J AM ACAD DERMATOL
342 Letters
AUGUST 2015
Andre Vadimovich Moyakine, MD,a Denise Josephina Johanna Hermans, MD, PhD,a Joris Fuijkschot, MD, PhD,b and Catharina Joanna Maria van der Vleuten, MD, PhDa Department of Dermatology, Hecovan Team for Hemangioma and Vascular Malformations,a and Department of Pediatrics,b Radboud University Medical Centre, The Netherlands Funding sources: None.
Fig 2. Number of patients with minus scores in 1 single VWS section. VWS, Van Wiechen-scheme, a Dutch screening instrument used by the Well Child Preventive Health Care Clinics (WCPHCC ) to assess the psychomotor development of children between the age of 1 month and 4.5 years. A minus score of $3 in a single section is considered deviant with an increased risk of psychomotor developmental delay.
section. There are no distinct cutoff points that define developmental delay. Before the study, we defined 3 or more minus scores in any 1 particular section as a deviant section. This is regarded as an abnormal VWS outcome, predicts an increased risk for developmental delay, and is an indication for referral to a pediatrician. The latest available assessment points (78.6% obtained between 2 and 4 years of age) were used. Only 1 child (1%) (95% CI: 0.03%; 5.29%) scored 3 minus scores in 1 section (communication) on the VWS and was therefore labeled as having an increased risk for psychomotor developmental delay (Fig 2). This was a 3-year-old child of immigrant parents. Cultural aspects and a language barrier may have accounted for his delayed communication skills. No child was referred to a pediatrician for further evaluation of possible psychomotor delay at any stage. None of the 5 propranolol-treated twins scored 3 or more minus scores in 1 particular section, in contrast to the untreated twin sibling group in which 1 child scored 3 or more minus scores. In conclusion, our study did not detect evidence of psychomotor developmental delay among infants with IH treated with propranolol. It remains possible that propranolol treatment causes subtle adverse effects, which cannot be traced with tools such as the VWS. Future prospective studies using universal screening tools such as the Parents Evaluation of Developmental Status (PEDS), the Ages and Stages Questionnaire (ASQ),4,5 or more advanced neuropsychologic tests are needed to support our findings.
Andre Moyakine participated in generating, gathering, and interpreting the data for the study. He wrote the majority of the original draft of the paper and approved the final version of this paper. Denise Hermans participated in generating and gathering the data for the study, and in writing the paper. She approved the final version of this paper. Joris Fuijkschot participated in gathering and interpreting the data for the study. He approved the final version of this paper. Catharina van der Vleuten devised the design of the study, participated in generating and gathering the data for the study, and in writing the paper. She approved the final version of this paper and guarantees that all individuals who meet the journal’s authorship criteria are included as authors of this paper. Conflicts of interests: None declared. Correspondence to: Andre Vadimovich Moyakine, MD, PO box 9101, 6500 HB, Nijmegen E-mail: [email protected] REFERENCES 1. Langley A, Pope E. Propranolol and central nervous system function: potential Implications for pediatric patients with infantile hemangiomas. Br J Dermatol. 2014;1:13379. 2. Gesell A. The mental growth of the pre-school child: a psychological outline of normal development from birth to the sixth year, including a system of developmental diagnosis. New York: MacMillan; 1925. 3. Gesell A, Amatruda CS. Developmental diagnosis; normal and abnormal child development. Oxford, England: Hoeber; 1941. 4. Glascoe FP. Screening for developmental and behavioral problems. Ment Retard Dev Disabil Res Rev. 2005;11:173-179. 5. Limbos MM, Joyce DP. Comparison of the ASQ and PEDS in screening for developmental delay in children presenting for primary care. J Dev Behav Pediatr. 2011;32:499-511. http://dx.doi.org/10.1016/j.jaad.2015.04.053
A pilot study to determine vitiligo target size using a computer-based image analysis program To the Editor: Currently no objective outcome measure to determine the size of vitiligo lesions exists. The Vitiligo Area Scoring Index, commonly
J AM ACAD DERMATOL
Letters 343
VOLUME 73, NUMBER 2
Table I. Details of each patient’s demographics including race/ethnicity, clinical phenotype, treatment modalities used, and percentage change in lesion area over time (automated fill factor, also generally termed ‘‘percent repigmentation’’) for individual vitiligo lesions being monitored with serial photography
Patient
Ethnicity
Phenotype
Treatment
1
Hispanic
Acrofacial
None
2
African American
Nonsegmental vitiligo
NBUVB OC
3
Caucasian, Native Nonsegmental American vitiligo
NBUVB
4
Caucasian
Mucosal
5
Caucasian
Nonsegmental vitiligo
Tacrolimus NBUVB NBUVB
6
South Asian
Segmental
NBUVB
7
Greek
Acrofacial
TC
8
Caucasian
Nonsegmental vitiligo
NBUVB TC
9
South Asian
Acrofacial
NBUVB
10
African American
Segmental
NBUVB TC
Repigmentation
Site
Fill factor ( percent change in lesion area over time)
Baseline 1 mo 6 mo 0 2 19 0 1 9 Baseline 1 mo 2 mo 3 mo Chest 0 8 24 63 L elbow 0 2 9 17 R elbow 0 4 11 26 Baseline 1 mo 2 mo 3 mo PF L popliteal fossa 0 5 9 10 Marginal[PF L wrist 0 4 10 16 PF[marginal R popliteal fossa 0 3 5 11 Marginal[PF R antecubital 0 6 16 22 fossa Marginal and PF R forearm 0 11 25 36 Marginal and PF R wrist 0 9 25 28 Baseline 1 mo 2 mo PF Genital 0 2 5 Baseline 3 mo 5 mo 6 mo PF[marginal L hand 0 1 5 14 PF L knee 0 7 1 14 PF[marginal R hand 0 2 3 24 Marginal R foot 0 2 4 5 PF R knee 0 0 3 7 Baseline 1 mo 2 mo 3 mo Marginal L eye 0 3 19 28 Marginal L neck 0 3 4 6 Marginal L cheek 0 6 53 N/A Baseline 1 mo 2 mo PF L forehead 0 2 2 Marginal L hand 0 1 3 PF R forehead 0 3 7 Marginal R hand 0 2 1 Baseline 1 mo 2 mo 4 mo PF Forehead 0 2 3 16 Marginal L temple 0 83 91 N/A Marginal R temple 0 16 11 84 Baseline 1 mo 2 mo PF L lower lip 0 4 7 PF R oral 0 4 8 commissure PF R upper lip 0 2 21 Marginal[PF R cheek 0 8 86 Baseline 4 mo 8 mo 10 mo Marginal[PF R cheek 0 27 52 57
Marginal Marginal PF[marginal
L hand R hand
L, Left; N/A, not available; NBUVB, narrowband ultraviolet B phototherapy; OC, oral corticosteroids; PF, perifollicular; R, right; TC, topical corticosteroids.
used in clinical trials, and the Vitiligo European Task Force score both rely on subjective evaluation of investigator-measured units to determine the degree of disease involvement.
Previous studies using image analysis to evaluate treatment efficacy exist but also often rely on investigator-defined boundaries.1,2 Recognizing the need to create an objective, user-friendly method for
J AM ACAD DERMATOL
344 Letters
AUGUST 2015
Fig 1. A, Baseline image of vitiligo lesion on elbow after image analysis and identification of lesion contours. B, Three-month follow-up image of vitiligo lesion on elbow after image analysis showing automated fill factor of 26%.
monitoring vitiligo, we developed a novel image analysis program piloted on 10 patients treated for vitiligo. After institutional review board approval, patients with a diagnosis of vitiligo were recruited for a baseline and at least 2 follow-up visits. Being an observational pilot study, no standardized intervention was performed. Photographs of skin lesions were taken under same conditions using normal room lighting and a handheld digital camera (SX210 IS, Canon, Melville, NY). Image sequences were analyzed using a novel software program combining standard image-processing techniques. White balancing and contrast enhancement were used to normalize lighting differences. Color correction was then performed by adjusting red, green, and blue color channels separately to recreate color under neutral lighting. An image segmentation scheme that exploits the difference in features such as skin color between the lesion and adjacent skin was used to automatically identify lesion contours in the individual images.3 A scale invariant feature transformebased4 feature matching approach was used to transform and align identified lesions from follow-up images with those in the initial image. The areas of aligned lesions were compared to baseline lesion area to determine percentage change in area over time, termed the ‘‘automated fill factor’’ (ie, percent repigmentation). The automated program was highly sensitive to change, detecting differences in lesion area as low as 1%, and detecting both improvement and worsening of lesions (Table I). Validation was performed by comparison with investigator-traced images using Image J software5 on the first 4 sequential images from 8 randomly selected series of photographs. Differences between the follow-up lesion and baseline lesion area using this manual tracing method
was termed the ‘‘manual fill factor.’’ There was no significant difference between automated and manual methods. Examining aggregate data from the validation series using Wilcoxon rank sum test (N ¼ 24 for 8 lesions and 3 time periods each), the median difference between manual and automated fill factors was 2.3% (P ¼ .7). Individual time points were evaluated separately, again with no significant difference between automated and manual methods (median differences of 6%, 0.8%, and 2.3%, respectively, P [ .05). To determine intrarater variability, the same image series were run through the automated program 3 times. Similarly, investigators manually traced each image series 3 times with Image J analysis.5 Intrarater variability was 0 using the automated program and 3% using the manual program. The automated program was found to be reliable in multiple skin phototypes and able to measure marginal and perifollicular repigmentation (Fig 1). The program also detected improvement with therapeutic intervention. Because the software corrects for minor variations in lighting and perspective, there is no strict requirement for image capture conditions. The distinct advantage of our method is speed and ease of use. However, the sample size used for validation was small. The program proved most useful for patients with limited skin involvement or smaller discrete lesions. Limitations of this method include potential difficulty in assessing larger lesions or lesions over curved surfaces. Further, larger studies are needed to confirm the initial but promising results shown herein. Vaneeta M. Sheth, MD,a Rahul Rithe, PhD,b Amit G. Pandya, MD,c and Anantha Chandrakasan, PhDb
J AM ACAD DERMATOL VOLUME 73, NUMBER 2
Clinical Research Program, Department of Dermatology, Brigham and Women’s Hospital, Boston, Massachusettsa; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridgeb; and Department of Dermatology, University of Texas Southwestern Medical Center, Dallasc Supported by Foxconn Technology Group. Conflicts of interest: None declared. Correspondence to: Vaneeta M. Sheth, MD, Department of Dermatology, Brigham and Women’s Hospital, 221 Longwood Ave, Boston, MA 02115 E-mail: [email protected]
Letters 345
REFERENCES 1. Linthorst Homan MW, Wolkerstorfer A, Sprangers MA, van der Veen JP. Digital image analysis vs clinical assessment to evaluate repigmentation after punch grafting in vitiligo. J Eur Acad Dermatol Venereol. 2013;27:e235-e238. 2. AlGhamdi KM, Kumar A, Ta€ıeb A, Ezzedine K. Assessment methods for the evaluation of vitiligo. J Eur Acad Dermatol Venereol. 2012;26:1463-1471. 3. Li C, Huang R, Ding Z, Gatenby C, Metaxas DN, Gore JC. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process. 2011;20:2007-2016. 4. Lowe D. Distinctive image features from scale-invariant keypoints. Int J Comput Vision. 2004;60:91-110. 5. National Institutes of Health. Image J software. Available from: URL: http://imagej.nih.gov. Accessed April 14, 2014. http://dx.doi.org/10.1016/j.jaad.2015.04.035
342 Letters
AUGUST 2015
Andre Vadimovich Moyakine, MD,a Denise Josephina Johanna Hermans, MD, PhD,a Joris Fuijkschot, MD, PhD,b and Catharina Joanna Maria van der Vleuten, MD, PhDa Department of Dermatology, Hecovan Team for Hemangioma and Vascular Malformations,a and Department of Pediatrics,b Radboud University Medical Centre, The Netherlands Funding sources: None.
Fig 2. Number of patients with minus scores in 1 single VWS section. VWS, Van Wiechen-scheme, a Dutch screening instrument used by the Well Child Preventive Health Care Clinics (WCPHCC ) to assess the psychomotor development of children between the age of 1 month and 4.5 years. A minus score of $3 in a single section is considered deviant with an increased risk of psychomotor developmental delay.
section. There are no distinct cutoff points that define developmental delay. Before the study, we defined 3 or more minus scores in any 1 particular section as a deviant section. This is regarded as an abnormal VWS outcome, predicts an increased risk for developmental delay, and is an indication for referral to a pediatrician. The latest available assessment points (78.6% obtained between 2 and 4 years of age) were used. Only 1 child (1%) (95% CI: 0.03%; 5.29%) scored 3 minus scores in 1 section (communication) on the VWS and was therefore labeled as having an increased risk for psychomotor developmental delay (Fig 2). This was a 3-year-old child of immigrant parents. Cultural aspects and a language barrier may have accounted for his delayed communication skills. No child was referred to a pediatrician for further evaluation of possible psychomotor delay at any stage. None of the 5 propranolol-treated twins scored 3 or more minus scores in 1 particular section, in contrast to the untreated twin sibling group in which 1 child scored 3 or more minus scores. In conclusion, our study did not detect evidence of psychomotor developmental delay among infants with IH treated with propranolol. It remains possible that propranolol treatment causes subtle adverse effects, which cannot be traced with tools such as the VWS. Future prospective studies using universal screening tools such as the Parents Evaluation of Developmental Status (PEDS), the Ages and Stages Questionnaire (ASQ),4,5 or more advanced neuropsychologic tests are needed to support our findings.
Andre Moyakine participated in generating, gathering, and interpreting the data for the study. He wrote the majority of the original draft of the paper and approved the final version of this paper. Denise Hermans participated in generating and gathering the data for the study, and in writing the paper. She approved the final version of this paper. Joris Fuijkschot participated in gathering and interpreting the data for the study. He approved the final version of this paper. Catharina van der Vleuten devised the design of the study, participated in generating and gathering the data for the study, and in writing the paper. She approved the final version of this paper and guarantees that all individuals who meet the journal’s authorship criteria are included as authors of this paper. Conflicts of interests: None declared. Correspondence to: Andre Vadimovich Moyakine, MD, PO box 9101, 6500 HB, Nijmegen E-mail: [email protected] REFERENCES 1. Langley A, Pope E. Propranolol and central nervous system function: potential Implications for pediatric patients with infantile hemangiomas. Br J Dermatol. 2014;1:13379. 2. Gesell A. The mental growth of the pre-school child: a psychological outline of normal development from birth to the sixth year, including a system of developmental diagnosis. New York: MacMillan; 1925. 3. Gesell A, Amatruda CS. Developmental diagnosis; normal and abnormal child development. Oxford, England: Hoeber; 1941. 4. Glascoe FP. Screening for developmental and behavioral problems. Ment Retard Dev Disabil Res Rev. 2005;11:173-179. 5. Limbos MM, Joyce DP. Comparison of the ASQ and PEDS in screening for developmental delay in children presenting for primary care. J Dev Behav Pediatr. 2011;32:499-511. http://dx.doi.org/10.1016/j.jaad.2015.04.053
A pilot study to determine vitiligo target size using a computer-based image analysis program To the Editor: Currently no objective outcome measure to determine the size of vitiligo lesions exists. The Vitiligo Area Scoring Index, commonly
J AM ACAD DERMATOL
Letters 343
VOLUME 73, NUMBER 2
Table I. Details of each patient’s demographics including race/ethnicity, clinical phenotype, treatment modalities used, and percentage change in lesion area over time (automated fill factor, also generally termed ‘‘percent repigmentation’’) for individual vitiligo lesions being monitored with serial photography
Patient
Ethnicity
Phenotype
Treatment
1
Hispanic
Acrofacial
None
2
African American
Nonsegmental vitiligo
NBUVB OC
3
Caucasian, Native Nonsegmental American vitiligo
NBUVB
4
Caucasian
Mucosal
5
Caucasian
Nonsegmental vitiligo
Tacrolimus NBUVB NBUVB
6
South Asian
Segmental
NBUVB
7
Greek
Acrofacial
TC
8
Caucasian
Nonsegmental vitiligo
NBUVB TC
9
South Asian
Acrofacial
NBUVB
10
African American
Segmental
NBUVB TC
Repigmentation
Site
Fill factor ( percent change in lesion area over time)
Baseline 1 mo 6 mo 0 2 19 0 1 9 Baseline 1 mo 2 mo 3 mo Chest 0 8 24 63 L elbow 0 2 9 17 R elbow 0 4 11 26 Baseline 1 mo 2 mo 3 mo PF L popliteal fossa 0 5 9 10 Marginal[PF L wrist 0 4 10 16 PF[marginal R popliteal fossa 0 3 5 11 Marginal[PF R antecubital 0 6 16 22 fossa Marginal and PF R forearm 0 11 25 36 Marginal and PF R wrist 0 9 25 28 Baseline 1 mo 2 mo PF Genital 0 2 5 Baseline 3 mo 5 mo 6 mo PF[marginal L hand 0 1 5 14 PF L knee 0 7 1 14 PF[marginal R hand 0 2 3 24 Marginal R foot 0 2 4 5 PF R knee 0 0 3 7 Baseline 1 mo 2 mo 3 mo Marginal L eye 0 3 19 28 Marginal L neck 0 3 4 6 Marginal L cheek 0 6 53 N/A Baseline 1 mo 2 mo PF L forehead 0 2 2 Marginal L hand 0 1 3 PF R forehead 0 3 7 Marginal R hand 0 2 1 Baseline 1 mo 2 mo 4 mo PF Forehead 0 2 3 16 Marginal L temple 0 83 91 N/A Marginal R temple 0 16 11 84 Baseline 1 mo 2 mo PF L lower lip 0 4 7 PF R oral 0 4 8 commissure PF R upper lip 0 2 21 Marginal[PF R cheek 0 8 86 Baseline 4 mo 8 mo 10 mo Marginal[PF R cheek 0 27 52 57
Marginal Marginal PF[marginal
L hand R hand
L, Left; N/A, not available; NBUVB, narrowband ultraviolet B phototherapy; OC, oral corticosteroids; PF, perifollicular; R, right; TC, topical corticosteroids.
used in clinical trials, and the Vitiligo European Task Force score both rely on subjective evaluation of investigator-measured units to determine the degree of disease involvement.
Previous studies using image analysis to evaluate treatment efficacy exist but also often rely on investigator-defined boundaries.1,2 Recognizing the need to create an objective, user-friendly method for
J AM ACAD DERMATOL
344 Letters
AUGUST 2015
Fig 1. A, Baseline image of vitiligo lesion on elbow after image analysis and identification of lesion contours. B, Three-month follow-up image of vitiligo lesion on elbow after image analysis showing automated fill factor of 26%.
monitoring vitiligo, we developed a novel image analysis program piloted on 10 patients treated for vitiligo. After institutional review board approval, patients with a diagnosis of vitiligo were recruited for a baseline and at least 2 follow-up visits. Being an observational pilot study, no standardized intervention was performed. Photographs of skin lesions were taken under same conditions using normal room lighting and a handheld digital camera (SX210 IS, Canon, Melville, NY). Image sequences were analyzed using a novel software program combining standard image-processing techniques. White balancing and contrast enhancement were used to normalize lighting differences. Color correction was then performed by adjusting red, green, and blue color channels separately to recreate color under neutral lighting. An image segmentation scheme that exploits the difference in features such as skin color between the lesion and adjacent skin was used to automatically identify lesion contours in the individual images.3 A scale invariant feature transformebased4 feature matching approach was used to transform and align identified lesions from follow-up images with those in the initial image. The areas of aligned lesions were compared to baseline lesion area to determine percentage change in area over time, termed the ‘‘automated fill factor’’ (ie, percent repigmentation). The automated program was highly sensitive to change, detecting differences in lesion area as low as 1%, and detecting both improvement and worsening of lesions (Table I). Validation was performed by comparison with investigator-traced images using Image J software5 on the first 4 sequential images from 8 randomly selected series of photographs. Differences between the follow-up lesion and baseline lesion area using this manual tracing method
was termed the ‘‘manual fill factor.’’ There was no significant difference between automated and manual methods. Examining aggregate data from the validation series using Wilcoxon rank sum test (N ¼ 24 for 8 lesions and 3 time periods each), the median difference between manual and automated fill factors was 2.3% (P ¼ .7). Individual time points were evaluated separately, again with no significant difference between automated and manual methods (median differences of 6%, 0.8%, and 2.3%, respectively, P [ .05). To determine intrarater variability, the same image series were run through the automated program 3 times. Similarly, investigators manually traced each image series 3 times with Image J analysis.5 Intrarater variability was 0 using the automated program and 3% using the manual program. The automated program was found to be reliable in multiple skin phototypes and able to measure marginal and perifollicular repigmentation (Fig 1). The program also detected improvement with therapeutic intervention. Because the software corrects for minor variations in lighting and perspective, there is no strict requirement for image capture conditions. The distinct advantage of our method is speed and ease of use. However, the sample size used for validation was small. The program proved most useful for patients with limited skin involvement or smaller discrete lesions. Limitations of this method include potential difficulty in assessing larger lesions or lesions over curved surfaces. Further, larger studies are needed to confirm the initial but promising results shown herein. Vaneeta M. Sheth, MD,a Rahul Rithe, PhD,b Amit G. Pandya, MD,c and Anantha Chandrakasan, PhDb
J AM ACAD DERMATOL VOLUME 73, NUMBER 2
Clinical Research Program, Department of Dermatology, Brigham and Women’s Hospital, Boston, Massachusettsa; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridgeb; and Department of Dermatology, University of Texas Southwestern Medical Center, Dallasc Supported by Foxconn Technology Group. Conflicts of interest: None declared. Correspondence to: Vaneeta M. Sheth, MD, Department of Dermatology, Brigham and Women’s Hospital, 221 Longwood Ave, Boston, MA 02115 E-mail: [email protected]
Letters 345
REFERENCES 1. Linthorst Homan MW, Wolkerstorfer A, Sprangers MA, van der Veen JP. Digital image analysis vs clinical assessment to evaluate repigmentation after punch grafting in vitiligo. J Eur Acad Dermatol Venereol. 2013;27:e235-e238. 2. AlGhamdi KM, Kumar A, Ta€ıeb A, Ezzedine K. Assessment methods for the evaluation of vitiligo. J Eur Acad Dermatol Venereol. 2012;26:1463-1471. 3. Li C, Huang R, Ding Z, Gatenby C, Metaxas DN, Gore JC. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process. 2011;20:2007-2016. 4. Lowe D. Distinctive image features from scale-invariant keypoints. Int J Comput Vision. 2004;60:91-110. 5. National Institutes of Health. Image J software. Available from: URL: http://imagej.nih.gov. Accessed April 14, 2014. http://dx.doi.org/10.1016/j.jaad.2015.04.035
