10.5826/dpc.1203a134
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.
text/xml
2160-9381
2078
en
Pietro Rubegni
https://creativecommons.org/licenses/by-nc/4.0
Dermatology Practical & Conceptual
body, early melanoma, atypical nevi, dermoscopy, risk factors, clinical and personal data
An updated algorithm integrated with patient data for the differentiation of atypical nevi from early melanomas: the idScore 2021
Text.Serial.Journal
https://dpcj.org/index.php/dpc
Original Article
2022-07-28
2021-09-29
2022-07-28
2022-09-15
e2022134
Dermatol Pract Concept
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.
Pietro Rubegni
2022/07/28
10.5826/dpc.1203a134
e2022134
Dermatology Unit, Department of Medical, Surgical and NeuroSciences, University of Siena, Siena, Italy 
2160-9381
body, early melanoma, atypical nevi, dermoscopy, risk factors, clinical and personal data
en
e2022134
An updated algorithm integrated with patient data for the differentiation of atypical nevi from early melanomas: the idScore 2021
https://dpcj.org/index.php/dpc/article/download/2078/1670
An Updated Algorithm Integrated With Patient Data
 for the Differentiation of Atypical Nevi From Early
            Melanomas: the idScore 2021
Linda Tognetti1, Alessandra Cartocci1,2, Martina Bertello1, Mafalda Giordani1, Elisa Cinotti1,
                            Gabriele Cevenini3, Pietro Rubegni1


1 Dermatology Unit, Department of Medical, Surgical and NeuroSciences, University of Siena, Siena, Italy
2 Department of Medical Biotechnologies, University of Siena, Siena, Italy
3 Bioengineering & Biomedical Data Science Lab - University of Siena, Siena, Italy



Key words: melanoma, atypical nevi, dermoscopy, risk factors
Citation: 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: https://doi.org/10.5826/dpc.1203a134
Accepted: November 27, 2021; Published: July 2022
Copyright: ©2022 Tognetti et al. This is an open-access article distributed under the terms of the Creative Commons Attribution-
NonCommercial License (BY-NC-4.0), https://creativecommons.org/licenses/by-nc/4.0/, which permits unrestricted noncommercial use,
distribution, and reproduction in any medium, provided the original authors and source are credited.
Funding: None.
Competing interests: None.
Authorship: All authors have contributed significantly to this publication.
Corresponding author: Linda Tognetti, MD, PhD, Hospital S. Maria alle Scotte, Viale Bracci 16, 53100 Siena, Italy,
E-mail: linda.tognetti@ dbm.unisi.it




  ABSTRACT              Introduction: It is well known that multiple patient-related risk factors contribute to the development
                        of cutaneous melanoma, including demographic, 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 mela-
                        nocytic skin lesions sharing equivocal dermoscopic features, excised in the suspicion of malignancy.
                        Dermoscopic standardized images of 258 atypical nevi (aN) and 116 early melanomas (eMM) 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
                        demographic (2), phenotypic (5) and anamnestic (3) ones.
                        Results: The proposed iDScore 2021 algorithm is composed by 9 variables (age, skin phototype I/II,
                        personal/familiar history of MM, 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 a score from 0 to 18, reached
                        an area under the ROC curve of 92% and, with a score threshold ≥ 6, a sensitivity (SE) of 98.2% and
                        a specificity (SP) of 50.4%, surpassing the experts in SE (+13%) and SP (+9%).




Original Article | Dermatol Pract Concept. 2022;12(3):e2022134                                                                               1
                     Conclusions: An integrated checklist combining multiple anamnestic data with selected relevant der-
                     moscopic features can be useful in the differential diagnosis and management of eMM and aN exhib-
                     iting with equivocal features.




Introduction                                                    specific dermoscopic pattern. Histological diagnoses were
                                                                retrospectively collected, including dermoscopic standard-
An adequate dermoscopic differentiation between atypical        ized polarized images (OM 20X) have been prospectively
melanocytic skin lesions (aMSLs), ie, atypical nevi (aN) and    collected along with lesional data (maximum diameter and
early melanomas (eMM) can represent a challenge in daily        body location based on a sun-exposure classification), as
practice, especially for less experienced dermatoscopists.      previously described [14]. In addition, patients personal data
Dermoscopy alone cannot be accurate enough to adequately        concerning 8 MM risk factors were collected, ie: personal/fa-
recognize aN exhibiting equivocal dermosocpic feature or, of    miliar history of MM; sunburns before 14 years, phototype,
converse, can fail to identify those eMM that do not exhibit    pheomelanin, blond hairs, blue/green eyes, >11 nevi on the
clear-cut dermoscopic features suggestive for malignancy        right arm (Table 1). The presence of pheomelanin phenotype
[1-6]. In addition, we also debate whether a certain degree     was assessed when the patient had red/carroty/straw red/
of overdiagnosis of in situ MM might have took place in         brown-reddish hair, pale skin, freckles and high tendency to
the last decade worldwide [7-10]. In this context, a reason-    sunburn and/or inability to tan.
able way out seems to be to follow a global approach to the
patient integrating dermoscopic imaging with multiple risk      Dermoscopic-reader Study
assessment tools and personal plus lesional data [7,11-18].     Dermoscopic evaluations were independently performed by
                                                                4 experts in dermoscopy, blinded for histopathological di-
Objectives                                                      agnosis (EC, MB, AL, PR). They were asked to recognize a
                                                                dermoscopic feature among a series of 7, previously selected
We previously demonstrated the efficacy of integrating 3 rel-   for the iDScore 2018 checklist (Tables 1 and 4), including:
evant clinical parameters (ie, age, maximum diameter and        Atypical Network (AN), Irregular Streaks (IS), Blue White
body location) into a dermoscopic algorithm (the iDScore        Veil (BWV), Blue Gray Peppering (BGP), White Scar-like Ar-
2018) [1], which reached high diagnostic accuracy on both       eas (WSA), Shiny White Streaks (SWS) and Irregular Dots
a monocentric dataset of 435 aMSLs and on a multicentric        Globules (IDG) (Figures 1 and 2). Then, they were asked
dataset of 980 aMSLs. We then aimed to extend the list of       to express an intuitive diagnosis of eMM/aN. The presence
clinical parameters to the most relevant potential melanoma     of one or more dermoscopic features inside each lesion and
risk factors and to investigate which were the most signifi-    the final diagnosis was assessed based on the agreement of
cant independent association with a MM histologic diagno-       3 out of 4.
sis. Secondly, we aimed to select through stepwise logistic
regression analysis a series of interdependently significant    Integrated dataset
data, useful to develop a new iDScore 2021 checklist, able      After selection (MG, LT, AC) for image quality, availability
to provide a differential score to distinguish eMM from aN      of patient data and agreement of 2 out of 3 pathologists on
with equivocal features.                                        histopathological diagnosis, the final database consisted of
                                                                374 standardized dermoscopic pictures, 258 and 116 eMM.
Methods                                                         Each lesion was paired with 19 objective parameters, includ-
                                                                ing: 8 MM risk factors, 2 patient demographic data, 2 aMSL
This retrospective study was realized in accordance with the    objective data and 7 dermoscopic features (Table 1).
Declaration of Helsinki and approved by the local ethical
committee (ID16801); all data were de-identified before use.    Statistical analysis
                                                                Descriptive analysis was carried out using absolute frequen-
Data Collection                                                 cies and percentages for qualitative variables, mean and
A total of 410 aMSLs were consecutively excised from            standard deviation for age and diameter, median and min-
January 2018 to May 2021 in Siena University Hospital           imum-maximum range for the iDScore. Age and diameter
in the suspicion of malignancy. All aMSLs localized on the      were then categorized for the score model purpose, merg-
face, palms, and soles were excluded a priori due to their      ing classes with same risk. The association of gender, risk



2                                                                   Original Article | Dermatol Pract Concept. 2022;12(3):e2022134
factor and clinical features with histology were evaluated by          P < 0.05 was considered statistically significant. The analyses
chi-squared test. The difference of age and diameter between           were carried out with R version 4.10.
aN and eMM by t test, instead, the iDScore by Mann-Whit-
ney test. Kolmogorov-Smirnov test was used to evaluate the             Results
normality distribution of quantitative variables. Bivariate
analysis was performed by logistic regression, the Odds Ra-            Case study
tios (OR) and their 95% confidence interval (CI) were esti-            In Table 1 is reported the distribution of all patienst demo-
mated, too. In particular, eight bivariate logistic regression         graphic data (2), the melanoma risk factors (8) and the aMSLs
were carried out, each one with iDScore plus one risk factor.          morphologic data (2). Concerning the eMM, they affected males
After that, an integer score model was developed based on              in 53% versus females in 46% of cases, mean age was 58.9
logistic regression. Leave-one-out procedure was used for              years, the predominant body area was the upper trunk (49% of
testing the model. ROC curves and their areas (AUROC)                  cases) and the average diameter was 9.5mm. Histologic stages
were also estimated to compare the model performances. A               included: Tis (50), Ia (37), Ib (20) and IIa (9 cases) [19].



  Table 1. Distribution of patient demographic data, melanoma risk factors and lesional data in the
    case study iDScore database 2018-2020 of 374 atypical melanocytic skin lesions. The results
   of univariate analysis for significant association with MM histologic diagnosis are also reported
                                       with corresponding P-values.
                                                                  Atypical nevi (aN)     Early melanomas
                                                                       N=258              (eMM) N=116                        P
                                                          Demographic data
 1. Age (years)                                                       48.0±14.2               58.9±15.0                  < 0.001
 2. Gender
    Female                                                           106 (41.1%)             54 (46.6%)
                                                                                                                          0.366
    Male                                                             152 (58.9%)             62 (53.4%)
                                                       Anamnestic risk factors
                                          st
 1. History of melanoma (personal / 1 relative)                      72 (28.2%)              50 (43.1%)                   0.007
 2. Sunburns before 14 (yes/no)                                     173 (68.7%)              82 (71.9%)                   0.542
 3. Smoke (>5 cigarettes/day) (yes/no)                               66 (26.3%)              19 (22.1%)                   0.475
                                                        Phenotypic risk factors
 4. Skin phototype
    I+II                                                             66 (25.8%)              60 (51.7%)
                                                                                                                          0.005
    III+IV                                                           190 (74.2%)              56 48.3%)
 5. Pheomelanin phenotype (yes/no)                                    23 (9.0%)               21 (18%)                    0.005
 6. Blonde hair (yes/no)                                             42 (16.4%)              28 (24.3%)                    0.05
 7. Green/light-blue/blue eyes (yes/no)                              86 (33.7%)              43 (37.4%)                   0.556
 8. >11 nevi/right arm (yes/no)                                      128 (50.4%)             66 (57.4%)                   0.005
                                                                 aMSLs data
 1. Maximum diameter (mm)                                              6.4±2.5                  9.5±3.2                   <0.001
 2. Body area / anatomical site
    Upper Extremities - chronically photoexposed
                                                                      18 (7.0%)              14 (12.1%)                   0.113
    [head/neck/arms/hands]
    Upper Trunk - seldom photoexposed
                                                                     146 (56.6%)             57 (49.1%)                   0.217
    [shoulders/back/chest/breast]
    Lower Extremities - frequently photoexposed
                                                                     26 (10.1%)              18 (15.5%)                   0.164
    [thighs/legs/ankles/back of the feet]
    Lower Trunk - rarely photoexposed
                                                                     68 (26.4%)              27 (23.3%)                   0.608
    [side/bottom/abdomen]

                                                                                                                         Table1 continues



Original Article | Dermatol Pract Concept. 2022;12(3):e2022134                                                                         3
     Table 1. Distribution of patient demographic data, melanoma risk factors and lesional data in the
       case study iDScore database 2018-2020 of 374 atypical melanocytic skin lesions. The results
      of univariate analysis for significant association with MM histologic diagnosis are also reported
                                   with corresponding P-values. (Continued)
                                                            Atypical nevi (aN)         Early melanomas
                                                                 N=258                  (eMM) N=116                       P
                                                 aMSLs Dermoscopic features
       1. Atypical Network (yes/no)                               230 (89.1%)             104 (89.7%)                   1.000
       2. Irregular Streaks (yes/no)                              29 (11.2%)              49 (39.7%)                    <0.001
       3. Blue White Veil (yes/no)                                 18 (7.0%)              29 (25.0%)                    <0.001
       4. Blue Gray Peppering (yes/no)                             21 (8.1%)              28 (24.1%)                    <0.001
       5. White Scar-like Areas (yes/no)                           6 (2.3%)               18 (15.5%)                    <0.001
       6. Shiny White Streaks (yes/no)                             1 (0.4%)                4 (3.4%)                     0.034
       7. Irregular Dots Globules (yes/no)                        71 (27.5%)              60 (51.7%)                    <0.001
    iDScore 2018                                                    6 [2-11]                9 [4-14]                    <0.001



Table 2. Bivariate analysis of the 8 variables/melanoma risk factors combined with the iDscore 2018
                       checklist and association with MM histologic diagnosis.
                                                                               iDScore 2018 checklist + new variable
               8 variables/melanoma risk factors                          OR (95% CI)                               P
    >11 nevi/right arm                                                    1.1 (0.6-2.3)                           0.801
                                 st
    History of MM (personal/1 relative)                                   2.8 (1.4-5.4)                         < 0.001
    Sunburns                                                              1.0 (0.5-2.0)                           0.928
    Skin phototype (I+II vs III+IV)                                       2.9 (1.5-5.6)                         < 0.001
    Pheomelanin phenotype                                                 1.5 (0.7-3.6)                           0.333
    Green/light-blue/blue eyes                                            1.0 (0.6-2.0)                           0.913
    Blonde hair                                                           1.3 (0.6-2.7)                           0.546
    Smoke (>5 cigarettes/day)                                             1.1 (0.5-2.4)                           0.803

CI = Confidence Interval; OR Odds ratio.



Dermoscopic-reader study                                            Bivariate analysis
The presence of 7 dermosocpic variables in the 2 groups of          Each one of the 8 variables assumed as possible MM risk
aN and eMM according to experts consensus (ie, number               factors was tested in combination with the iDscore 2018
of positive observation, %) is also reported in Table 1. The        checklist for the association with MM histologic diagno-
4 experts in dermoscopy obtained, on average, a sensitivity         sis: according to the bivariate analysis results (Table 2), it
(SE) and specificity (SP) of 85.2% and 41.5%, respectively,         appeared that only 2 variables added a significant increase
on the present dataset of difficult aMSLs.                          in accuracy when incorporated into the previous checklist
                                                                    iDScore 2018, namely the “skin phototype I/II” and the “his-
Univariate analysis                                                 tory of MM” (personal or regarding the 1st degree relative).
A total of 12 variables resulted more frequently associated
with a eMM diagnosis rather than with aN diagnosis, and             Logistic regression
to significantly discriminate (P < 0.05) the 2entities, namely:     According to the stepwise analysis of the logistic regression
“
 age”, “history of MM (personal / 1st relative)”, “skin pho-        (Table 3), the new iDScore 2021 checklist would be com-
totype I/II”, “pheomelanin phenotype”, “>11 nevi/right              posed by only 9 parameters, including the “age ranges 31-60
arm”, “maximum diameter” and the presence of IS, BWV,               years” and ≥ 61 years, the “skin phototype I/II”, the “his-
BGP, WSA, SWS and IDG (Table 1).                                    tory of MM”, the “maximum diameter” ranges 6-10mm



4                                                                        Original Article | Dermatol Pract Concept. 2022;12(3):e2022134
and ≥11mm, the “body location on the lower Extremities              addition, the preferred score threshold (St) for both model is
(including thighs/legs/ankles/back of the feet), and presence       reported, along with the corresponding SE and SP values, while
of 4 dermoscopic variables such as IDG, IS, BWV and BGP.            the global performance is expressed as area under the ROC
                                                                    curve with 95% confidence interval. In detail, the iDScore
Performance analysis of the integrated model                        2021 showed: with St ≥ 6, SE = 98.2%, SP = 50.4 (+0.6% SE
Table 4 illustrates the composition of the iDScore 2018 and         and +2.3% SP compared with 2018 model); with St ≥ 5, SE =
the new iDScore 2021 model, the partial scores (coefficients)       100%, SP = 30%; with St ≥ 7, SE = 96.4% and SP=31.7.
assigned to each variable and the total score (S range) which           In Figures 1 and 2 are reported 6 exemplificative cases,
could be assigned to a given aMSL (from S = 0 to S = 18). In        namely 3 aMSL of the back (Figure 1) and 3 aMSLs of the



 Table 3. Results of the stepwise multivariate logistic regression analysis performed over all variables
    (2 patient anagraphic data + 8 melanomas risk factors + 2 lesion data + 7 dermoscopic data)
                             for the association with a histologic diagnosis.
            9 selected variables (iDScore 2021)                             OR (95% CI)                            P
 Age 31-60 years                                                         16.9 (3.0-54.4)                         0.004
 Age ≥ 61 years                                                        89.5 (14.4-889.4))                      < 0.001
 Maximum Diameter 6-10mm                                                  7.4 (3.1-20.2)                       < 0.001
 Maximum Diameter ≥11mm                                                 36.7 (12.1-126.3)                      < 0.001
 Lower Extremities                                                          3.1 (1.1-8.7)                        0.027
 IDG                                                                        2.6 (1.4-5.1)                        0.004
 IS                                                                       5.1 (2.4-11.3)                       < 0.001
 BWV                                                                      6.7 (2.5-19.1)                       < 0.001
 BGP                                                                        3.7 (1.5-9.3)                        0.005
 Phototype (I+II vs III+IV)                                                 3.2 (1.7-6.2)                      < 0.001
 History of melanoma (personal /1st relative)                               3.2 (1.6-6.5)                      < 0.001

BGP = Blue Gray Peppering; BWV = Blue White Veil; CI = Confidence Interval; IDG = Irregular Dots Globules; IS = Irregular Streaks; OR
Odds ratio.




   Table 4. Comparison of the two models of 2 models of integrated iDScore checklist: composition
          and performances obtained over 324 atypical melanocytic skin lesions of the body.
                          iDScore 2018                                                      iDScore 2021
          composition                        coefficient                     composition                      coefficient
 1. Atypical Network                               1               1. Blue white veil                              2
 2.Irregular Streaks                               1               2.Irregular Streaks                             2
 3.Blue White Veil                                 1               3.Irregular dots and globules                   1
 4.Blue Gray Peppering                             1               4. Blue Gray Peppering                          1
 5.White Scar-like Areas                           1               5. Maximum Diameter
 6.Shiny White Streaks                             1                  6–10 mm                                      2
 7.Irregular Dots Globules                         1                  ≥ 11mm                                       4
 8.Maximum diameter                                1               6. Age
      6–10 mm                                      3                  31-60 years                                  3
      ≥11 mm                                       4                  ≥ 61 years                                   5
 9.Age                                                             7. Lower Extremities                            1
      30-40 years                                  1               frequently photo-exposed
                                                                   [thighs / legs / ankles / back
      41-60 years                                  2
                                                                   of the feet]
      ≥61 years                                    3

                                                                                                                       Table4 continues



Original Article | Dermatol Pract Concept. 2022;12(3):e2022134                                                                       5
    Table 4. Comparison of the two models of 2 models of integrated iDScore checklist: composition
     and performances obtained over 324 atypical melanocytic skin lesions of the body. (Continued)
                            iDScore 2018                                                               iDScore 2021
          composition                           coefficient                           composition                         coefficient
 10.Body area                                                              8. Fair phototype (I/II)                            1
    Upper extremities -                                2                   9. History of melanoma                              1
    chronically photo-exposed                                              (personal/ 1st degree relative)
    Lower extremities -                                2
    frequently photo-exposed
    Upper trunk - seldom                               1
    photo-exposed
 ROC area (CI 95%)                        0.904 (0.872-0.935)              ROC area (CI 95%)                        0.917 (0.887-0.944)
 Score Range                                     0-16                      Score Range                                     0-18
 Score threshold                                 St ≥6                     Score threshold                                 St≥ 6
                                       (SE = 97.4%; SP = 48.1%)                                                  (SE = 98.2%; SP = 50.4%)

CI = confidence interval; SE = sensitivity; SP = specificity; st = core threshold




Figure 1. Examples of atypical melanocytic skin lesions (aMSLs) on the upper back from the case study. A 71 years-old male, phototype II,
personal history of melanoma, with a 11 mm aMSL, iDScore 2021 = 15 (iDScore 2018 = 9): histological examination revealed an early mela-
noma (MM T1aN0M0, thickness 0.7mm) (aA and B). A 61 years-old male, phototype II, with 7mm aMSL: the iDScore 2021 was 9 (iDScore
2018 = 7) and the histological examination revealed a nevus with moderate atypia (C and D). A 50 years-old male, 11 mm, phototype II,
> 11 nevi/right arm, 1st relative history of MM, sunburns before the age of 14, with a 10mm aMSL: the iDScore 2021 was 10 (iDScore 2018
= 8) and the histological examination revealed a nevus with severe atypia (E and F).



6                                                                                   Original Article | Dermatol Pract Concept. 2022;12(3):e2022134
Figure 2. Examples of atypical melanocytic skin lesions (aMSLs) of the chest from the case study. A 80 years-old male, phototype III, familiar
history of melanoma, with a 7 mm aMSL: iDScore 2021 was 12 (iDScore 2018 = 8) and the histological analysis revealed an in situ melanoma
(A and B). A 66 years-old male, phototype II, familiar history of MM, with a 10mm aMSL: the iDScore 2021 was 10 (iDScore 2018 = 8);
the histological analysis revealed a nevus with moderate atypia (C and D). A 47 years-old female, phototype III, with a 7.7 mm aMSL, the
iDScore 2021 was 6 (iDScore 2018 = 5) and the histological examination revealed a compound nevus (E and F).




chest (Figure 2) with the corresponding total scores obtained                When taking into account the patient demographic data,
iDScore 2021; for comparison, the iDScore 2018 total scores              age confirmed to be a significant independent risk factors
are also reported in brackets.                                           for discriminating aN from eMM, with a flexing point of
    Finally, according to the ROC curve analysis comparison              the S-shaped curve for malignancy incidence at 50 years [23-
(Figure 3) the new algorithm demonstrates to surpass the                 25]. The statistical analyses here conducted on a large data-
previous one by +1.3%.                                                   set of aMSLs were restricted to two crucial cut-offs at 31
                                                                         and 60years and allowed to identify three range groups with

Conclusions                                                              increasing risk for malignancy (Table 4). Concerning sex, we
                                                                         here observed that sex variable is not a variable to be con-
The debate about the relative impact of modifiable and non-              sidered for an algorithm, because aN and eMM are similarly
modifiable risk factors on melanoma development is still                 distributed among males and females, in line with recent
ongoing [20-22]. However, some demographic data related                  studies confirming no significancy, but only in association
to the patient and some characteristics of the lesion itself             with the UV-exposure habits and/or hormonal changes (ie
have currently acquired a considerable body of evidence                  female sex) [26-28]. Among the patient anamnestic data, the
and deserve to be investigated as possible additional risk               positive history for MM-personal or in a 1st degree ­relative-
score coefficients along with the dermoscopic parameters                 is still a considered a nonmodifiable risk for the incidence of
[1,2,11,13,14].                                                          a new MM [20-23,27,28]. Here in this dataset, this variable



Original Article | Dermatol Pract Concept. 2022;12(3):e2022134                                                                              7
                                                  ROC curve
                                                                                              cigarette smoking was found not to increase the risk of MM
               1                                                                              development, as well as in a large cohort study on US white

              0,9
                                                                                              women [39,40]. Similarly, here in this study we find the
                                                                                              smoke habit to involve 26% of patients from the aN group
              0,8
                                                                                              and 21% of patients from the eMM group (Table 1) and not
              0,7                                                                             to impact significantly on the differential diagnosis among
                                                                                              these two entities (Tables 2 and 3).
              0,6
                                                                                                 In the last decade, the parameter “total nevi number”
SENSITIVITY




              0,5                                                                             was investigated in adult European and American popula-
              0,4
                                                                                              tion as possible MM risk factor, both independently or in as-
                                                                                              sociation with other parameters (MM body site distribution,
              0,3
                                                                                              patient height, etc.) [37,39-43]: the high nevus count > 50
              0,2                                                                             of the whole body appeared to be independently associated
              0,1
                                                                                              with MM incidence, and high nevus count on the extremities
                                                                                              (ie photo-exposed areas) appeared to bring more risk than
               0
                    0   0,1   0,2   0,3     0,4       0,5         0,6   0,7   0,8   0,9   1   high nevus count on the trunk [37,40,42,44]. Then, several
                                                  1-SPECIFICITY                               investigations were carried out to find a valid esteem of the
                                                                                              total body count taking into account the nevi count on the
                                      iDscore           iDscore 2021
                                                                                              4 extremities, on the upper extremities (> 20), on the lower
Figure 3. Receiver operating characteristic (ROC) of the 3 inte-                              extremities (> 10) or on the right arm (> 11) [37,40-42,44].
grated algorithms: iDScore 2018 (black) and iDScore 2021 (blue)
                                                                                              To facilitate the risk factors collection in clinical practice,
obtained on the iDScore database 2018-2020 of 324 atypical me-
lanocytic skin lesions. The segments of the curves represent cases
                                                                                              we decided to adopt the cut-off of > 11 nevi on the right
obtaining the same score.                                                                     arm as predictor of the total nevi count, based on current
                                                                                              literature knowledge. When investigating this parameter in
                                                                                              our adult population of patients with aMSLs, similar rates
appeared to have a significant discrimination power in the                                    of high n
                                                                                                      ­ evus count in both the eM (53%) and the aN
univariate (Table1) and bivariate (Table 2) analysis and                                      (50%) group (Table 1), and it was not selected by multiple
was one of the predictive variable of the score model clas-                                   regression analysis. Indeed, our aN group population hosts
sifier (Tables 3 and 4), in line with previous studies on data                                a considerable quote of patients with multiple Clark nevus
from a multivariate analysis of predictors of eMM diagno-                                     phenotype, as occur in many second level referring ambula-
sis [29-31]. The parameter “positive history of sunburns in                                   tories for screening and follow-up. There are however data
childhood” is well known to be primary inciting event in the                                  suggesting that the MM incidence is higher in patients with
development of acquired melanocytic nevi in adults [30-32].                                   multiple aN/Clark nevi in addition to a family history of
On the other hand, recent ecological and case-control stud-                                   melanoma among relatives with the same phenotype, but
ies highlighted that is the total cumulative ambient sun ex-                                  low among people with sporadic phenotype of multiple
posure during childhood to correlate with melanoma risk                                       Clark nevi [45,46]. Consequently, the nevus count is not a
development, more than the parameter “positive history of                                     discriminant variable for distinguishing aN form EMs, but
sunburns in childhood” [33-35]. In line with these literature                                 should be evaluated along with the nevi characteristics, such
data, here in this study we found similar distribution of the                                 as the stability/change during follow-up and additional pa-
infancy sunburn parameter when comparing the population                                       tient data (eg the “Clark phenotype”).
of patient with dysplastic nevus syndrome/multiple atypical                                      Finally, we took into account the impact of all phys-
nevi (69%) and eMM (70%), which resulted not significant                                      ical characteristics related to melanin type, including the
in discriminating among the two entities (Tables 2 and 3).                                    skin phototype of the patient, its hair color, the eye color
Renown as a risk factor for several types of human cancer,                                    and the presence/absence of a pheomelanin phenotype. The
cigarette smoke was correlated with premature skin aging,                                     presence of blond hair and of blue/light-blue or green color
squamous cell carcinoma of the skin, psoriasis and impaired                                   were traditionally investigated as risk factor for skin cancer
wound healing [36]. Many studies have been carried out in                                     [46-48]. First studies in northern Europe population-based
the last decades for testing the association with MM too,                                     studies, the light eye color emerged as independently asso-
but results were not univocal or clear-cut due to residual                                    ciated risk factor for MM development (~1.6-fold higher
statistical confounders or inadequate sample size [36-38]. In                                 risk for MM compared with dark eyes), while the blond
a recent case-control study carried out over 1,157 patients                                   hair color had moderate risk [45-48]; more recently, Spanish
diagnosed with MM and 5,595 controls in the Netherlands,                                      population-based study revealed that hair and eye color did



8                                                                                                 Original Article | Dermatol Pract Concept. 2022;12(3):e2022134
not show any significant effects even after adjustments for       significant numerosity in the whole dataset. Importantly, the
confounders [44].                                                 atypical network was similarly observed in both the aN and
    Here in this study based on a southern European popu-         the eMM groups (89.1% and 89.7% of cases, respectively)
lation, the univariate analysis (Table 1) demonstrated that       (Table 1) thus cannot be considered a discriminant factor.
the discriminant independent power of the variables “fair         Thus, the differential diagnosis of aN and eMM equivocal
phenotype”, “blonde hair” and “green/light-blue/blue eyes”        images, concerning this monocentric dataset, relies essen-
is similar. Moreover, a significant discrimination is obtained    tially on the combination of 4 dermoscopic variables: “Blue
when comparing phenotypes I+II versus phenotypes III+IV,          white veil”, “Irregular Streaks, Irregular dots and globules”
in line with literature data that assigned a 3-fold higher risk   and “Blue Gray Peppering” (Table 4).
for MM as compared with phenotypes V+VI [45]. How-                   Third, for the final checklist composition, the selection
ever, the multivariate logistic regression analysis selected      based on multivariate analysis was restricted to the most rel-
the variable fair skin phenotype (I-II) (Table 4) instead of      evant interdependent 9 integrated variables, to respect the
the two variables “blonde hair” and “light-colored eyes”:         feasibility requirement for using the checklist in daily prac-
these two were likely to be statistically “absorbed” by the       tice without reducing the accuracy [1,13].
fair phototype variable, which is nevertheless considered an         Fourth, the total score range of iDScore 2021 is wider,
including category.                                               from 0 to 18, while for iDScore 2018 was 0-16 (Table 4,
    Of converse, the “pheomelanin phototype” is assessed          Figures 1 and 2).
in a patient exhibiting when red/carroty/reddish hair, pale          Concerning the performance comparison of the two
skin and freckles in combination with the high tendency to        models, when tested on the same monocentric dataset of 324
sunburn and/or inability to tan [45-50]. Recent molecular         aMSL, the new iDScore 2021 appeared to be more accu-
studies in vitro and in vivo on mouse models (including           rate (ROC area=92%, SE=98%, SP=50%) then the iDScore
inactivated mutation of the MC1R gene and BRAFV600E               2018 (ROC area = 90%, SE = 97%, SP = 48%) (Table 4,
mutation) suggest that the pheomelanin phenotype may              Figure 3), and to surpass the experts in terms of SE (+13%)
facilitate skin carcinogenesis through either an UV-depen-        and SP (+9%).
dent (ie accumulation of DNA damage through oxidative                The present study has some limitations. First, although
stress) and an UV-independent pathway [49-50]. It is under-       the number of eMM lesions selected was enough to obtain
stood that this parameter should be regarded as a body-site       an adequate discriminant power, the whole sample size was
and sun-exposure independent risk factor for MM, with             limited. Secondly, the evaluators were forced to use a series
reported with rates between 1.4 and 3 [45-48]. We indeed          of selected dermoscopic parameters (ie iDScore checklist
observed a discriminant power for this parameter in the dd        2018) in the dermoscopic pattern analysis: this selection of
between aN and EMs (P = 0.005) according to univariate            7 dermoscopic criteria has a practical value but could also
analysis (Table 1).                                               be regarded as a bias in the sense that some recent additional
    When comparing the new iDScore 2021 checklist with            terminology/dermoscopic features of aN and eMM is pre-
its precursor iDScore 2018 (Table 4), some differences can        ventively excluded.
be highlighted.                                                      Taken together, the present findings suggest the follow-
    First, the training phase was based on a total of 19 param-   ing consideration. First, the investigation approach of de-
eters (3 anamnestic risk factors + 5 phenotypic risk factors      veloping a scoring checklist based on an integrated dataset
+ 2 anagraphic data + 2 aMSL data + 7 aMSLs dermoscopic           of patients demographic, phenotypic and anamnestic risk
features) instead of the 10 parameters (2 anagraphic data +       factors integrated with objective clinical and dermoscopic
2 aMSL data + 7 aMSLs dermoscopic features) of the iD-            data could help dermatologists in early identification of the
Score 2018.                                                       patient with high risk of MM in routinary medical consulta-
    Second, some modifications were applied in order to           tions. Second, using an integrated risk score algorithm such
simplify the checklist final use: estimation of 3 age groups      as the new 2021 iDScore checklist with 9 parameters, each
with different coefficient instead of using 4 age groups; se-     one associated with a peculiar partial score, could be pro-
lection of one body area with the high discriminant power,        posed as a rapid and easy tool to screen patients with mul-
instead of using 3 body areas; reduction of dermoscopic           tiple aMSLs and assign them a progressive predictive score
variables from 7 to 4. Concerning this final selection of         ranging from an aN to an eMM diagnosis based on statisti-
4 inter-dependently significant dermoscopic variables, the        cal probability. Third, managing these patients according to
3 left out were: White Scar-like Areas, Shiny White Streaks       the peculiar aMSL risk score could help not only in reducing
and Atypical network. White Scar-like Areas and Shiny             the rate of inappropriate excision for benign lesions but also
White Streaks were significant in the univariate analysis,        in organizing the proper follow-up timing (3/6/9/12 months)
but not in the multivariate analysis, as they did not reached     during daily practice.



Original Article | Dermatol Pract Concept. 2022;12(3):e2022134                                                                9
      Further studies on larger integrated datasets from multi-                   atypical nevi and early melanomas: usefulness of an integrated
ple centers are required to confirm the validity of the present                   clinical-dermoscopic method (iDScore). J Eur Acad Derma-
                                                                                  tol Venereol. 2021;35(3):650-657. DOI: 10.1111/jdv.16847.
approach and proceed to the testing phase of the proposed
                                                                                  PMID: 32743829.
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                                                                                  tion by the combination of human and artificial intelligence. Eur
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Original Article | Dermatol Pract Concept. 2022;12(3):e2022134                                                                                   11
