An updated algorithm integrated with patient data for the differentiation of atypical nevi from early melanomas: the idScore 2021
Keywords:
body, early melanoma, atypical nevi, dermoscopy, risk factors, clinical and personal dataAbstract
Background: It is well known that multiple patient-related risk factors contribute to the development of cutaneous melanoma, including anagraphic, phenotypic and anamnestic factors.
Objectives: We aimed to investigate which MM risk factors were relevant to be incorporated in a risk scoring-classifier based clinico-dermoscopic algorithm.
Methods: This retrospective study was performed on a monocentric dataset of 374 atypical melanocytic skin lesions (aMSLs) sharing equivocal dermoscopic features, excised in the suspicion of malignancy. Dermoscopic standardized images of 258 atypical nevi and 116 early melanomas were collected along with objective lesional data (i.e., maximum diameter, specific body site and body area) and 7 dermoscopic data. All cases were combined with a series of 10 MM risk factors, including anagraphic (2), phenotypic (5) and anamnestic (3) ones.
Results: According to the stepwise analysis logistic regression, the proposed iDScore 2021 algorithm is composed by 9 variables, including 3 patient data (age, skin phototype I/II, personal/familiar history of MM), 2 lesion characteristics (maximum diameter, location on the lower extremities (thighs/legs/ankles/back of the feet) and 4 dermoscopic features (irregular dots and globules, irregular streaks, blue gray peppering, blue white veil). The algorithm assigned to each lesion/case a progressive score from 0 to 18. The performance obtained in the ROC curve analysis reached 92% AUC in differentiating early melanomas from atypical nevi.
Conclusions: An integrated checklist combining multiple anamnestic data with selected relevant dermoscopic features can be useful in the differential diagnosis and management of early melanomas and atypical nevi exhibiting equivocal features.
References
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3. Kittler H, Guitera P, Riedl E, et al. Identification of Clinically Featureless Incipient Melanoma Using Sequential Dermoscopy Imaging. Arch Dermatol. 2006; 142(9):1113–1119. DOI: 10.1001/archderm.142.9.1113 PMID: 16982998.
4. Puig S, Argenziano G, Zalaudek I et al. Melanomas that failed dermoscopic detection: a combined clinicodermoscopic approach for not missing melanoma. Dermatol Surg 2007; 33: 1262– 1273. DOI: 10.1111/j.1524-4725.2007.33264.x PMID: 17903162.
5. Argenziano G, Zalaudek I, Ferrara G, et al. Dermoscopy features of melanoma incognito: indications for biopsy. J Am Acad Dermatol 2007; 56(3):508–13. DOI: 10.1016/j.jaad.2006.10.029 PMID: 17113189.
6. Pizzichetta MA, Stanganelli I, Bono R et al. Dermoscopic features of difficult melanoma. Dermatol Surg 2007; 37: 91– 99. DOI: 10.1111/j.1524-4725.2007.33015.x PMID: 17214687.
7. Nufer KL, Raphael AP, Soyer HP. Dermoscopy and overdiagnosis of melanoma in situ. JAMA dermatology. 2018 Apr 1;154(4):398-9. DOI: 10.1001/jamadermatol.2017.6448 PMID: 29466567.
8. Ferris LK. Early Detection of Melanoma: Rethinking the Outcomes That Matter. JAMA Dermatol. 2021;157(5):511–513. DOI: 10.1001/jamadermatol.2020.5650. PMID: 33729450.
9. Muzumdar S, Lin G, Kerr P, et Al. Is Melanoma Overdiagnosed? A Review of the Evidence. J Am Acad Dermatol. 2021 Jun 8:S0190-9622(21)01110-5. DOI: 10.1016/j.jaad.2021.06.010 PMID: 34116095.
10. Welch HG, Mazer BL, Adamson AS. The Rapid Rise in Cutaneous Melanoma Diagnoses. N Engl J Med. 2021 Jan 7;384(1):72-79. DOI: 10.1056/NEJMsb2019760. PMID: 33406334.
11. Tognetti L, Cinotti E, Moscarella E, et Al. Impact of clinical and personal data in the dermoscopic differentiation between early melanoma and atypical nevi. Dermatol Pract Concept. 2018 Oct 31;8(4):324-327. DOI: 10.5826/dpc.0804a16. PMID: 30479866.
12. Yap J, Yolland W, Tschandl P. Multimodal skin lesion classification using deep learning. Exp Dermatol. 2018 Nov;27(11):1261-1267. DOI: 10.1111/exd.13777. PMID: 30187575.
13. Tognetti L, Cevenini G, Moscarella E, et al. Validation of an integrated dermoscopic scoring method in an European teledermoscopy web platform: the iDScore project for early detection of melanoma. JEADV 2020; Volume34, 3:640-647. DOI: 10.1111/jdv.15923. PMID: 31465600.
14. Tognetti L, Cartocci A, Cinotti E, et Al. The impact of anatomical location and sun exposure on the dermoscopic recognition of atypical nevi and early melanomas: usefulness of an integrated clinical-dermoscopic method (iDScore). J Eur Acad Dermatol Venereol. 2021 Mar;35(3):650-657. DOI: 10.1111/jdv.16847. PMID: 32743829.
15. Hekler A, Utikal JS, Enk AH, et al. Superior skin cancer classification by the combination of human and artificial intelligence. Eur J Cancer. 2019 Oct;120:114-121. DOI: 10.1016/j.ejca.2019.07.019. PMID: 31518967.
16. Tognetti L, Bonechi S, Andreini P, et Al. A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi. J Dermatol Sci. 2021 Feb;101(2):115-122. DOI: 10.1016/j.jdermsci.2020.11.009. PMID: 33358096.
17. Höhn J, Krieghoff-Henning E, Jutzi TB, von Kalle C, et al. Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification. Eur J Cancer. 2021 May; 149:94-101. DOI: 10.1016/j.ejca.2021.02.032. PMID: 33838393.
18. Tognetti L, Cartocci A, Cinotti E, et Al. Dermoscopy of early melanomas: variation according to the anatomic site. Arch Dermatol Res. 2021 Mar 26. DOI: 10.1007/s00403-021-02226-x. PMID: 33772339.
19. Gershenwald JE Melanoma staging: Evidence-based changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA Cancer J Clin (2017), 67(6):472. DOI: 10.3322/caac.21409. PMID: 29028110.
20. Carr S, Smith C, Wernberg J. Epidemiology and Risk Factors of Melanoma. Surg Clin North Am. 2020 Feb;100(1):1-12. Epub 2019 Nov 4. DOI: 10.1016/j.suc.2019.09.005. PMID: 31753105.
21. Raimondi S, Suppa M, Gandini S. Melanoma Epidemiology and Sun Exposure. Acta Derm Venereol. 2020 Jun 3;100(11):adv00136. DOI: 10.2340/00015555-3491. PMID: 32346751.
22. Kaiser I, Pfahlberg AB, Uter W, et Al. , Risk Prediction Models for Melanoma: A Systematic Review on the Heterogeneity in Model Development and Validation. Int J Environ Res Public Health. 2020 Oct 28;17(21):7919. DOI: 10.3390/ijerph17217919. PMID: 33126677.
23. Palve JS, Korhonen NJ, Luukkaala TH, Kääriäinen MT. Differences in Risk Factors for Melanoma in Young and Middle-aged Higher-risk Patients. In Vivo. 2020 Mar-Apr;34(2):703-708. DOI: 10.21873/invivo.11827. PMID: 32111773.
24. Paulson KG, Gupta D, Kim TS, et al (2020) Age-Specific Incidence of Melanoma in the United States. JAMA Dermatology. 2019; 156:57–64. DOI: 10.1001/jamadermatol.2019.3353. PMID: 31721989.
25. Liu F, Bessonova L, Taylor TH et al. A unique gender difference in early onset melanoma implies that in addition to ultraviolet light exposure other causative factors are important. Pigment Cell Melanoma Res 2013; 26: 128–135. DOI: 10.1111/pcmr.12035. PMID: 23095171.
26. Slape D, Tang J, Lawless R, McCrossin I and Frew JW: A retrospective cohort study of melanoma prevalence stratified by body site in a regional Australian population 1994-2017: Site- specific protective mechanisms. Photodermatol Photoimmunol Photomed 2019. 35(3): 135-140, 2019. DOI: 10.1111/phpp.12436. PMID: 30381854.
27. Ghiasvand R, Robsahm TE, Green AC, et al. Association of Phenotypic Characteristics and UV Radiation Exposure With Risk of Melanoma on Different Body Sites. JAMA Dermatol. 2019;155(1):39–49. DOI: 10.1001/jamadermatol.2018.3964 PMID: 30477003
28. Liu-Smith F, Ziogas A. Age-dependent interaction between sex and geographic ultraviolet index in melanoma risk. J Am Acad Dermatol. 2020 May;82(5):1102-1108.e3. Epub 2017 Dec 2 DOI: 10.1016/j.jaad.2017.11.049. PMID: 29203439
29. Argenziano G, Giacomel J, Zalaudek I, et Al. Twenty nevi on the arms: a simple rule to identify patients younger than 50 years of age at higher risk for melanoma. Eur J Cancer Prev. 2014 Sep;23(5):458-63. DOI: 10.1097/CEJ.0000000000000053. PMID: 25068806.
30. Garbe C, Buttner P, Weiss J, et al. Associated factors in the prevalence of more than 50 common melanocytic nevi, atypical melanocytic nevi, and actinic lentigines: multicenter case-control study of the Central Malignant Melanoma Registry of the German Dermatological Society. J Invest Dermatol 1994;102:700-705. DOI: 10.1111/1523-1747.ep12374298 PMID: 8176251
31. Dennis LK, White E, Lee JA, et al. Constitutional factors and sun exposure in relation to nevi: a population-based crosssectional study. Am J Epidemiol 1996;143:248-256. DOI: 10.1093/oxfordjournals.aje.a008735 PMID: 8561158
32. Carli P, Naldi L, Lovati S, et al, Oncology Cooperative Group of the Italian Group for Epidemiologic Research in Dermatology. The density of melanocytic nevi correlates with constitutional variables and history of sunburns: a prevalence study among Italian schoolchildren. Int J Cancer 2002; 101:375-379. DOI: 10.1002/ijc.10629. PMID: 12209963.
33. Whiteman DC, Whiteman CA, Green AC. Childhood sun exposure as a risk factor for melanoma: a systematic review of epidemiologic studies. Cancer Causes Control. 2001; 12:69-82. DOI: 10.1023/a:1008980919928. PMID: 11227927.
34. Karlsson MA, Rodvall Y, Wahlgren CF, et al. Similar anatomical distributions of childhood naevi and cutaneous melanoma in young adults residing in northern and southern Sweden. Eur J Cancer. 2015;51:2067-2075. DOI: 10.1016/j.ejca.2015.06.114. PMID: 26187511.
35. Karlsson MA, Rodvall Y, Wahlgren CF, et al. Similar anatomical distributions of childhood naevi and cutaneous melanoma in young adults residing in northern and southern Sweden. Eur J Cancer. 2015;51:2067-2075. DOI: 10.1016/j.ejca.2015.06.114. PMID: 26187511.
36. DeLancey JO, Hannan LM, Gapstur SM, Thun MJ. Cigarette smoking and the risk of incident and fatal melanoma in a large prospective cohort study. Cancer Causes Control. 2011; 22(6):937–42. DOI: 10.1007/s10552-011-9766-z. PMID: 21544529.
37. Wei EX, Li X, Nan H. Extremity nevus count is an independent risk factor for basal cell carcinoma and melanoma, but not squamous cell carcinoma. J Am Acad Dermatol 2019; 80(4):970-978. DOI: 10.1016/j.jaad.2018.09.044. PMID: 30713015.
38. Kessides MC, Wheless L, Hoffman-Bolton J, Clipp S, Alani RM, Alberg AJ. Cigarette smoking and malignant melanoma: a case-control study. J Am Acad Dermatol. 2011 Jan; 64((1)):84–90. DOI: 10.1016/j.jaad.2010.01.041. PMID: 20334951.
39.Sondermeijer L, Lamboo LGE, de Waal AC, et Al. Cigarette Smoking and the Risk of Cutaneous Melanoma: A Case-Control Study. Dermatology. 2020; 236(3):228-236. Epub 2019 Sep 10. DOI: 10.1159/000502129. PMID: 31505496.
40.Li X, Kraft P, De Vivo I, Giovannucci E, Liang L, Nan H. Height, nevus count, and risk of cutaneous malignant melanoma: Results from 2 large cohorts of US women. J Am Acad Dermatol 2020; 83(4):1049-1056. DOI: 10.1016/j.jaad.2020.04.158. PMID: 32376423.
41.Argenziano G, Giacomel J, Zalaudek I et al. Twenty nevi on the arms: a simple rule to identify patients younger than 50 years of age at higher risk for melanoma. Eur J Cancer Prev 2014; 23:458–63. DOI: 10.1097/CEJ.0000000000000053. PMID: 25068806.
42. Ribero S, Zugna D, Osella-Abate S, et Al. Prediction of high naevus count in a healthy U.K. population to estimate melanoma risk. Br J Dermatol 2016; 174(2):312-8. DOI: 10.1111/bjd.14216. PMID: 26479165.
43. M. Kvaskoff, N. Pandeya, A.C. Green, et al. Site-specific determinants of cutaneous melanoma: a case-case comparison of patients with tumors arising on the head or trunk. Cancer Epidemiol Biomarkers Prev, 22 (2013), pp. 2222-2231. DOI: 10.1158/1055-9965.EPI-13-0475. PMID: 24083994.
44. Fariñas-Alvarez C, Ródenas JM, Herranz MT, Delgado-Rodríguez M. The naevus count on the arms as a predictor of the number of melanocytic naevi on the whole body. Br J Dermatol 1999; 140:457–62. DOI: 10.1046/j.1365-2133.1999.02709.x. PMID: 10233266.
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2022-07-28
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Copyright (c) 2022 Linda Tognetti, Alessandra Cartocci, Martina Bertello, Mafalda Giordani, Elisa Cinotti, Gabriele Cevenini, Pietro Rubegni
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Tognetti L, Cartocci A, Bertello M, et al. An updated algorithm integrated with patient data for the differentiation of atypical nevi from early melanomas: the idScore 2021. Dermatol Pract Concept. 2022;12(3):e2022134. doi:10.5826/dpc.1203a134
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