10.5826/dpc.1401a51
Introduction: Dermoscopy has become widespread in the diagnosis of inflammatory skin diseases. Cutaneous vasculitis (CV) is characterized by inflammation of vessels, and a rapid and reliable technique is required for the diagnosis.
Objectives: We aimed to define CV dermoscopic features and increase the diagnostic accuracy of dermoscopy with machine learning (ML) methods.
Methods: Eighty-nine patients with clinically suspected CV were included in the study. Dermoscopic images were obtained before biopsy using a polarized dermoscopy. Dermoscopic images were independently evaluated, and interobserver variability was calculated. Decision Tree, Random Forest, and K-Nearest Neighbors were used as ML classification models.
Results: The histopathological diagnosis of 58 patients was CV. Three patterns were observed: homogeneous pattern, mottled pattern, and meshy pattern. There was a significant difference in background color between the CV and non-CV groups (P = 0.001). The milky red and livedoid background color were specific markers in the differential diagnosis of CV (sensitivity 56.7%, specificity 96.3%, sensitivity 29.4%, specificity 99.2%, respectively). Red blotches were significantly more common in CV lesions (P = 0.038). Red dots, comma vessels, and scales were more common in the non-CV group (P = 0.002, P = 0.002, P = 0.003, respectively). Interobserver agreement was very good for both pattern (???? = 0.869) and background color analysis (???? = 0.846) (P &lt; 0.001). According to ML classifiers, the background color and lack of scales were the most significant dermoscopic aspects of CV.
 Conclusions: Dermoscopy may guide as a rapid and reliable technique in CV diagnosis. High accuracy rates obtained with ML methods may increase the success of dermoscopy.
text/xml
2160-9381
3583
en
Yunus Gural
https://creativecommons.org/licenses/by-nc/4.0
Dermatology Practical & Conceptual
inflammoscopy
Dermoscopic Features of Cutaneous Vasculitis
Text.Serial.Journal
https://dpcj.org/index.php/dpc
Original Article
2024-01-31
2023-06-02
2024-01-31
2024-02-21
e2024051
Dermatol Pract Concept
Introduction: Dermoscopy has become widespread in the diagnosis of inflammatory skin diseases. Cutaneous vasculitis (CV) is characterized by inflammation of vessels, and a rapid and reliable technique is required for the diagnosis.
Objectives: We aimed to define CV dermoscopic features and increase the diagnostic accuracy of dermoscopy with machine learning (ML) methods.
Methods: Eighty-nine patients with clinically suspected CV were included in the study. Dermoscopic images were obtained before biopsy using a polarized dermoscopy. Dermoscopic images were independently evaluated, and interobserver variability was calculated. Decision Tree, Random Forest, and K-Nearest Neighbors were used as ML classification models.
Results: The histopathological diagnosis of 58 patients was CV. Three patterns were observed: homogeneous pattern, mottled pattern, and meshy pattern. There was a significant difference in background color between the CV and non-CV groups (P = 0.001). The milky red and livedoid background color were specific markers in the differential diagnosis of CV (sensitivity 56.7%, specificity 96.3%, sensitivity 29.4%, specificity 99.2%, respectively). Red blotches were significantly more common in CV lesions (P = 0.038). Red dots, comma vessels, and scales were more common in the non-CV group (P = 0.002, P = 0.002, P = 0.003, respectively). Interobserver agreement was very good for both pattern (???? = 0.869) and background color analysis (???? = 0.846) (P &lt; 0.001). According to ML classifiers, the background color and lack of scales were the most significant dermoscopic aspects of CV.
 Conclusions: Dermoscopy may guide as a rapid and reliable technique in CV diagnosis. High accuracy rates obtained with ML methods may increase the success of dermoscopy.
Yunus Gural
2024/01/31
10.5826/dpc.1401a51
e2024051
Firat University Faculty of Science, Division of Statistics, Elazig, Turkey
2160-9381
inflammoscopy
en
e2024051
Jaworek-Korjakowska J. A Deep Learning Approach to Vascular Structure Segmentation in Dermoscopy Colour Images. Biomed Res Int. 2018;2018:5049390. DOI: 10.1155/2018/5049390. PMID: 30515404. PMCID: PMC6236870.
Dermoscopic Features of Cutaneous Vasculitis
https://dpcj.org/index.php/dpc/article/download/3583/2429
        Dermoscopic Features of Cutaneous Vasculitis
         Ozge Sevil Karstarli Bakay1, Nida Kacar1, Melis Gonulal2, Nese Calli Demirkan3,
                             Hülya Cenk1, Sule Goksin1, Yunus Gural4


1 Pamukkale University Faculty of Medicine, Department of Dermatology, Denizli, Turkey
2 Tepecik Education and Research Hospital Department of Dermatology, University of Health Sciences Turkey, İzmir, Turkey
3 Department of Pathology, Medical Faculty, Pamukkale University, Denizli, Turkey
4 Firat University Faculty of Science, Division of Statistics, Elazig, Turkey



Key words: Dermoscopy, machine learning, cutaneous vasculitis, inflammoscopy
Citation: Karstarli Bakay OS, Kacar N, Gonulal M, et al. Dermoscopic Features of Cutaneous Vasculitis. Dermatol Pract Concept.
2024;14(1):e2024051. DOI: https://doi.org/10.5826/dpc.1401a51
Accepted: August 26, 2023; Published: January 2024
Copyright: ©2024 Karstarli Bakay 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: Ozge Sevil Karstarli Bakay, Pamukkale University, Faculty of Medicine, Department of Dermatology, 20160,
Kınıklı Campus. Denizli, Turkey. Phone: +095376755441 E-mail: ozgekarstarli@hotmail.com




  ABSTRACT             Introduction: Dermoscopy has become widespread in the diagnosis of inflammatory skin diseases.
                       Cutaneous vasculitis (CV) is characterized by inflammation of vessels, and a rapid and reliable tech-
                       nique is required for the diagnosis.
                       Objectives: We aimed to define CV dermoscopic features and increase the diagnostic accuracy of
                       dermoscopy with machine learning (ML) methods.
                       Methods: Eighty-nine patients with clinically suspected CV were included in the study. Dermoscopic
                       images were obtained before biopsy using a polarized dermoscopy. Dermoscopic images were inde-
                       pendently evaluated, and interobserver variability was calculated. Decision Tree, Random Forest, and
                       K-Nearest Neighbors were used as ML classification models.
                       Results: The histopathological diagnosis of 58 patients was CV. Three patterns were observed: homo-
                       geneous pattern, mottled pattern, and meshy pattern. There was a significant difference in background
                       color between the CV and non-CV groups (P = 0.001). The milky red and livedoid background color
                       were specific markers in the differential diagnosis of CV (sensitivity 56.7%, specificity 96.3%, sensi-
                       tivity 29.4%, specificity 99.2%, respectively). Red blotches were significantly more common in CV
                       lesions (P = 0.038). Red dots, comma vessels, and scales were more common in the non-CV group
                       (P = 0.002, P = 0.002, P = 0.003, respectively). Interobserver agreement was very good for both pattern
                       (κ = 0.869) and background color analysis (κ = 0.846) (P < 0.001). According to ML classifiers, the
                       background color and lack of scales were the most significant dermoscopic aspects of CV.
                       Conclusions: Dermoscopy may guide as a rapid and reliable technique in CV diagnosis. High accura-
                       cy rates obtained with ML methods may increase the success of dermoscopy.



Original Article | Dermatol Pract Concept. 2024;14(1):e2024051                                                                            1
Introduction                                                          method to improve the accuracy of dermoscopic diagnosis
                                                                      of vasculitis.
Vasculitis is defined as blood vessel wall inflammation and can
affect small, medium, or large vessels. Clinical manifestations
                                                                      Methods
occur depending on the size of the affected vessels, involving
many organ systems in the body [1]. The most common form              This prospective study was conducted at Pamukkale
of cutaneous vasculitis (CV) is cutaneous small-vessel vascu-         ­University and included patients with clinically suspected
litis (CSVV). CSVV affects small vessels in the superficial and       CV. The procedures of the clinical photograph, dermoscopic
middle dermis of the skin, including arterioles, capillaries,         examination, and skin biopsy had been performed after the
and postcapillary venules [1,2]. CSVV typically presents with         informed consent of the patients and were conducted fol-
palpable or macular purpura, but urticarial papules, pustules,        lowing the Helsinki Declaration. The study was approved
blisters, petechiae, or targetoid lesions may be seen [1].            by the Ethics Committee of the Pamukkale University Train-
    Skin biopsy remains the gold standard for diagnosis,              ing and Research Hospital, Denizli, Turkey. Individuals
classification, and exclusion of imitators in CV. The term            over the age of 18 years with macular or papular purpuric
leukocytoclastic vasculitis (LCV) refers to the histopatho-           lesions were included in the study. A 4-mm punch biopsy
logical description of a common form of CSVV [2]. Clas-               was taken from palpable purpura that emerged within 24-
sical histopathological findings of LCV are neutrophilic              48 hours. Biopsy was taken from all patients for definitive
infiltration with signs of leukocytoclasia, fibrinoid necrosis,       diagnosis. Dermoscopic examination preceded biopsy, and
extravasated red blood cells, and damaged endothelial cells.          both were performed on the same lesions. It was planned
Histopathologically, LCV can be found in ANCA-associated              to include patients presenting within 3 years of local eth-
vasculitis, immunocomplex vasculitis such as IgA vasculi-             ics committee approval. The dermoscopic images of each
tis (Henoch–Schonlein purpura), urticarial vasculitis, and            lesion were taken using a polarized dermoscopy (DermLite
vasculitis-related systemic diseases [3].                             Foto, ×10 magnification; 3Gen) adopted on a digital cam-
    In patients with CV, radiological and laboratory evaluations      era (Canon G10; Canon). Among 99 patients with purpuric
should be performed to investigate systemic involvement [4].          lesions, we excluded ten patients who did not have a defi-
Since histopathological diagnosis takes time, many patients           nite histopathological diagnosis or clear dermoscopic image
with clinical suspicion of CV are exposed to unnecessary lab-         records. A checklist was created based on a review of arti-
oratory examinations or immunosuppressive therapies. There-           cles on dermoscopic examination of purpuric lesions and all
fore, there is a need for a rapid and reliable diagnostic tool that   images were assessed by a dermatologist (NK) experienced
will reduce unnecessary evaluations and invasive procedures.          in dermoscopy [5,15-19]. Three dermatologists (OSKB, HC,
    A dermatoscope is an optical device that allows visual-           SG) independently evaluated the pattern and background of
ization of the epidermis, dermo-epidermal junction, and pap-          the dermoscopic images, and interobserver agreement was
illary dermis, which cannot be seen with the naked eye. Few           calculated. All authors evaluating dermoscopic images were
studies with small groups have suggested that dermoscopy              blind to histopathological diagnosis.
can be used as a rapid and noninvasive method for the diag-
nosis of CV [5-9].                                                    Statistical Analysis
    The correct diagnosis rate of both pigmented and                  Statistical package program SPSS 22.0 was used in the analy-
non-pigmented lesions with dermoscopic examination is                 sis of the data. The interobserver agreement was determined
higher than the unaided eye [10,11]. However, dermoscopy              using Fleiss Kappa statistics. The relationship between dis-
requires special training and experience. Manual review of            ease groups (CV and non-CV) and dermoscopic findings
dermoscopy images, even by well-trained dermatologists, is            was evaluated using Cramer's V statistics. The frequency,
often time-consuming and subjective. There is increasing re-          sensitivity, and specificity of dermoscopic features found in
search on the development of artificial intelligence (AI) tech-       cutaneous vasculitis were calculated.
niques to minimize diagnostic errors due to the difficulty and            Decision Tree, Random Forest (RF), and K-nearest
subjectivity of visual interpretation [12,13]. The field of AI        neighbors (KNN) were used as classification models for ma-
includes machine learning (ML) and its subset, deep learn-            chine learning. The dermoscopic features were input as in-
ing (DL) techniques, which use complex algorithms to build            dependent variables, while the main disease groups (CV and
models that can make these predictions [14].                          non-CV) were used as dependent variables. Decision tree
                                                                      analysis is a predictive modeling tool that can be applied
                                                                      in many areas and uses a branching structure to show the
Objectives
                                                                      consequences of a decision. Decision trees can be created
We aimed to investigate the dermoscopic findings of CV. In            with an algorithmic approach that can split the data set in
addition, we analyzed dermatoscopic findings using the ML             different ways according to different conditions. Decisions

2                                                                         Original Article | Dermatol Pract Concept. 2024;14(1):e2024051
are the most powerful algorithms that fall under the cat-             groups (Table 1). The total kappa value was 0.869 (Table 2)
egory of supervised algorithms. It can be used to map the             in the evaluation of the dermoscopic images of the three
possible consequences of a decision. Each node represents             dermatologists in terms of pattern, representing very good
a possible outcome [20]. RF is a decision tree-based en-              agreement (P < 0.001).
semble learning method. It follows an algorithm that uses                 Three background colors were observed: yellowish-
a bootstrap resampling technique to select samples repeat-            erythematous, milky-red, and livedoid. There was a signif-
edly randomly from the original training sample set as the            icant difference in background color between the CV and
training set and the remaining samples as the test set [21].          non-CV groups (P = 0.001). The yellowish erythematous
KNN is a non-parametric classification method that collects           background color was detected in 80.6% of the non-CV
existing classes and then classifies the new classes according        group. The milky red (sensitivity 56.7%, specificity 96.3%)
to the comparison measure [15]. All dermoscopic features              and livedoid background color (sensitivity 29.4%, specificity
were run through machine learning classifiers to assess the           99.2%) was found as specific markers in the differential di-
accuracy of CV diagnosis. We take 80% of the patients as              agnosis of CV lesions, showing low sensitivity. Interobserver
the training set and 20% of the patients as the validation            agreement was also very good for background color analysis
set. The classifiers performance was assessed using accuracy,         (Table 2) (κ = 0.846, P < 0.001).
precision, sensitivity, and specificity.                                  The most common dermoscopic finding in CV was red
                                                                      globules (N = 43, 74.1%). Red blotches were detected in

Results                                                               26 (44.8%) CV lesions, but it was seen in only 7 (22.6%)
                                                                      lesions in the non-CV group, and this difference was statis-
Histopathological diagnoses of 89 patients (42 women; 47              tically significant (P = 0.038; sensitivity 78.8%, specificity
men) were CV in 58 patients, including LCV in 48 patients,            42.9%) (Table 1).
Henoch-Schönlein purpura in 9 patients, and urticarial vas-               The rate of presence of red dots, comma vessels, and
culitis in 1 patient. In the histopathological diagnosis of the       scales in CV lesions was very low compared to the non-CV
remaining 31 patients, pigmented purpuric dermatosis (PPD)            group and this difference was statistically significant
was the most prevalent diagnosis with 14 patients, the others         (Table 1) (P = 0.002, P = 0.002, P = 0.003, respectively).
were 10 chronic dermatitis (CD), 2 lichen planus, 2 mycosis               Remarkably, none of the PPD lesions had blue-gray back-
fungoides, 1 erythema multiforme, 1 folliculitis and 1 pseu-          ground color. milky red and blue-gray background color
doxanthoma elasticum.                                                 were specific markers for CV (P = 0.002, sensitivity 56.7%,
    The most significant histological signs of LCV were the           specificity 99.8% and sensitivity 29.4%, specificity 100%,
deposition of fibrin in the vessel walls and perivascular poly-       respectively). Similar to CV, the most common dermoscopic
morphonuclear leukocytes in the upper or middle dermis.               finding in PPD was red globules (N = 13, 92.8%). Red dots
    Three patterns were observed according to the arrange-            (64.28%), comma (50%), and annular (57.1%) vessels were
ment of the vessels; 1) structureless purpuric area, termed           more common in PPD (P < 0.01).
as homogeneous, 2) multiple erythematous and/or bluish                    There was no correlation between dermoscopic patterns
blotches and/or globules and/or dots arranged uniformly               and histopathological findings such as erythrocyte extravasa-
over a structureless background, termed as mottled and                tion, the presence of fibrinogen, and eosinophil. Erythrocyte
3) consists of a vascular network, we termed as reticular             extravasation was observed more frequently in lesions with a
(Figure 1). Mottled pattern was commonly detected in both             yellowish erythematous background color. The relationship




 Figure 1. Dermoscopic patterns of purpuric lesions. (A) Homogeneous pattern: structureless purpuric area. (B) Mottled pattern: multiple
 erythematous and/or bluish blotches and/or globules and/or dots arranged uniformly over a structureless background. (C) Reticular pat-
 tern: branched vessels of which branches were arranged in a meshy configuration over a structureless background.

Original Article | Dermatol Pract Concept. 2024;14(1):e2024051                                                                         3
                           Table 1. Dermoscopic findings observed in purpuric lesions.
                                                                    CV                Non-CV
                                                                   N (%)               N (%)            Cramer V            P value
 Pattern                   Homogeneous                        16 (28.1)               9 (29)
                           Mottled                            38 (66.7)              19 (63.3)            0.190              0.524
                           Meshy                                   3 (5.3)             3 (9.7)
 Background color          Yellowish Erythematous             23 (39.7)              25 (80.6)
                           Milky-red                          25 (43.1)               4 (12.9)            0.392              0.001
                           Blue-gray                          10 (17.2)                2 (6.5)
 Linear vessels                                               17 (29.3)               8 (25.8)            0.037              0.726
 Red dots                                                     16 (27.6)              19 (61.3)            0.329              0.002
 Red globules                                                 43 (74.1)              23 (74.2)            0.001              0.995
 Red blotches                                                 26 (44.8)               7 (22.6)            0.219              0.038
 Blue globules                                                 8 (13.8)               5 (16.1)            0.032              0.766
 Annular vessels                                               8 (13.8)               8 (25.8)            0.149              0.160
 Comma vessels                                                     5 (8.6)           11 (35.5)            0.333              0.002
 Scale                                                         8 (13.8)              13 (41.9)            0.316              0.003
 Branched vessels                                             13 (22.4)                3 (9.7)            0.158              0.136



      Table 2. Fleiss kappa analysis to assess the interobserver agreement of dermoscopic patterns
                              and background colors in purpuric lesions.
                                         Agreement on Individual Categories                                  Overall Agreement
                                    Rating Category                 Fleiss Kappa                 P        Fleiss Kappa             P
 Patern                     Homojen                                     0.900               <0.001
                            Mottled                                     0.935               <0.001             0.869            <0.001
                            Reticular                                   0.703               <0.001
 Background color           Yellowish-erythematous                      0.865               <0.001
                            Milky red                                   0.869               <0.001             0.846            <0.001
                            Blue-gray                                   0.769               <0.001




Table 3. Machine learning classifier performance                           The three dermoscopic findings with the highest impor-
                   measures.                                          tance score in the diagnosis of CV according to RF classifica-
                     Precision                                        tion were scale, background color, and red dots, respectively.
                      (Positive                                       Decision Tree classification presented an algorithm using
                     Predictive Sensitivity Specificity               80% of samples for training data and 20% of samples for
            Accuracy   Value)      (%)         (%)                    validation data (Figure 2).
 RF           0.882       0.882         0.882         0.871
 Decision     0.824       0.824         0.802         0.631
 Tree                                                                 Conclusions
 KNN          0.706       0.690         0.706         0.617
                                                                      Dermoscopic identification of vascular lesions may guide pa-
KNN = K-nearest neighbors; RF = Random Forest.                        tient management. Most vascular proliferative lesions have
                                                                      well-defined, variable-colored areas called lacunae on der-
between erythrocyte extravasation and background color                moscopy [22]. In addition to proliferative vascular lesions,
was statistically significant (Cramer V=0.275, P = 0.009).            dermoscopic features of inflammatory vascular lesions are
    RF, Decision Tree, and KNN give classification rates              a matter of interest. Pigmented purpuric dermatoses (PPDs)
of 88.0%, 82.40%, and 70.6% accuracy, respectively. The               are a group of inflammatory skin diseases that may be mis-
highest accuracy, precision, sensitivity, and specificity values      taken as vasculitides [23]. Common dermoscopic character-
were achieved with RF classification (Table 3).                       istics of PPDs include a copper-red background, rounded



4                                                                            Original Article | Dermatol Pract Concept. 2024;14(1):e2024051
    Figure 2. Decision Tree Plot: decision tree classification presented an algorithm using 17 samples for training data and 72 samples
    for validation data. By following this algorithm, Decision Tree predicted cutaneous vasculitis diagnosis with approximately 80%
    accuracy.

to oval dots, gray dots, and a network of brownish-to-gray             [5,6,9]. On the other hand, purpuric dots or globules on an
linked lines [22].                                                     orange-brown background have been suggested as an im-
    Four basic dermoscopic patterns have been reported for             portant dermoscopic finding for urticarial vasculitis [6 9].
purpuric lesions including homogeneous, mottled, perifollic-               In the study of Choo et al, which included 15 true vas-
ular, and epidermal purpuric by Vazquez-Lopez et al. The               culitis and 15 vasculopathy cases, found the mottled pattern
homogeneous pattern has been suggested to characterize a               and orange-brown background most frequently in both
noninflammatory form of purpura, such as bleeding diathe-              groups. The term vasculopathy is used to describe conditions
sis, presenting with wide, homogeneous, structureless pur-             with significant histopathological vascular changes that do
puric areas; while the mottled pattern, consisting of multiple         not fully meet the criteria for vasculitis [24]. The most com-
small, speckled, blurred purpuric blotches and/or globules             mon vasculopathy detected in this study was PPD (N = 14).
over a purple to the orange-brown background, to charac-               The subgroups of PPDs observed in our patients included
terize an inflammatory form, such as LCV and PPD [17]. In              progressive pigmentary dermatosis (Schamberg disease) and
the present study, the most common pattern was the mottled             lichen aureus. They reported that blue-gray blotches were
pattern. However, the homogeneous pattern was detected in              found only in the vasculitis group and an important find-
a significant part of the inflammatory disease lesions. With           ing for differentiation [7]. In our study, there were 58 pa-
these results, we concluded that the homogeneous pattern               tients with true vasculitis and 14 patients with vasculopathy
not only characterizes the non-inflammatory form of pur-               (PPD). Similarly, we found the most common mottled pat-
pura but can also be seen in inflammatory skin diseases.               tern in both groups and yellowish erythematous background
Although purpura, the most common manifestation of cuta-               color in PPD. The yellowish erythematous background of
neous vasculitis, is highlighted by dermoscopy, there is little        PPD is related to the presence of a dense lymphocytic and
evidence of the dermoscopic pattern of diseases included in            histiocytic infiltrate in the dermis caused by the extravasa-
this group [17,18].                                                    tion of red blood cells and the presence of hemosiderin in
    In the literature, most of the studies on dermoscopic              histiocytes [16]. Unlike the study of Choo et al, the most
findings of cutaneous vasculitis focused on the differen-              common background color in CV lesions in our study was
tial diagnosis of common urticaria and urticarial vasculitis           milky red. This difference is most likely due to the age of



Original Article | Dermatol Pract Concept. 2024;14(1):e2024051                                                                            5
the vasculitis lesions. Differences in background color reflect       findings. We also evaluated dermoscopic data with RF, Deci-
the status of extravasated erythrocytes. Intact erythrocytes          sion Tree, and KNN, which are the mostly used ML classifica-
reflect a purplish color while siderophages reflect a yellow-         tion methods [26]. We achieved over 80% accurate diagnosis
ish color [17]. Milk red background color is more likely in           of CV with RF (88%) and Decision Tree (82%). The sensitiv-
the early period, however, the probability of encountering an         ity, specificity, and positive predictive value of the RF classifier
orange-brown color increases in the late period. Similarly, in        were also above 80% (Table 3). Although studies that evaluate
our study, blue-gray background color (Figure 3) was only             dermoscopic images with AI mostly focus on melanoma, there
present in cases of true vasculitis, and this difference was          are also studies on different diseases such as non-melanoma
statistically significant. Necrotic lesions are seen by dermos-       skin cancers, onychomycosis, and rosacea. [13,27-29]. In our
copy as whitish-blue patches [17]. Probably because necro-            literature review, no study was found in which vasculitis der-
sis is not expected in vasculopathy, bluish-gray patches are          moscopy was combined with AI. However, there were studies
not observed in dermoscopy. In the present study, red glob-           in which the dermoscopy of vascular structures, which has
ules were the most common finding in both CV and PPD,                 an important place in vasculitis dermoscopy, was evaluated
whereas red dots, annular and comma vessels were signifi-             with AI [26,30]. In our study, RF and decision tree classifiers,
cantly more common in PPD. Red globules in dermoscopy of              which are ML methods, highlighted background color, scales,
LCV are reflections of extravasated erythrocytes secondary            red dots, and comma vessels as distinctive dermoscopic find-
to fibrinoid degeneration of small vessel walls. However, the         ings in the classification of purpuric lesions.
red globules of PPD are associated with variable amounts                  The relatively small number of samples is a limitation of
of erythrocytes, lymphocytes, and siderophages surrounding            this study. However, its strengths are that it is one of the few
swollen blood vessels in the upper dermis [17]. Presumably,           studies in the literature in which dermoscopic data of cutane-
these swollen blood vessels seem as comma or annular ves-             ous vasculitis is examined and that interobserver agreement
sels on dermoscopy. We suggest that, since it is difficult to         has been evaluated.
see intact vascular structures in CV, most likely due to fibri-           In conclusion, although the histopathological examina-
noid degeneration, comma/annular vessels are few and the              tion is the gold standard in the diagnosis of cutaneous vascu-
red blotches that we detect quite frequently in CV (44.8%)            litis, the evaluation of patients with dermoscopy in the first
reflect fragmented vascular structures.                               step seems valuable for patient management. Red globules
    Dotted vessels in a patchy distribution on a dull red back-       and blotches in a mottled distribution on a milky-red back-
ground and yellow scales have been identified as CD most              ground without scales were the most frequent dermoscopic
common dermoscopic features [25]. In our study, scales,               features for CV.
dotted and comma vessels were significantly more common
in the dermatoses group and were important dermoscopic
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Original Article | Dermatol Pract Concept. 2024;14(1):e2024051                                                                                7
