Classification of facial vitiligo: A cluster analysis of 473 patients

Development and Validation of the Fingertip Unit for Assessing Facial Vitiligo Area Scoring Index (F-VASI)

BACKGROUND

Facial involvement of vitiligo is an important factor in patient's life and has often been evaluated separately from body surface area in clinical trials. However, no reliable measurement tools to measure facial vitiligo specifically are available thus far.

OBJECTIVE

To develop and validate a practical instrument for assessing facial vitiligo METHODS: The ratios of a hand to a fingertip unit (FTU) of 98 healthy volunteers (age range: 2-69 years) were calculated to define the FTU. Facial Vitiligo Area Scoring Index (F-VASI) was measured as the sum of all FTUs of each vitiligo lesion on the face (range: 0-112 FTU). In the validation study, 6 raters evaluated 11 patients with facial vitiligo twice at an interval of 2 weeks.

RESULTS

One hand was measured at 32.1±1.3 FTU, and it was highly consistent among subjects across different age groups, genders, and races. F-VASI showed remarkably high accuracy (concordance correlation coefficient: 0.946, smallest detectable change: 2.2 FTU) as well as high intra-rater reliability (intraclass correlation coefficient: 0.903) and inter-rater reliability (0.903).

LIMITATIONS

Lack of dynamic validation of responsiveness CONCLUSION: F-VASI using the FTU is an intuitive, precise, and reliable instrument for assessing the extent of facial involvement in vitiligo patients.

Quantitative Outcome Assessment of Color Match and the Extent of Repigmentation after Pixel Array Epidermal Grafting for Head and Neck Stable Vitiligo: A Prospective Cohort Study

BACKGROUND

Epidermal grafting with an automatic harvesting system has been reported as a simple and efficacious procedure for stable vitiligo. However, no prospective cohort study has quantitatively evaluated the color matching and extent of repigmentation in the head and neck area by this method.

OBJECTIVE

To evaluate the color matching and extent of repigmentation after pixel array epidermal grafting by image analysis software and physicians' naked eye.

METHODS

Ten Asian patients with head and neck vitiligo lesions stable for at least 6 months were treated with pixel array epidermal grafting with an automatic harvesting system and post-grafting phototherapy. The patients were evaluated 1, 3, and 6 months post grafting for the percentage of repigmentation by blinded physicians' assessment and image analysis software. The color matching index of repigmentation was evaluated by measuring the melanin index in the grafted area and the juxta non-vitiliginous area.

RESULTS

The average blister harvest time was 46.3 ± 9.7 min. The area percentile of repigmentation by the image analysis software were 32.3 ± 26.8, 64.6 ± 29.4, and 76.5 ± 25.9 at 1, 3, and 6 months post grafting, respectively. There were no significant differences between the physicians' assessments and the results from the image analysis software. The change in the area percentile of repigmentation between 3 and 6 months post grafting was only statistically significant using image analysis software. The grafted area achieved a color match of 83.1 ± 13.4% that of the juxta non-vitiliginous area 6 months after grafting. Three patients had repigmentation of leukotrichia.

CONCLUSION

By quantitative measurement, uniform pixel array micrografts provide a very good extent of repigmentation and color match in the head and neck area. Image analysis software revealed a steady increase in repigmentation after POM3 until POM6, which was not detected by subjective assessment.

Semi-Automatic Tool for Vitiligo Detection and Analysis

Vitiligo vulgaris is an autoimmune disease which causes a strong reduction of the cells producing melanin, which is the main skin pigment. This results in the growth of white patches on patients’ skin, which are more or less visible, depending on the skin phototype. Precise, objective and fast detection of vitiligo patches would be crucial to produce statistically relevant data on huge populations, thus giving an insight on the disease. However, few methods are available in literature. In the present paper, a semi-automatic tool based on image processing to detect facial vitiligo patches is described. The tool requires pictures to be captured under black light illumination, which enhances patches contrast with respect to healthy skin. The user is only required to roughly define the regions of interest and set a global threshold, thus, no specific image-processing skills are required. An adaptive algorithm then automatically discerns between vitiligo and healthy skin pixels. The tools also allow for a statistical data interpretation by overlapping the detected patches of all patients on a face template through an occurrence map. Preliminary results obtained on a small population of 15 patients allowed us to assess the tool’s performance. Patch detection was checked by an experienced dermatologist, who confirmed the detection for all the studied patients, thus supporting the effectiveness of the proposed tool.

Topographic Phenotypes of Alopecia Areata and Development of a Prognostic Prediction Model and Grading System: A Cluster Analysis

Importance

Diverse assessment tools and classification have been used for alopecia areata; however, their prognostic values are limited.

Objective

To identify the topographic phenotypes of alopecia areata using cluster analysis and to establish a prediction model and grading system for stratifying prognoses.

Design, Setting, and Participants

A retrospective cohort study of 321 patients with alopecia areata who visited a single tertiary referral center between October 2012 and February 2017 and underwent 4-view photographic assessment.

Exposures

Clinical photographs were reviewed to evaluate hair loss using the Severity of Alopecia Tool 2. Topographic phenotypes of alopecia areata were identified using hierarchical clustering with Ward's method. Differences in clinical characteristics and prognosis were compared across the clusters. The model was evaluated for its performance, accuracy, and interobserver reliability by comparison to conventional methods.

Main Outcomes and Measures

Topographic phenotypes of alopecia areata and their major (60%-89%) and complete regrowth probabilities (90%-100%) within 12 months.

Results

A total of 321 patients were clustered into 5 subgroups. Grade 1 (n = 200; major regrowth, 93.4%; complete regrowth, 65.2%) indicated limited hair loss, whereas grades 2A (n = 66; major regrowth, 87.8%; complete regrowth, 64.2%) and 2B (n = 20; major regrowth, 73.3%; complete regrowth, 45.5%) exhibited greater hair loss than grade 1. The temporal area was predominantly involved in grade 2B, but not in grade 2A, despite being comparable in total extent of hair loss. Grade 3 (n = 20; major regrowth, 45.5%; complete regrowth, 25.5%) included diffuse or extensive alopecia areata, and grade 4 (n = 15; major regrowth, 28.2%; complete regrowth, 16.7%) corresponded to alopecia (sub)totalis. No significant differences in prognosis (hazard ratio [HR] for major regrowth, 0.79; 95% CI, 0.56-1.12) were found between grades 2A and 1, whereas grades 2B (HR, 0.41; 95% CI, 0.21-0.81), 3 (HR, 0.24; 95% CI, 0.12-0.50), and 4 (HR, 0.16; 95% CI, 0.06-0.39) had significantly poorer response. Among multiple models, the cluster solution had the greatest prognostic performance and accuracy. The tree model of the cluster solution was converted into the Topography-based Alopecia Areata Severity Tool (TOAST), which revealed an excellent interobserver reliability among 4 dermatologists (median quadratic-weighted κ, 0.89).

Conclusions and Relevance

Temporal area involvement should be independently measured for better prognostic stratification. The TOAST is an effective tool for describing the topographical characteristics and prognosis of hair loss and may enable clinicians to establish better treatment plans.