳大利亚国家健康与医学研究理事会; 英国医学研究理事会;
关键词
melanoma; prevention; artificial intelligence; genomics; risk stratification; CUTANEOUS MELANOMA; FAMILY-HISTORY; RISK-FACTORS; NEVI;
D O I
10.3389/fmed.2021.818096
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular total body photography with sequential digital dermoscopic imaging. This approach aims to address both underdiagnosis (a missed or delayed melanoma diagnosis) and overdiagnosis (the diagnosis and treatment of indolent lesions that would not have caused a problem). Holistic risk stratification considers several types of melanoma risk factors: clinical phenotype, comprehensive imaging-based phenotype, familial and polygenic risks. Artificial intelligence computer-aided diagnostics combines these risk factors to produce a personalized risk score, and can also assist in assessing the digital and molecular markers of individual lesions. However, to ensure uptake and efficient use of AI systems, researchers will need to carefully consider how best to incorporate privacy and standardization requirements, and above all address consumer trust concerns.
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收藏
页数:7
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