The clinical posters presented by MELA Sciences at the Winter Clinical Dermatology Conference and their respective abstracts are attached. The poster authored by Dr. Darrell Rigel was named "Poster of the Day" on the first day of the conference.

1. BACKGROUND
ATYPICAL MOLE SYNDROME PATIENTS AND MULTISPECTRAL DIGITAL
SKIN LESION ANALYSIS DEVICE (MSDSLA)
Harold S. Rabinovitz, MD, Skin and Cancer Associates Center for Cosmetic Enhancement®, Plantation, FL; Margaret Oliviero, ARNP, MSN, Skin and Cancer Associates
Center for Cosmetic Enhancement®, Plantation, FL; Nikolai Kabelev, MELA Sciences, Inc., Irvington, NY; Dina Gutkowicz-Krusin, PhD, MELA Sciences, Inc., Irvington, NY
DEVICE DESCRIPTION
RESULTS
What:
Pivotal Study Analyses:
Performance:
 The classifier was successfully tested in the largest positive prospective
clinical study of melanoma detection to date, which demonstrated a
sensitivity to melanomas and high grade dysplastic nevi of 98.3% with a
statistically significant higher biopsy specificity than dermatologists (9.9%
versus 3.7% (p = 0.022).1
 Pivotal study1 for MSDSLA showed that mean scores for melanomas and
high grade lesions were higher than for benign lesions.
 Performed with 98% negative predictive value (NPV) and a 7.6:1 biopsy
ratio in Pivotal Study.1
AIMS/METHODS
Exploratory Aims:
 To determine whether the distribution of
MSDSLA scores is different for different
patients with multiple nevi, and whether
such distributions can be utilized to
identify “signature” lesions for a given
patient, and thus identify “ugly duckling”
lesions.
 To determine the feasibility of defining
and using individual thresholds based on
relative morphological disorganization of
lesions on a patient.
Methods:
 10 Patients with 200+ pigmented lesions.
 5 males, 5 females : Age range 31-62.
 9 with personal or family history of MM,
or history of basal/squamous cell
carcinoma.
 For each patient, MSDSLA images of
approximately 100 lesions were acquired
and analyzed .
Figure 1. Patient Examples
Atypical Mole Syndrome Patients Results:
Table 1. Data from the Pivotal Study1, displayed as a histogram, shows
the distribution of scores for melanomas/high grade lesions as
compared to that of low grade dysplastic nevi.
Frequency of occurrence
 Non-invasive multispectral digital skin lesion analysis device, MSDSLA,
intended to aid dermatologists in the detection of melanoma (MelaFind®,
MelaSciences, Inc., Irvington, NY).
 The purpose of multispectral data capture is to improve differentiation of
early melanoma from other pigmented skin lesions.
 Uses light of 10 wavelengths, from blue light (430 nm) to near infrared light
(950 nm) to capture lesion data up to 2.5 mm under the skin.
 Samples the 3-dimensional morphology of a pigmented skin lesion and
surrounding skin by analyzing multi-spectral data directly, or by enhancing
the morphological patterns characteristic of melanoma.
 Result provides a numeric value, where scores of zero and above are
considered “High Disorganization” and scores below zero are considered
“Low Disorganization.”
RESULTS
Table 4. Mean scores with corresponding individual threshold scores for
all patients in study.
Patient ID
0.800
No.
Cases
Mean
Score
1
2
3
4
5
6
7
8
9
10
1.000
97
102
103
102
104
101
95
99
102
103
0.56
1.29
1.61
0.22
0.78
0.45
0.92
0.75
1.54
0.63
Melanoma +
High Grade
0.600
0.400
Low Grade
Dysplastic
Nevi
0.200
0.000
-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
MSDSLA Classifier Score
Table 2. Data from the Pivotal Study1. Shows a higher probability of a
lesion being a melanoma or high grade lesion as the classifier
score increases.
Probability a lesion with a score in
Classifier Score Bracket
this bracket is MM/HG
< -2.0
5%
-2.0 to -1.0
3%
-1.0 to 0.0
1%
0.0 to 1.0
3%
1.0 to 2.0
7%
2.0 to 3.0
10%
3.0 to 4.0
23%
4.0 to 5.0
28%
5.0 to 6.0
26%
> 6.0
42%
Table 3. Displays the classifier scores for 3 patients in the Pivotal Study1
who contributed 6 or more lesions for biopsy. The numbers
denoted in red represent histologically confirmed melanomas or
high-grade lesions. All other lesions are non-melanoma/high
grade lesions.
MSDSLA Scores for 3 patients
Case No.
Patient 1
Patient 2
Patient 3
1
4.83
5.26
6.11
2
2.52
4.42
2.74
3
2.42
3.76
2.19
4
1.85
2.95
1.54
5
1.81
2.72
1.30
6
1.81
2.43
0.11
7
1.71
2.40
8
1.31
2.26
9
1.17
1.99
10
1.15
1.63
11
1.06
0.68, 0.08, -0.06,
12 - 16
-0.21, -0.80
No. Cases
above
Threshold =
0
70
91
95
60
72
67
79
75
89
68
Individual
Threshold
No.
Outliers
3.06
3.86
4.12
2.97
4.19
3.43
4.15
3.73
4.63
4.06
2
3
1
3
0
4
1
3
0
2
Figure 3. Score distributions for patients 3 and 4. Dotted lines indicate
individual thresholds
Table 6. Distribution of MSDSLA Classifier Scores
0.6
0.5
Patient 3
Age
Lesion #
2
F
31
13
2
2
F
F
31
31
22
98
6
7
M
F
31
54
34
104
Lesion
Location
Upper
back
Upper
back
Leg
Lower
Back
Forearm
MF
Score
Individual
Threshold
Local
Path
Central
Path
2.59
3.86
NDHG
NDLG
2.45
4.73
3.86
3.86
NDLG
NDLG
NDLG
NDLG
0.33
5.95
3.43
4.15
NDLG
NDHG
NDLG
MMIS
Only 5 lesions in the study were biopsied, results shown above. Red font
indicates scores greater than the individual threshold as displayed in table
4 (NDHG = high grade dysplastic nevus; NDLG = low grade dysplastic
nevus; MMIS = melanoma in situ).
Figures 1 & 2. Clinical and dermoscopic images for the two outliers from
Table 5
Figure 1. Patient ID 2
0.3
0.2
0
-4
-2
0
2
4
Figure 2. Patient ID 7
MSDSLA score distributions are Gaussian (KS test).
Score distributions among some patients are significantly different resulting in
individual thresholds to identify outliers (“ugly ducklings”). Patient 4 had the
lowest mean score of 0.22 and Patient 3 had the highest mean score at 1.61
CONCLUSIONS
 Distributions of MSDSLA scores may be very different on different patients
with many nevi.
 MSDSLA scores could be used to identify “typical” lesions on a patient and to
identify “ugly duckling” lesions (outliers).
 Lesions with scores that are outliers should be considered for biopsy to rule
out melanoma.
 The analysis of MSDSLA scores may allow quantitative identification of
outliers that should be considered for biopsy.
REFERENCES
MSDSLA Score: 4.73
MSDSLA Score: 5.95
Local/Central Pathology: Low
Grade Dysplastic Nevus
Local Pathology: High Grade
Dysplastic Nevus
Central Pathology: Melanoma in situ
6
MSDSLA Classifier Score
Patient 4
Table 5. Lesions with biopsy.
Sex
0.4
0.1
The mean scores range from 0.22 to 1.61, demonstrating that the qM
(quantitative morphological value) differentiates between lesions on
different patients. The number of cases above threshold 0 for each
patient ranges from 60-95. However, utilizing individual thresholds, the
number of lesions identified as highly disorganized for a given patient is
greatly reduced.
Patient
ID
Distribution
Benign nevi on a given patient have similar morphological
characteristics to each other2 (“signature lesions”). Grob
and Bonerandi proposed that lesions significantly different
from common nevi on a patient (“ugly ducklings”) be used
for melanoma screening3. Based on these findings, it was
questioned if MSDSLA can be used to identify these
“signature” lesions in patients with atypical mole
syndrome in order to identify “ugly ducklings” for biopsy.
1. Monheit G, Cognetta AB, Ferris L, et al. The performance of MelaFind.
Arch Dermatol. 2011;147:188-94.
2. Goodson AG, Grossman D. Strategies for early melanoma detection:
approaches to the patient with nevi. JAAD 2009;60:719-38.
3. Grob JJ, Bonerandi JJ. The “ugly duckling” sign: identification of the
common characteristics of nevi in an individual as a basis for melanoma
screening. Arch Dermatol. 1998;134:103-4.

2. Atypical Mole Syndrome Patients and
Multispectral Digital Skin Lesion Analysis Device (MSDSLA)
Harold Rabinovitz, MD; Margaret Oliviero, ARNP;
Nikolai Kabelev; Dina Gutkowicz-Krusin, PhD
Introduction: A multispectral digital skin lesion analysis device (MSDSLA) (MelaFind®: MELA Sciences, Inc., Irvington, NY), uses light imaging
technology and analysis software to provide a numeric value that characterizes the 3 dimensional morphological disorganization of clinically
atypical pigmented skin lesions. Classifier scores of zero and above indicate “High Disorganization” and classifier scores below zero indicate
“Low Disorganization.”
Objective: Since benign nevi on a given patient share similar morphological characteristics, it was investigated whether MSDSLA scores can be
used to identify “typical” lesions on individual patients, and conversely be used to identify “ugly ducklings”. A pivotal study for the MSDSLA device
showed that classifier scores for melanomas and high-grade lesions tended to be higher than scores for benign lesions on patients with multiple
nevi.
Methods: Patients (N=10) with at least 200 nevi were selected by an examining dermatologist who was also a pigmented skin lesion expert in a
clinical setting; approximately 100 lesions on each patient were evaluated with MSDSLA. Five patients were male, and five patients were female;
nine had a personal or family history of melanoma or a personal history of non-melanoma skin cancer. MSDSLA score distributions for all
patients and for each individual patient were analyzed using the Kolmogorov-Smirnov test to determine normal distribution of scores. Tentative
individual thresholds were then proposed so that the probability of finding scores greater than the threshold value for each individual was less
than 1% for a normal distribution of scores.
Results: MSDSLA score distribution was Gaussian; score distributions among some patients were significantly different. Individual thresholds
for each patient were calculated using mean score plus 2.33 standard deviations; individual thresholds ranged from 2.8 for one patient to 4.5 for
another patient. Seventy-six percent of lesions were above the threshold (“0”) used for the general population as opposed to only 2.1% of lesions
above the proposed individual thresholds as calculated for each patient.
Conclusions: Distribution of MSDSLA scores may be very different on different patients with many nevi. MSDSLA scores may be used to
identify “typical” lesions on a patient and to also identify “ugly duckling” lesions (outliers). Individual thresholds for these types of patients may be
determined through baseline imaging with MSDSLA (10-15 lesions). Lesions with scores that are outliers should be considered for biopsy to rule
out melanoma. The analysis of MSDSLA scores may allow quantitative identification of outlier lesions that should be considered for biopsy.
Monheit, G et al. The Performance of MelaFind: A Prospective Multicenter Study. Arch Dermatol Oct 2010: E1-E7

3. Case Study: Use of a Multispectral Digital Skin Lesion Analysis Device (MSDSLA) in Pediatrics and Young Adults
Dr. med. Marcella Kollmann-Hemmerich, Munich, Germany
BACKGROUND
Introduction
 Two reader studies were conducted presenting high
resolution images and case histories for 130 pigmented
skin lesions to dermatologists in the U.S. (n = 110)5 and
in Germany5 (n = 101). After reviewing the case,
dermatologists were asked to provide their lesion
management decisions.
C A S E S T U D I E S – Y O U N G A D U LT S
READER STUDY REVIEW
 Differentiation of early melanomas from benign lesions in young adults
(16 – 25) can be difficult clinically.
 Incidence of melanoma in children and adolescents has increased at an
annual rate of 2.0% per year from 1973 to 2009.1
 Biopsy ratio of up to 80 to 1 have been observed in younger patients. 2
 Melanoma is the most common cancer in young adults aged 20 to 30. 3
 MSDSLA can be used to help dermatologists decide which lesions may
be suspicious for melanoma by providing information about the structure
of a lesion from under the skin (MelaFind; MELA Sciences, Inc.).
Pivotal Study
 The pivotal study for MSDSLA (MelaFind®: Irvington, NY) established
that it was a safe and effective device by demonstrating high sensitivity to
melanomas and high grade dysplastic nevi (98.3%) and better specificity
than study clinicians (9.9% vs. 3.7%, p = 0.022).4
 In the pivotal study, 183 lesions from young adult patients aged 16 to 25
were enrolled; of these, 13 were melanomas or high grade lesions, 11 of
which were correctly considered MSDSLA “High Disorganization.”
 Of the 11 melanomas enrolled from young adults, all were invasive,
except one melanoma in situ, with median Breslow thickness of 0.50
mm.
 Sensitivity of both U.S. and German dermatologists
were 72% and 70%, respectively.
 5 melanomas from young adult patients, all invasive,
were randomly selected for review by these 211
dermatologists (Table 1).
CASE STUDY # 1:
MSDSLA
Dermoscopic
Image
 17 year old female; regular skin visits
CASE STUDY # 2:
 24 year old male
 Lesion appeared within 4 months of
excision and grew within that time
 > 50 nevi (none dysplastic)
 No family history of melanoma
 No risk factors for melanoma
 Fitzpatrick Skin Type II-III
Lesion
Location
 Lesion clinically unsuspicious over 3
yrs
 Clinical diagnosis: Spitz/Reed Nevus
 Pathology: melanoma in situ
 MSDSLA score: 3.5
 Pathology: melanoma in situ
MSDSLA Multispectral Images:
MSDSLA Multispectral Images:
430 nm
Clinical
Dermoscopic
Patient
Age
500 nm
510 nm
600 nm
430 nm
460 nm
500 nm
510 nm
600 nm
660 nm
Breslow
(mm)
460 nm
700 nm
780 nm
880 nm
950 nm
660 nm
700 nm
780 nm
880 nm
950 nm
% of
dermatologists
electing NOT to
biopsy
Table 1. Characteristics of Young Adults (pivotal study)
Histology:
Histology:
% (N = 136)
Female
Male
Asian or Pacific Highlander
White
RACE
Other
Mean
Median
AGE
Standard Deviation
I
II
FITZPATRICK SKIN TYPE
III
IV
HISTORY OF DYSPLASTIC NEVI*
66.2%
33.8%
3.7%
95.6%
0.7%
21
21
2.8
5.1%
51.5%
36.0%
7.4%
29.0%
HISTORY OF MELANOMA
FAMILY HISTORY OF MELANOMA**
20.2%
BLONDE/RED HAIR
0.32
24
1.9%
0.17
24
48.6%
0.60
18
57.3%
7.4%
40.4%
SEX
BLUE/GREEN EYES
NUMBER OF NEVI
USE OF TANNING BED
CONCLUSIONS
*five values were “unknown”; ** twelve values were “unknown”
37.5%
36.8%
13.2%
12.5%
40.4%
23.5%
14.7%
21.3%
REFERENCES
1 Wong
0.70
17
46.0%
1. Melanomas on young adult patients can be
difficult to detect.
63.2%
1-10
11-30
31-50
> 50
Never
1-10 times
11-24 times
> 24 times
Lesion
Location
 MSDSLA score: 3.3
Table 2. Melanomas from young adults on reader
studies
 A total of 136 young adults were enrolled on the pivotal study; none had a
history of pigmented basal or squamous cell carcinoma. Table 1 shows
characteristics of these young adults.
Baseline Characteristics
MSDSLA
Dermoscopic
Image
0.28
0.15
25
25
78.1%
76.2%
2. MSDSLA can be helpful in assisting in the
detection of early melanomas within young
adults, a population where melanoma incidence is
on the rise.
JR. Pediatrics. 2013;doi:10.1542/peds.2012-2520
2 English
et al. Med J Aust. 2004
P, Borden EC. Malignant Melanoma: Treatments Emerging, but early detection is
still key. Cleveland Clinic Journal of Medicine July 2002; Vo. 69 # 7: 529-545.
3 Masci
4
Monheit G, Cognetta AB, Ferris L, et al. The performance of MelaFind: a prospective
multicenter study. Arch Dermatol. 2011;147:188-94.
5 Chen
SC, Wells R and Adrian J. (2011, February). Performance of an adjuvant melanoma
detection tool compared to physicians. Poster presented at the American Academy of
Dermatology, New Orleans, LA.
6
Hauschild, Axel. Protocol 20101: To Excise or Not?: Comparing Clinical Management
Decisions for Melanoma Between Dermatologists with and without the Aid of MelaFind.
University of Kiel, Kiel, Germany. Unpublished data; August 2012.
Funded in part by a grant from MELA Sciences, Inc.

4. Analysis of Pigmented Skin Lesions in Young Adults with
A Multispectral Digital Skin Lesion Analysis Device (MSDSLA)
Marcella Kollmann, MD
Background: The incidence of melanoma is increasing worldwide, particularly in young adults, a group in which melanoma detection can be
problematic. MelaFind®, a multispectral digital skin lesion analysis device (MSDSLA), is a non-invasive and objective instrument that dermatologists
can use to help determine whether a pigmented skin lesion is suspicious and should be biopsied to rule out melanoma (MelaFind ®, MELA Sciences,
Inc., Irvington, NY). A previous clinical trial had established the safety and effectiveness of this device using sensitivity and specificity as metrics. This
device is presently in use at various dermatologist practices in the United States and in Germany.
Objective: To investigate the utility of a multispectral digital skin lesion analysis device in the diagnosis of suspicious pigmented skin lesions in young
adults.
Methods: Data reviewed for these analyses were as follows: 1) real-world case study data from a 17 year-old female and a 24 year-old male
evaluated with MSDSLA in clinical practice, 2) MSDSLA and clinical data on 183 lesions evaluated from young adults (ages 16 to 25) enrolled onto the
pivotal study for the device1, and 3) six melanomas from young adults who participated in the pivotal study which were subsequently reviewed in a
reader study format by 211 U.S. and German dermatologists.
Results: In clinical practice, the MSDSLA device assisted in the detection of a melanoma in a male patient aged 17 and one in a female patient aged
24. In the pivotal study1, 183 lesions from young adult patients were examined; of these, 13 were melanomas or high-grade lesions, 11 of which were
correctly considered by the MSDSLA as possessing “High Disorganization”. Six of the 13 melanomas from young adult patients in the pivotal study1
were also reviewed in two separate, but similar, reader studies consisting of 110 U.S. dermatologists and 101 German dermatologists who provided
their lesion management decisions for atypical lesions based upon review of high-resolution clinical and dermoscopic images as well as detailed patient
and lesion information. Average false negative rates of the combined 211 dermatologists for these melanomas were as follows (median Breslow in
parenthesis): 48.6% (0.17 mm), 57.3% (0.60 mm), 1.9% (0.32 mm), 76.2% (0.15 mm), 46.0% (0.70 mm), and 78.1% (0.28 mm).
Conclusions: Melanomas on young adult patients can be difficult to detect. MSDSLA may be helpful in the detection of early melanomas within
young adults, a population where the incidence of melanoma is on the rise.
Monheit, G et al. The Performance of MelaFind: A Prospective Multicenter Study. Arch Dermatol Oct 2010: E1-E7

5. Assessing the Predictive Probability of Melanoma and Other High Risk Pigmented Lesions Using Data
Provided by a Multispectral Digital Skin Lesion Analysis Device
Jane Yoo, MD MPP*; Natalie Tucker, BS**; Darrell S. Rigel, MD MS***
*Dept. of Dermatology, Albert Einstein School of Medicine, Bronx, NY; **MELA Sciences, Irvington, NY; ***Dept. of Dermatology, NYU School of Medicine, NYC, NY
Introduction
Risk prediction models are often used as
research tools to help identify individuals at high
risk of specific cancers in the general population.
Developing statistical models that evaluate the
probability of developing cancer over a defined
period of time allows for earlier or more frequent
screening and counseling as well as earlier
intervention and treatment. While many different
diagnostic tools for melanoma have emerged in
the past decade, very few have quantified their
predictive capacity for melanoma or other high
risk pigmented lesions.
MSDSLA Classifier Score Description
A Mulltispectral Digital Skin Lesion Analysis
device (MSDSLA), (MelaFind®, MelaSciences,
Inc., Irvington, NY) combines multispectral data
acquisition and novel feature generation with
automatic quantitative analysis.
The automatic quantitative analysis utilizes a
lesion classifier score derived from 75 features
to evaluate the degree of 3-dimensional
morphological disorganization of pigmented skin
lesions.
MSDSLA was successfully tested in the largest
positive prospective clinical study of melanoma
detection to date, which demonstrated a
sensitivity to melanomas and high grade
dysplastic nevi of 98.3% with a statistically
significant higher biopsy specificity than
dermatologists (9.9% versus 3.7%, p = 0.022)1.
In this study, the classifier scores ranged from 5.25 to +9.00. Of note, scores below zero are
considered to be indicative of “low
disorganization” and scores of zero and above
are considered indicative of “high
disorganization.”
Methods
Data from 1632 pigmented lesions analyzed
by MSDSLA were used to perform a logistic
regression analysis using Stata v12 (College
Station, Tx).1
Final pathological diagnoses were assigned to
four distinct categories: 1) High grade
dysplastic nevus (HGDN), 2) Atypical
melanocytic hyperplasia (AMH), 3) Malignant
melanoma (MM) or, 4) Other
The MSDSLA device assigns classifier scores
to pigmented lesions, which were considered
as dependent variables. Using the MSDSLA
derived classifier score (a numerical value
based on analytical values from a lesion’s
level of structural disorder), we repeat
logistical regression models to determine the
probability distribution for Malignant
Melanoma and for lesions that might be
considered suitable for biopsy (Melanoma or
Atypical Melanocytic Hyperplasia or High
Grade Dysplastic Nevus).
Results
Case Examples
Results
Melanoma model:
Melanoma/AMH/HGDN model:
P = probability of melanoma
= e (-3.8+0.53x) / 1+e (-3.8+0.53x)
P = probability of MM/AMH/HGDN
= e (-3.3+0.49x) / 1+e (-3.3+0.49x)
or p/1-p=odds of melanoma
or p / 1-p= odds of MM/AMH/HGDN
For every one unit increase in the MSDSLA
classifier score, the odds of favoring the
presence of melanoma increased by 1.7.
Melanoma Prediction Model
For every one unit increase in the MSDSLA
classifier score, the odds of favoring the
presence of MM/AMH/HGDN increased by
1.6.
Melanoma/AMH/HGDN Prediction
Model
Classifier
Score
Probability
of MM
Std. Error
95% Confidence
Interval
Classifier
Score
Probability
MM/AMH/HGDN
Std. Error
95% Confidence
Interval
-10
0.01%
0.01%
-0.00%, 0.03%
-10
0.03%
0.02%
-0.00%, 0.06%
-9
0.02%
0.01%
-0.00%, 0.04%
-9
0.05%
0.03%
-0.00%, 0.10%
-8
0.03%
0.02%
-0.00%, 0.07%
-8
0.07%
0.04%
-0.00%, 0.15%
-7
0.05%
0.03%
-0.00%, 0.11%
-7
0.12%
0.06%
0.01%, 0.23%
-6
0.09%
0.04%
0.00%, 0.18%
-6
0.20%
0.08%
0.03%, 0.36%
-5
0.15%
0.07%
0.02%, 0.29%
-5
0.32%
0.12%
0.08%, 0.56%
-4
0.26%
0.10%
0.06%, 0.46%
-4
0.52%
0.17%
0.17%, 0.86%
-3
0.45%
0.15%
0.15%, 0.74%
-3
0.84%
0.24%
0.36%, 1.32%
-2
0.76%
0.22%
0.33%, 1.19%
-2
1.35%
0.33%
0.70%, 2.01%
-1
1.29%
0.31%
0.69%, 1.89%
-1
2.18%
0.44%
1.33%, 3.04%
0
2.17%
0.41%
1.37%, 2.98%
0
3.50%
0.55%
1
3.65%
0.53%
2.62%, 4.69%
1
5.57%
0.65%
6.08%
0.65%
4.79%, 7.36%
2
8.75%
0.76%
7.27%, 10.23%
where p is the calculated probability of melanoma
and x1x 2 xi are explanatory variables. The
model logit(p) = a + bx is equivalent to p =
probability of melanoma (or of lesion of interest
considered for biopsy) = e(a+bx) / 1+ e(a+bx)
3
9.94%
0.87%
8.23%, 11.66%
3
13.49%
0.98%
11.57%, 15.41%
4
15.85%
1.43%
13.04%, 18.66%
4
20.22%
1.58%
17.13%, 23.32%
5
24.32%
2.56%
19.31%, 29.34%
5
29.19%
2.67%
23.96%, 34.42%
6
35.42%
4.14%
27.30%, 43.53%
6
40.13%
4.04%
32.21%, 48.04%
7
48.34%
5.69%
37.18%, 59.49%
7
52.14%
5.26%
41.84%, 62.45%
8
61.49%
6.56%
48.62%, 74.35%
8
63.92%
5.88%
52.40%, 75.44%
9
73.15%
6.44%
60.53%, 85.76%
9
74.23%
5.74%
62.97%, 85.48%
10
82.29%
5.52%
71.47%, 93.12%
10
82.40%
5.02%
72.57%, 92.24%
Clinical image
Dermoscopic
image
Case Example # 2:
Clinical diagnosis before MSDSLA:
Not melanoma (Biopsied because
of patient concern)
Histological diagnosis: Melanoma
in-situ
Classifier Score: 4.21
Probability of melanoma: 17.4%
Probability of MM/AMH/HGDN:
21.91%
Clinical image
4.29%, 6.85%
2
Clinical diagnosis before MSDSLA:
Likely melanoma (67-99%)
Histological diagnosis: Invasive
melanoma
Breslow thickness: 0.36 mm
Classifier Score: 8.15
Probability of melanoma: 63.33%
Probability of MM/AMH/HGDN:
65.55%
2.42%, 4.58%
logit(p) = a + b1 x1+ b 2 x 2 + ….+ bixi
Case Example #1:
Calculation
The logistic regression model used in this study:
1. Monheit, G et al. The Performance of MelaFind: A Prospective Multicenter Study. Arch Dermatol 2011 Feb; 147 (2):188-94.
Dermoscopic
image
Case Example #3:
Clinical Diagnosis before MSDSLA:
Likely melanoma (67-99%)
Histological diagnosis: Low Grade
Dysplastic Nevus
Classifier Score: -1.56
Probability of melanoma: 1%
Probability of MM/AMH/HGDN: 1.67%
Clinical image
Dermoscopic
image
Conclusions
By performing a univariate logistic regression
analysis, we were able to calculate the predictive
probability of melanoma or a high risk pigmented
lesion for consideration for biopsy based on data
obtained from a multispectral digital skin lesion
analysis device. This is the first study, to our
knowledge, that has evaluated a dermatological
technological instrument for its potential
quantitative predictive capacity for presence of
melanoma and other high risk pigmented lesions.

6. Assessing the Predictive Probability of Melanoma and Other High Risk Pigmented Lesions Using Data Provided by a Multispectral Digital
Skin Lesion Analysis Device
Jane Yoo, MD MPP*, Natalie Tucker**, Darrell S. Rigel, MD MS***
* Dept of Dermatology, Albert Einstein School of Medicine, Bronx, NY; ** MelaSciences, Irvington, NY; *** Clinical Professor of Dermatology, NYU School of Medicine, NYC, NY
Introduction: Risk prediction models are often used as research tools to help identify individuals at high risk of specific cancers in the general population. Developing statistical models that
evaluate the probability of developing cancer over a defined period of time allows for earlier or more frequent screening and counseling as well as earlier intervention and treatment. While
many different diagnostic tools for melanoma have emerged in the past decade, very few have quantified their predictive capacity for melanoma or other high risk pigmented lesions.
Methods: Data from 1,632 pigmented lesions analyzed by a Multispectral Digital Skin Lesion Analysis device (MSDSLA) (MelaFind®, MelaSciences, Inc., Irvington, NY) were used to
perform a logistical regression analysis. The MSDSLA device assigns classifier scores to pigmented lesions. Final pathological diagnoses were assigned to four distinct categories: high
grade dysplastic nevus, atypical melanocytic hyperplasia, malignant melanoma or other and used as the dependent variable. Using the MSDSLA derived classifier score (a numerical value
based on analytical values from lesion’s level of structural disorder) we derived logistical regression models to determine the probability distribution for Malignant Melanoma and for lesions
that might be considered suitable for biopsy (Melanoma or Atypical Melanocytic Hyperplasia or High Grade Dysplastic Nevus).
The Logistic regression model used was:
logit(p) =a + b1 x1+ b 2 x 2 + ….+ bixi
where p is the calculated probability of melanoma and x1x 2 xi are explanatory variables. The model logit(p)=a+bx is equivalent to p=probability of melanoma (or of lesion considered for
biopsy) = e^(a+bx) / 1+e^(a+bx)
Results:
For the Melanoma model:
p=probability of melanoma = e (-3.1+0.49x) / 1+e (-3.1+0.49x)
or p / 1-p= odds of melanoma
For every one increase in unit in the MSDSLA classifier score, the odds of favoring the presence of melanoma increased by 1.3.
For the Melanoma/AMH/HGDN model:
p=probability of MM/AMH/HGDN = e (-3.8+0.53x) / 1+e (-3.8+0.53x)
or p/1-p=odds of MM/AMH/HGDN
For every one unit increase the MSDSLA classifier score, the odds of favoring the presence of MM/AMH/HGDN increased by 1.7.
Conclusion: By performing a multifactorial logistical regression analysis, we were able to calculate the predictive probability of a pigmented lesion for melanoma or for consideration for
biopsy based on data obtained from a multispectral digital skin lesion analysis skin lesion analysis device. This is the first study, to our knowledge, that has evaluated a dermatological
technological instrument for its potential quantitative predictive capacity for presence of melanoma and other high risk pigmented lesions.
Monheit, G et al. The Performance of MelaFind: A Prospective Multicenter Study. Arch Dermatol Oct 2010: E1-E7

7. Physical Properties, Novel Features and Clinical Validation of a Multispectral Digital Skin Lesion Analysis Device for Melanoma Detection
Darrell S. Rigel, MD, MS, NYU School of Medicine, New York, NY; Laura Ferris, MD, PhD, University of Pittsburgh, Pittsburgh, PA; Arthur Sober, MD, Harvard University, Boston, MA; Clay J. Cockerell, MD, University of Texas Southwestern, Dallas, TX
Multispectral Images and Analysis
Description
• MSDSLA is a non-invasive and objective computer vision system intended to aid
dermatologists in the detection of melanoma (MelaFind®, Mela Sciences, Inc.,
Irvington, NY).
• MSDSLA acquires multi-spectral data in 10 different spectral bands from blue light
(430 nm) to near-infrared (950 nm).1
• MSDSLA uses information not visible to the human eye to characterize the
morphological disorganization of pigmented skin lesions that are clinically
ambiguous.
Classifier Score By Lesion Type
E X A M P L ES – M S D S L A D ATA
LESION TYPE
(N = 1632)
Histologically Benign*
Non-melanoma skin cancers
High grade lesions
Melanomas
Path ology: In vasive M elan om a
*excludes
C lin ic a l O ve r vie w
C lin ic a l C lo s e - u p
Contribution
• The purpose of multispectral data capture is to improve differentiation of
cutaneous melanoma from other pigmented skin lesions.
• Early melanomas may mimic benign look-alikes and present a challenge to
clinicians.
• MSDSLA provides information about the entire three dimensional structure of the
lesion up to 2.5 mm in depth.
• MSDSLA utilizes 20-micron resolution allowing it to discern clusters of 3
melanocytes.
Classifier Score Description
• MSDSLA combines multispectral data acquisition and novel feature generation
with automatic quantitative analysis.
• The lesion classifier uses 75 features (some with correlations) to evaluate the
degree of 3-dimensional morphological disorganization of pigmented skin
lesions.
• This classifier was successfully tested in the largest positive prospective clinical
study of melanoma detection to-date, which demonstrated a sensitivity to
melanomas and high grade dysplastic nevi of 98.3% with a statistically
significant higher biopsy specificity than dermatologists (9.9% versus 3.7%, p =
0.022).2
• In this study, the classifier scores ranged from -5.25 to +9.00; scores below zero
were considered to be “low disorganization” and scores of zero and above were
considered “high disorganization.”
1Gutkowicz-Krusin
D, Elbaum M, Jacobs A, Keem S, Kopf AW, Kamino H, Wang S, Rubin P, Rabinovitz H, Oliviero M.
Precision of automatic measurements of pigmented skin lesion parameters with a MelaFind(TM) multispectral digital
dermoscope Melanoma Res. 2000 Dec;10(6):563-70.
2Monheit, G et al. The Performance of MelaFind: A Prospective Multicenter Study. Arch Dermatol 2011 Feb; 147 (2):
188-94
MEAN CLASSIFIER SCORE MEDIAN CLASSIFIER SCORE
1.74
2.64
2.68
3.47
1.61
2.56
2.51
3.49
melanomas, high grade lesions and non-melanoma skin cancers
Table 1. The mean and median scores by lesion type, as observed on the pivotal study. Benign lesions had a mean
score of 1.74, non-melanoma skin cancers 2.64, high grade lesions 2.68 and melanomas 3.47.
D e r m o s c o p ic
Classifier Score By Clinical Characteristics
Path ology: Low Gr ad e Dysp lastic Nevu s
Function
• MSDSLA samples the 3-dimensional morphology of a pigmented skin lesion and
surrounding skin by analyzing multi-spectral data directly or by enhancing the
morphological patterns characteristic of melanoma.
• Lesions are dark at short wavelengths due to strong absorption by superficial
melanin.
• With increasing wavelengths, images of a benign nevus tend to become uniformly
brighter while images of a melanoma tend to show more irregular morphology.
N
1424
33
48
127
NUMBER CLINICAL/HISTORICAL
CHARACTERISTICS
C lin ic a l C lo s e - u p
MEAN CLASSIFIER
SCORE
MEDIAN CLASSIFIER
SCORE
0
C lin ic a l O ve r vie w
N
11
0.84
0.25
1
86
0.96
1.07
2
194
1.40
1.44
3
408
1.50
1.44
4
421
1.94
1.72
5
260
2.36
2.32
6
156
2.67
2.40
7
80
3.00
2.44
8
16
4.03
4.13
D e r m o s c o p ic
MSDSLA visual representations of this invasive melanoma and low grade dysplastic
nevus are displayed below
MULTISPECTRAL ASYMMETRY
TEXTURE
STRUCTURE
BAND WAVELENGTH DEPTH
MELANOMA
Band 0
NEVUS MELANOMA NEVUSMELANOMA NEVUS MELANOMA
NEVUS
430 nm
0.4mm
(blue/violet)
Band 1
460 nm
(blue)
0.7mm
Band 2
500 nm
(green)
0.8mm
Band 3
510 nm
(green)
Table 2. For all lesions enrolled into the study, clinical or historical characteristics observed by the examining
dermatologists were recorded. The more clinical or historical characteristics present, the higher the mean and
median raw classifier scores. Characteristics include Asymmetry, Border Irregularity, Color Variegation, Diameter
greater than 6 mm, Evolution, Patient’s Concern, Regression and/or Ugly Duckling.
0.9mm
Performance by Varying Thresholds
Band 6
Band 7
600 nm
(red)
700 nm
(red)
1.9mm
780 nm
2.0mm
(near infrared)
Band 8
880 nm
2.2mm
(near infrared)
Band 9
950 nm 2.5mm
(near infrared)
 MelaFind discerns 5000 features, 75 of which are taken together to generate information about the
morphological disorganization of a lesion.
 Multispectral data is transformed to enhance various features for analysis, such as asymmetry,
texture and structure (displayed above)
 Benign lesion is more uniform, structured and robust in appearance for selected features displayed,
from the blue to near infrared bands
≥ -3
100.0% (97.9-100%)
0.8% (0.4-1.4%)
≥ -2
99.4% (96.9-100%)
1.3% (0.8-2.0%)
98.9% (95.9-99.9%)
3.6% (2.7-4.7%)
≥0
98.3% (95.1-99.6%)
10.8% (9.2-12.5%)
≥1
1.7mm
SPECIFICITY (95% CI)
93.1% (88.3-96.4%)
29.8% (27.4-32.2%)
≥2
75.4% (78.4-81.6%)
60.0% (57.4-62.5%)
≥3
54.3% (46.6-61.8%)
81.8% (79.7-83.8%)
≥4
Band 5
600 nm
1.3mm
(yellow-green)
SENSITIVITY (95% CI)
≥ -1
Band 4
THRESHOLD
30.9% (24.1-38.3%)
91.4% (89.9-92.8%)
≥5
15.4% (10.4-21.7%)
96.1% (95.0-97.0%)
≥6
7.4% (4.0-12.4%)
98.8% (98.2-99.4%)
Table 3. The sensitivity and specificity at varying thresholds and 95% confidence intervals. As the threshold moves
toward a higher classifier score, the sensitivity decreases and specificity increases. Conversely, as a negative
classifier score threshold is set, the sensitivity increases and the specificity decreases.
CONCLUSIONS:
In this study, the classifier scores ranged from -5.25 to +9.00; scores below zero were
considered to be “low disorganization” and scores of zero and above were considered
“high disorganization.” The average classifier score of melanomas, high grade lesions,
and non-melanoma/high grade lesions were 3.5, 2.7, 2.6, and 1.6, respectively,
providing further clinical validation of the novel MSDSLA features.

8. Physical Properties, Novel Features and Clinical Validation of a
Multispectral Digital Skin Lesion Analysis Device for Melanoma Detection
Darrell S. Rigel, MD, MS; Laura Ferris, MD, PhD;
Arthur Sober, MD; Clay Cockerell, MD
Background: MSDSLA is a non-invasive and objective computer vision system intended to aid dermatologists in the detection of melanoma (MelaFind®, MelaSciences,
Inc., Irvington, NY). Early melanomas may mimic benign look-alikes and therefore present a challenge to clinicians. By acquiring multi-spectral data in 10 different
spectral bands from blue light (430 nm) to near infrared (950 nm), MSDSLA provides information about a lesion’s morphology up to 2.5 mm into the skin. MSDSLA uses
this information, which is not interpretable to the human eye to characterize the morphological disorganization of pigmented skin lesions that are clinically ambiguous.
Objectives: To retrospectively evaluate an automated quantitative analysis (“classifier score”) for pigmented lesions computed by a multi-spectral digital skin lesion
analysis (MSDSLA) device relative to clinical characteristics and histopathology; this data was accrued in the largest positive prospective clinical study of melanoma
detection to-date.
Methods: MSDSLA samples the 3-dimensional morphology of a pigmented skin lesion and surrounding skin by analyzing information collected from 10 wavelengths of
light or by enhancing morphological patterns characteristic of melanoma (examples are asymmetry, texture and structure). Pigmented skin lesions are darker at shorter
wavelengths due to strong absorption by superficial melanin. MSDSLA provides images of the lesion at a depth of up to 2.5 mm. By using an algorithm that calculates
75 feature parameters, MSDSLA provides a numeric value characterizing the 3D morphological disorganization of pigmented skin lesions known as a “classifier score.”
Scores of “0" and above are considered “high disorganization” and scores below “0” are considered “low disorganization”, in reference to the 3D morphological
disorganization of the lesion.
Results: Average classifier scores were higher for melanomas as compared with benign lesions (3.5 versus 1.7). Additionally, the average classifier scores tended to
increase as the number of clinical or historical features of melanoma present in the lesion increased. The range of MSDSLA classifier scores observed in the pivotal
study1 for all lesion types was between -5.2 to 9.0. The average classifier scores of melanomas, high-grade lesions, non-melanoma skin cancers and benign lesions
were 3.5, 2.7, 2.6 and 1.7, respectively, providing further clinical validation of the MSDSLA.
Conclusions: Data from the pivotal study shows that quantitative information about a lesion’s morphological disorganization (“classifier scores”), as computed by
MSDSLA, correlates with the level of both histological and clinical irregularity. This information may be useful to dermatologists when deciding which clinically
ambiguous lesions may need to be biopsied to rule out melanoma.
Monheit, G et al. The Performance of MelaFind: A Prospective Multicenter Study. Arch Dermatol Oct 2010: E1-E7