Dermatoscopy

Other algorithms for melanocytic lesions

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Learning objectives

  • Describe several algorithms for evaluating melanocytic lesions by dermoscopy

Introduction

The first step algorithm identifies whether a lesion is melanocytic or nonmelanocytic. Various algorithms may be used to distinguish benign melanocytic lesions from malignant melanoma.

If these algorithms appear too complicated, use the 3-point checklist to identify malignant pigmented lesions.

ABCD rule

The ABCD rule (Stolz method) is used for the dermoscopic differentiation between benign melanocytic lesions and melanoma. The likelihood of melanoma depends on adding up the scores for different features as shown below.

CriteriaScore  X  Factor  =  Results
Asymmetry
In perpendicular axes: contour, colours and structures
0 - 2 1.3 0 - 2.6
Borders
8 segments: abrupt ending of pigment pattern
0 - 8 0.1 0 - 0.8
Colours
White, red, light-brown (tan), dark-brown, blue-grey, black
1 - 6 0.5 0.5 - 3.0
Differential structural components or Dermoscopic structures (pigment network, structureless areas, dots, aggregated globules, branched streaks) 1 - 5 0.5 0.5 - 2.5
Total score Benign   <4.76
  Suspicious   4.76-5.45
  Melanoma   >5.45

Blum's modified ‘ABC-point list’ is simpler to use:

  • A – asymmetry of outer shape or differential structures inside the lesion in at least 1 axis
  • B – the abrupt cutoff of network at the border in at least one quarter of circumference
  • C – 3 or more colors
  • D – 3 or more differential structures
  • E – noticed change (evolution) in the last 3 months

Menzies method

Menzies method to distinguish the dermoscopic features of benign melanocytic lesions from melanoma.

Negative features (benign lesions):

  • Symmetrical pattern (colours, structure)
  • Single colour

Positive features (melanoma):

  • Blue-white veil
  • Multiple brown dots
  • Pseudopods
  • Radial streaming
  • Scar-like depigmentation
  • Multiple (5-6) colours
  • Multiple blue/grey dots
  • Broadened network

Seven-point rule

7-point checklist (Argenziano) for the dermoscopic differentiation between benign melanocytic lesions and melanoma (scores in brackets). The scores should be added up. Three or more indicates melanoma.

  1. Atypical pigment network (2)
  2. Blue-whitish veil (2)
  3. Atypical vascular pattern (2)
  4. Irregular streaks (1)
  5. Irregular dots/globules (1)
  6. Irregular blotches (1)
  7. Regression structures (1)

The seven-point rule has been revised (2011) so that each item scores 1 (total is 7). IN patients with atypical naevi, any lesion with a score of one should be carefully examined and excision of such lesions will pick up many early-stage melanomas.

C.A.S.H. acronym

C.A.S.H. (Kopf et al) is used for the dermoscopic differentiation between benign melanocytic lesions and melanoma (scores in brackets).

Suspicion for melanoma
LowMediumHigh
Colours: few vs many
Light brown, dark brown, black, red, white, blue
Score 1 point for each colour
1-2 colours (1-2 points) 3-4 colours (3-4 points) 5-6 colours (5-6 points)
Architecture: order vs disorder
Score 0-2 points
None or mild disorder (no points) Moderate disorder (1 point) Marked disorder (2 points)
Symmetry vs asymmetry
Consider contour, colours and structures
Score 0-2 points
Symmetry in 2 axes (no points) Symmetry in 1 axis (1 point) No symmetry (2 points)
Homogeneity vs Heterogeneity
Consider pigment network, dots/globules, blotches, regression, streaks, blue-white veil, polymorphous vessels
Score 1 point for each structure
Only one structure (1 point) 2 types of structure (2 points) 3 or more structures (3-7 points)

Add up the scores for a total C.A.S.H. score (2 to 17).

  • C.A.S.H. score of 7 or less is likely benign.
  • C.A.S.H. score of 8 or more is suspicious of melanoma.

CHAOS and clues

A modified form of pattern analysis (Kittler, Rosendahl et al) looks for CHAOS (asymmetry of structure and/or colour) and at least one clue to diagnose malignancy. It can be applied to melanocytic and non-melanocytic lesions. This method does not use the 2-step rule.

Patterns are described by multiple elements of the same type: lines, dots, clods, circles, pseudopods (a line with a bulbous end) and structureless areas. Structureless areas are made up of colours: black, dark brown, light brown, grey, blue, orange, yellow, white, red and purple.

  • A single pattern or a single colour is a symmetrical structure, i.e. benign.
  • Two patterns can have one pattern inside the other pattern or the two patterns may be regularly distributed. Such lesions have a symmetrical structure. Two patterns can be distributed asymmetrically.
  • Multiple patterns / colours may result in symmetrical structure if forming concentric zones. Otherwise, they result in an asymmetrical structure.

Asymmetrical patterns should lead to looking for specific clues to malignancy. The clues to malignancy (melanoma and no-nmelanocytic tumours) are:

  1. Thick reticular lines
  2. Grey or blue structures of any kind
  3. Pseudopods or radial lines at the periphery
  4. Black dots in the periphery
  5. Eccentric structureless area of any colour
  6. Polymorphous vascular pattern
  7. White lines
  8. Parallel lines on ridges

Some of these images are poor quality, but the algorithm still works.

The BLINCK algorithm

The BLINCK algorithm has been devised to identify malignant lesions, particularly nodular melanoma, as this tumour often lacks conventional dermatoscopic features. It can also be used for nonmelanocytic lesions.

Benign If not, then consider the following:
Lonely An ugly duckling Score 1
Irregular Asymmetrical pigmentation pattern or >1 colour Score 1
Nervous Nervous patient OR changing lesion Score 1
Known Known clues to malignancy Score 1

Clues to malignancy are:

  1. Atypical network
  2. Segmental streaks
  3. Irregular black dots, globules, clods
  4. Eccentric structureless zone
  5. Irregular blue or grey colour
  6. Polymorphous, arborising, glomerular vessels
  7. Parallel ridge pattern or diffuse irregular brown/black pigmentation in acral lesion

A score of ≥2 requires biopsy.

Some of these images are poor quality, but the algorithm still works.

Activity

Find the evidence to support the use of the algorithms. Which is the best?

Related information

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