In the previous chapters I described the major cancer types, and briefly talked about some of the mimics of skin cancer. You might now be thinking that you have got the hang of things, ready to move on. Sadly, I have some bad news for you. Let me sketch out a simple thought experiment, a simple example that reveals how messy the real world of clinical medicine is.
Acres of skin
Take 1000 patients, the sort of number that might be on a (small) GPs list or the practice of an office dermatologist. Each patient has just under two square meters of skin, and I have therefore represented the total area of skin (2000 sq m)in the figure below.
Now let us add in something about skin cancer. For simplicity I will use melanoma because, although it is not the commonest skin cancer, melanoma accounts for over 75% of deaths from skin cancer.
The incidence of melanoma in many countries is close to 20/100,000. So in our target area of skin, over one year we would expect to see 0.2 melanomas. I have shown the red arrow pointing to a single melanoma, but in reality this is five times more than we can expect.
If this was all there was to melanoma diagnosis, many dermatologists would be out of work. Simply because if all you have to do was detect a single lesion — any lesion — it would require little skill. But now look at the figure below.
The arrow still points to the 0.2 melanoma, but the whole area of skin is covered in other lesions — the estimated 50,000 other lesions for every 0.2 melanomas [ ]. This is the clinicians problem. No matter that skin cancer is the commonest humans cancer, non-cancer lesions — moles, seborrhoeic keratoses, solar lentigines etc — are, in total, much more common (see the notes below for the justification for the figures used [ ]).
The clinicians skill is therefore not demarcating cancer from normal (skin), but demarcating cancer from the benign lesions that are orders of magnitude more common.
What is the chance of a mole becoming a melanoma.
If the above is not stark enough, I can represent the problem is a different way using some simple arithmetic using figures I have used already.
The average adult in the UK has close to 25 melanocytic nevi. We believe that fewer than a half of all melanomas are derived from benign melanocytic nevi [ ]. Given the incidence of melanoma is approximately 20/100,000, simple arithmetic shows that the chance of any single melanocytic nevus becoming a melanoma in one year is close to 1:250,000 [ ].
If you have ever thought that ‘wholesale removal of moles’ was the way to go, then you will have to remove 250,000 per year to catch one melanoma — and you will still miss half of all melanomas.
Signal to noise
The visual example above, and the simple arithmetic in the preceding paragraph, highlight that diagnosis in this domain of medical practice can be viewed as a signal to noise problem. Even if skin cancer is so common, how can we distinguish cancer from the myriad of mimics that are orders of magnitudes more common?
In many domains of medicine we could rely on tests or machines and simple algorithms. We might measure blood pressure with an automated tool, and decide on action if the machine tells us that the diastolic is over 100 or some other figure. We might imagine similar approaches with say renal failure: a blood test, and a simple rule that all can understand.
The problem in dermatology is that we have no machines and only to a very limited extent can we use rule based approaches. So how do we proceed?
The main method of distinguishing skin cancers from their mimics, and for distinguishing between different skin cancers, is a process called non-analytical reasoning. Do not worry about the term, because you use this process every minute of your life. It is how you rcognise faces, how you distinguish cats from dogs, how you know the difference between a bicyle and a lawnmower. Once you have seen enough examples, and have received feedback — just like your parents gave you when you learned the difference between cats and dogs — the process appear effortless and is usually very fast. The expert recognises a melanoma simply by noting that the index lesion resembles earlier example he or she has seen. Experts are also usually unaware of how they accomplish the tasks, but they candemonstrate their expertise.
What about the novice?
Everybody has to start somewhere, and one way to start acquiring these skills in simply to start looking at lots of images (hence this web site). But there are tricks and perhaps simple heuristics that we can use to get you going, and I am going to talk about some of the ones that are used for melanoma.
As easy as the ABCD(E)
One approach to melanoma diagnosis relies on the following nmenonic, ABCD(E)
- A for asymmetry
- B of border irregularity
- C for colour
- D for diameter
- E for evolution or elevation
Melanomas are often asymmetric in terms of pattern, colour or shape. The border of melanomas are often irregular; there are often multiple colours within a melanoma; and melanomas are often greater than 1cm across. In distinguishing between melanocytic nevi and melanomas, many experts claim these simple ‘rules’ help. However, a sceptic might question how these terms are operationalised. How irregular, how are colours defined, how asymmetrical etc? My own view is that the term may be useful as a reminder of features to consider, but that its role is limited. In any case mimics such as many seborrhoiec keratoses score highly on these criteria, too [ ].
A history of change
In the ABCD nmenomic E is often added, either short for elevation (some melanomas are raised), or for evolution, meaning change. History of change in a pigmented lesion is key history point: most melanomas have arisen due to ‘change’ and with time, will continue to change. So, eliciting the history is key. However, as you might have guessed there is a caveat: lots of non-melanomas change, and there are many more non-melanomas than there are melanomas. Melanocytic nevi are more common in 20 year olds than 10 year olds. They are more common at thirty, but then seem to disappear. As you age more and more seborrhoeic keratoses appear. In fact studies show that a significant proportion of the normal population report changes in lesions on their skin in any time period. And of course normal lesions greatly outweigh the numbers of melanomas.
All the above not withstanding, once you focus in on a particular lesion, history can be key to how you decide what to do next. A history of change raises the stakes. Do not ignore it.
Background and demographics
Risk factors may provide important insights into disease pathogenesis in many area of medicine, but their role in the diagnostic process in dermatology is often more marginal than most non-experts realise. Yes, skin melanoma is say 3 times more common in those without read hair. Yes, melanoma is more common in 70 year olds than 7 year olds. Melanoma is perhaps 4 times more common in Australians that British people. But all this data may only have a faint influence on your judgment about a particular lesion. Let me use some simple familiar. arithmetic.
I have said that the chance of a single mole becoming a melanoma in any one year is approximately 1:250,000. If the patient has red hair and we argue that the risk is now 3:250,000, this still means that the chances of it not being a melanoma are still 99.9988% (249,997/250,000) as compared with 99.999% (249,999/250,000).
So, as a general rule demographics, and history of sun exposure and holidays etc often only play a minor role in the clinical assessment. What you see with your eyes, and a history of change — and a history of previous melanomas — count for much more.
There once was an ugly duckling...
Us skin watchers will tell you that people’s moles and other lesions often have a ‘character’. To put this in terms a statistician would use: within person variance is smaller than between person variance [ ]. If you look at one person’s skin the moles often seem similar to each other. Another person’s moles may look different, but again, within that person they seem to have some common qualities.
Sometimes, you see a pigmented lesion that stands out from all the other melanocytic nevi. The so-called ugly duckling sign. Look at the back of the individual below. Notice how one mole stands out (white arrow) like an ugly duckling.
Hard though it is make explicit, many of us view this as a red flag sign — get expert advice. So, a person may have ten moles that all look similar — we would probably rarely get suspicious about this because, it is very unlikely that a patient will present with 10 primary melanomas. But one of these moles on the background of a different patient, might arouse intense clinical suspicion.
More than melanoma
The above sections have concentrated on melanoma, simply because that is perhaps the skin cancer where early diagnosis is both critical and so hard, and because melanoma accounts for most skin cancer deaths. Similar thoughts apply to the other skin cancers such as basal cell carcinoma and squamous cell carcinoma, and the premalignant lesions. Most clinical expertise relies on having seen many similar lesions (and patients) before, rather than application of formal rules. The distinguished and late epidemiologist and biostatistician Alvan Feinstein is a book entitled ‘Clinical Judgement’ described a clinician as nobody who:
‘depends not on a knowledge of causes, mechanisms, or names for diseases but on a knowledge of patients’
Just for the record, do I think machines will reduce the need for these clinical skills. Yes, but that is another story [ ].
Continued exposure and practice as the basis for expertise
One point that is implicit in what I have said above relates to the fact that perceptual expertise in dermatology (or pathology or radiology, for that matter) relies on exposure to large training sets (i.e. seeing lots of patients). This is not the only basis for expertise, but it is key.
However, as all medical students know, you tend to forget what you do not use. So, it is not enough to have seen lots of lesions, if you do not not continue to see lots of lesions. And you must see positive and negative examples frequently enough to maintain any skills you once had. So, for instance in primary care, an average GP will only see a patient with a primary melanoma once every 5 to 10 years. This makes maintenance of skills problematic whatever skills were gained in more intensive periods at an earlier stage.
Is Dermatoscopy / dermoscopy the solution?
Dermatoscopes are simple lower power lens that magnify, and if placed on the skin’s surface with suitable liquids (e.g. oil, alcohol), alter the optics such that speculation reflection is reduced. You can use non-touch polarising light devices as well, that produce similar but not identical effects. The major role for dermatoscopy is in melanoma diagnosis.
Dermatoscopy has become very popular in the last 20 years, but we do not expect out students to use it. In expert hands, most of us feel happier with the knowledge it brings. However, available studies suggest it may worsen diagnostic performance in the hands of non-experts. In any case, if you are not seeing a high throughput of melanomas, the criticisms made in the previous paragraph still stand.
Skincancer909 by Jonathan Rees is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Where different rights apply for any figures, this is indicated in the text.