Do Mammograms Cause Cancer?

Let’s just start with some facts on the magnitude of the risk of female breast cancer (National Cancer Institute SEER statistics, 2012):
1.  In the US today, about 2.7 million women have had a history of breast cancer.  That’s about the population of the city of Chicago, the third most populous city in the US.
2.  Over 12% of women born today will be diagnosed with breast cancer during their lifetime.  That’s about 1 in 8.
3.  The incidence rate (new cases) is 124.3 per 100,000 women per year.
4.  The median age at diagnosis is 61 and the median age at death is 68.
5.  If breast cancer is caught while it is still local; before it spreads, the 5 year relative survival is 98.6%.  Once it has metastasized, the 5 year relative survival drops to 23.8%.
6.  Breast cancer is the second leading cause of cancer death in women, behind lung cancer.

It is in 4 and 5 that the importance of screening is highlighted.  We need a way to diagnose cancer early on, while it is still easier to treat and offers a very positive prognosis.

Enter the breast cancer screening and diagnostic tests.  Let’s be clear about the difference between those two:

A.  Breast cancer screening is conducted on someone without signs or symptoms of disease.  It typically identifies risk categories and gives a healthcare professional additional information needed to guide further testing, but it doesn’t stand on its own as a result.  This might include a medical history, a BRCA/genetic screening panel, breast exam, X-ray mammography or other imaging technology.

B.  Breast cancer diagnostic tests are conducted when a risk is identified.  These are usually the higher risk tests, but they can more definitively identify the important information about the disease.  In the case of breast cancer, this may involve biopsy, diagnostic imaging or other tests.

Mammograms are a very common screening method, but are recently somewhat controversial.  Let’s take a quick look at what the recent research on the topic has uncovered:

1.  Mammograms CAN increase cancer risk, but they prevent far more cancer than they cause.

A woman is exposed to about 4 milliGrays (mGys) in a mammogram screening.  For comparison, an abdominal CT scan might expose you to about 8 mGys.  It’s not inconsequential.  It can cause DNA damage that can lead to cancer, but so can flying in an airplane or living in an area with abundant radon gas.  We need a better way of evaluating the actual risk than simply: “Radiation bad, no-radiation good”.  This is precisely what studies have attempted to evaluate.

If we limit the biennual mammogram risk/benefit calculation to women aged 40-49, a 1997 study found that for every life lost as the result of screening, about 50 were saved (J Natl Cancer Inst Monogr. 1997;(22):119-24.).  To put it another way, for every YEAR of life lost, 121 years were saved.  Given those odds, you are much, much more likely to be saved by screening than harmed.  However, you do have a right to know that the procedure does come with risks as well.

In the women aged 50-69 years category, the ratios of lives lost to lives saved may be much, much higher.  One study reports a ratio of 1 life lost for every 242 saved (J Med Screen. 1998;5(2):81-7.).


2.  Mammograms have a relatively high rate of false positives.

False positives, just to clarify, are when a test falsely indicates cancer exists where none actually does.  “Abnormal” mammography results only indicate cancer 1 out of every 10 diagnoses.  For some women, that means a lot of psychological distress and “needless” biopsies on tissue that later turns out to be benign.

3.  Mammograms have a low rate of false negatives, but they still won’t pick up all breast cancer.

The false negative is when the mammogram is read as “normal” missing the presence of breast cancer.  A mammogram fails to detect the presence of cancer in between 10 and 25% of cases where breast cancer can be confirmed later.  The large range is because of the difference in various studies on the topic.

Some studies suggest that mammograms are most effective when combined with a clinical breast exam.

4.  Although there are alternatives to X-ray mammograms, they aren’t going to replace mammograms anytime soon.

You may have heard about MRI breast cancer screening, or thermography (imaging the heat of your body), or Breast Pap smears, or some other similar tests.  If you haven’t, here’s a plain language “About.com” site that talks more about them.  The trouble is that none of them, alone, are as suitable for the primary mission of low-cost breast screening in small communities.  MRI’s, for example, give very good high definition imaging of breast tissue without the ionizing radiation exposure of an X-ray.  Unfortunately, they cost about 10 times as much, require the use of injected contrast agent, take an hour or more per scan, and are only available in certain geographies.  There are also certain types of breast cancer that an MRI has trouble identifying.  In many ways, the MRI is just TOO sensitive, and it requires a radiologist’s expertise to determine whether density differences are normal or abnormal.

The one other screening technique that should be included here is clinical breast exams, where a trained physician or nurse conducts a physical exam of the breast, feeling for any abnormalities in shape or texture.  It’s been shown to be very effective when done properly to detect large tissue abnormalities, but not very small growths.  It’s lower risk, can be low cost and easy to administer, and provides some additional value when used in conjunction with other techniques.

So all these techniques are generally quite good at improving the performance of overall breast cancer screening, but none are poised to replace mammography as a primary method of screening.

Why do I need to talk about this?  Because it’s very popular right now to exploit these facts in a vacuum; to twist them all to mean something much more profound: that mammograms for breast cancer screening are a bad decision for all women in all cases.  This is simply a LIE.  The only controversies are about how often a mammogram should be performed and at what age it should become a regular practice.  As we saw, the benefit to risk ration is overwhelmingly positive, but it increases even more with age.  If you are a woman between 40 and 49, you really should ask your doctor if an annual or biennual (every two years) mammogram is a good idea.  At 50 or older, the answer is almost certainly going to be yes because the risk of NOT being screened is so high.  Certain women, those with inherited BRCA genetic mutations, and those who are young (<30) really need to discuss the risks with their physician or a specialist, as the risk of exposure to radiation might outweigh the potential benefit.

Over the years, I’ve published a few rules in my videos.  The very first, and the most important of these I call C0nc0rdance’s First Law:

NEVER TAKE HEALTH ADVICE FROM THE INTERNET.

It’s intentionally self-inclusive.  If you want accurate health information, please, please, please consult a trusted healthcare provider.  The Internet is a free-for-all, and the content is as likely to be wrong as right.  Make sure you put your life in the hands of someone with experience and training and certification in medicine, not in the hands of someone who believes the Reptiloids are working in the service of the Illuminati to create a New World Order for the Alien Greys (/if that’s what your physician believes, get a new physician/).

A "typical course" flow chart for screening and diagnosis. Note that mammograms and clinical exams are really just an entry point to a more involved process.

Some Foods Contain Nicotine… But Not Much

One fact frequently used by proponents of e-cigarettes is that regulating their favorite product because it contains nicotine means we should also regulate foods that contain nicotine.  Yes, many foods we eat produce measurable levels of nicotine.  Tomatoes, potatoes, cauliflower, and green peppers are known to contain small amounts, and black tea is sometimes suggested to contain some, although recent tests don’t confirm this.

Does this nicotine affect us?  Are we, like Homer Simpson, creating addictive tomato-tobacco hybrids (Tomacco)?  The short answer is no.  The dose makes the poison, and in this case the dose of nicotine you receive from a cup of these vegetables barely registers when compared on the same graph with the nicotine content of a single cigarette.  The figure below uses figures produced in a letter to the editor of the NEJM.  Cooked vegetables, especially boiled, will probably have lower values, but the amount is only really significant in relation to the specificity of clinical tests designed to detect nicotine (and cotinine, its metabolite) levels in the blood.

What this figure also highlights is the potential harm from the abundant nicotine found in e-cigarette juice.  The cartridges are intended to be used many, many times so that the delivered dose is not much different than a single smoking session with a real cigarette, but it would be a simple thing to forget and over-consume from the cartridge, resulting in increased total nicotine intake.

I’ve covered it before, and frankly I have more important topics to deal with, but I personally think there’s not enough evidence to conclude that e-cigarettes are as safe as the FDA regulated nicotine replacement therapies designed to help people quit.  As always, I recommend anyone with questions about their health consult an actual physician.  I’m just some guy on the Internet.

John Ioannidis Is Just What Science Needs

For those of you who haven’t seen my video on “Why Most Research Findings Are False” or otherwise aren’t familiar with the excellent work of John PA Ioannidis, I invite you to learn more about his work:

He’s done such an important job in being the conscience of clinical science: exposing where the scientists are prone to self-deception.  He’s a critical thinker about critical thinking, and I love him for it.  Apparently, so do other scientists, because his article is the most downloaded from the Public Library of Science (PLoS) site.  His paper was also the clearest on “prior probability” and Bayesian statistical methods that I have ever read.

So now I come across his work in my background research on antidepressants, and as usual it’s a clear and revealing look at the critical thinking that should underlie the body of research.  Here’s the full-text article:

Effectiveness of antidepressants: an evidence myth constructed from a thousand randomized trials?

His wording is forceful and unapologetic:

“Based on the above considerations, antidepressants are probably indicated only in select patients with major depression, probably preferentially in those who have severe symptoms and have not responded to anything else. For most patients with some depressive symptoms who are currently taking antidepressants, using these drugs would not have been the preferred option, placebo would be practically as good, if not better, and would save toxicities and cost.”

Did you catch that?  Placebo would have been as effective, or more effective, in many cases without the side effects.  I have to concur based on the systematic reviews I’ve screened so far.  I think the number needed to treat says a lot as well, and will probably include that concept in the future video on the topic.  It’s not that they’re ineffective, it’s that they’re badly misused, overdiagnosed, and I think a big part of the blame can fall on pharma advertising on the airwaves.  The numbers below say a lot about why we keep generating new antidepressants:

Table 1
Top-selling antidepressants in the USA, 2006
Drug (brand name) Rank across all drugs Sales (billions $)
Venlafaxine XR (Effexor XR) 6 2.25
Escitalopram (Lexapro) 10 2.10
Sertraline (Zoloft) 15 1.77
Bupropion XL (Wellbutrin XL) 16 1.67
Duloxetine (Cymbalta) 35 1.08

Do the Latest Antidepressants Work?

I’m continuing my review of the literature on antidepressants.  Today we cover a systematic review in PLoS Medicine, which is one of the new open access journals that anyone can access.  It’s been amazing to watch these open-source journals revolutionize access to the literature for people like me who don’t have university access.

Here’s the link to the full text article.

PLoS Med. 2008 February; 5(2): e45.

The authors are diverse and from different countries, and they’re only considering FDA submitted data.  I’ll let them summarize their findings:

“Using complete datasets (including unpublished data) and a substantially larger dataset of this type than has been previously reported, we find that the overall effect of new-generation antidepressant medications is below recommended criteria for clinical significance. We also find that efficacy reaches clinical significance only in trials involving the most extremely depressed patients, and that this pattern is due to a decrease in the response to placebo rather than an increase in the response to medication.”

Let me give my own translation:  Unless someone is VERY, VERY depressed, it was hard to justify using these new antidepressants, and then only because there are no non-pharmacological solutions that seem to work anymore.  In other words, it’s not that the drugs worked particularly well in any case, but the placebo effect was no longer of any benefit.  There’s a figure that illustrates this pretty well:

As you start at the left with the people who started out less severely depressed, placebo outperforms the new drugs by a small amount.  Then, as the benefit of placebo decreases, the difference between the two increases.  It’s not that the drugs get that much more effective in people with stronger symptoms, but the difference increases until finally we enter the “green zone”, the area where the drug outperforms the placebo enough to be clinically relevant.  By that point the drug is still not showing much improvement.

I personally found that pretty surprising, given that the data they are mining for the review was in the FDA’s hands.  I went into this evaluation with a general impression that SSRIs and the like work only in cases where there’s an underlying serotonin problem (brain chemistry issues), but now I see that this isn’t going to be that straightforward.

My one concern is that in grouping all these studies together, there seems to be a lot of diversity in the kind of results produced.  I wonder what caused those outlier triangles with high improvement (high on the Y axis).

The investigation continues!

C.

Do Antidepressants Increase Suicide Risk?

I want to present a single paper on the topic for the moment.  It’s a meta-review:  it reviews the existing reviews/summaries of what is known, and it’s perfect for someone like me who really needs the high-level view of the topic.  There are some newer reviews on the topic, but none so thorough as this.  I’m only focusing on a single comparison, but the paper goes into several other topics.

Here’s the paper, full text, not behind a paywall:

BMJ. 2005 February 19; 330(7488): 396. “Association between suicide attempts and selective serotonin reuptake inhibitors: systematic review of randomised controlled trials”

This figure really tells the story.  It breaks down whether the literature supports an increased risk for suicide attempts in people taking placebo or SSRI antidepressants.  The effect is rather weak, but multiple studies support this slight increase.

What I look for in data like this are error bars that don’t overlap the midline… in other words we can be reasonably confident that there’s a reproducible “real” difference between these two groups, and that difference supports an increased risk in people taking the drug.

I always need to caution people who read these types of epidemiological data with the standard warning:

“WARNING: The risk assessments here are STATISTICAL ONLY.  Being 5% more likely to make a suicide attempt does not mean SSRI’s will cause you to attempt suicide!  It may have an effect on a susceptible proportion of the population, but all we can say for sure is that SSRI users are slightly higher risk than placebo users with depressive conditions”

Scientists have to be very careful communicating these risks, because we’re so used to minor statistical differences between very large groups that we forget that patients don’t see themselves as statistics or group members… they translate stochastic risk into deterministic outcomes.

I’m not sure if I follow why the sex differences are significant.  I gather from the literature that depression in women can be related to PMDD, possibly using a mechanism different than non sex hormone specific depression pathways in the brain?

It’s also interesting to me that pharma-funded research is actually more likely to find increase suicide attempt risk than non-pharma funded.

The paper contains a nice summary of findings, which I will briefly quote here:

“We documented a more than twofold increase in the rate of suicide attempts in patients receiving SSRIs compared with placebo or therapeutic interventions other than tricyclic antidepressants. Although many trials have documented the benefits of SSRIs in many forms of depression and other clinical indications, it has been difficult to document the relatively rare but very serious risk of suicide. We documented a difference in absolute risk of 5.6 suicide attempts per 1000 patient years of SSRI exposure compared with placebo. Although small, the incremental risk remains a very important population health issue because of the widespread use of SSRIs. In the United Kingdom, 1 million person years of SSRI treatment are provided annually by general practitioners.13 For the United States, the number of visits by patients for depression was 24.5 million in 2001, a 70% increase since 1987.1 In 2001, 69% of patient visits for depression resulted in prescriptions for SSRIs.1 Thus, a large number of patients were at risk for treatment induced suicidality.”

Understanding the “Hayflick Limit”

Leonard Hayflick, 1961 at the Wistar Institute

I met with an eminent scientist in the course of my work, and as part of the background research, I checked out his CV (curriculum vitae).  His background is substantial, Harvard education and NIH post-doc, but his mentor during his post-doc was Leonard Hayflick.  I knew about the Hayflick Limit from grad school, but I had never read the history of the phenomenon.  I thought it might make an interesting example of how science is conducted.  We’ll come to a definition shortly.

You may be aware that scientists routinely grow human cells in flasks.  It’s called cell culture or tissue culture.  We’re not talking about an arm or an ear, here.  The cells are only visible as a thin layer of translucent material, in the case of cells that adhere to a solid surface, or a slightly grainy slurry, in the case of cells in suspension.  We feed these cells, and they are bathed in, a solution containing salts, sugars, amino acids and other essential nutrients.  They also sometimes get antibiotics and growth stimulants.  Most also receive a healthy dose of serum, extracted from cow blood, in order to provide specialized proteins that stimulate growth.  We might say, for example, that our K-562 cells (immortalized cells from a patient with myelogenous leukemia) grow in RPMI1640 (a specific sugar/salt/amino acid solution) supplemented with 10% FBS (fetal bovine serum) plus pen/strep (penicillin/streptomycin) and L-glutamine (needed by some cells, and somewhat unstable in

Cell culture flasks with culture medium

storage).  It looks complicated, but is not much different from a recipe for chicken soup.  The flasks containing cells in their growth medium are placed in incubators, usually at 37 C and 5% CO2.  Periodically, the medium has to be changed, and if the cells are rapidly growing, they have to be passaged or “split”, removing a fraction of the growing cells, to avoid overgrowth.  We guard carefully against introducing infectious agents, since the cells have no immune system and any bacteria or fungus landing in the cell culture medium, loaded as it is with the materials of life, will rapidly overgrow, acidify the solution, and kill the cells.

So those are the very basics of growing mammalian cells in culture.  Now we come to the interesting part.  If I take a small scraping of your cheek and transfer them to a flask, they will grow for some time, then eventually they’ll stop growing and instead begin dissolving.  The number of divisions that occur before this happens is called the Hayflick limit.  Initially scientists assumed this was a technical problem with how they were culturing… not enough nutrients, or the introduction of bacteria or fungus pathogens… something was ending the life of the cells.

Some cells, however, could continue to grow almost indefinitely, almost always cancer cells or viral infected cells.

If they split the cells into two flasks, both cultures stopped replicating at about the same time, which made simultaneous infections unlikely.

Cells taken from embryonic tissues lasted much longer than mature cells taken from older patients.  Hayflick measured the number of times an embryonic cell could divide.  It converged on a value of about 50 divisions.  This would later be called the Hayflick limit.

Many cancer cells (~80%) do not obey the Hayflick limit, they are "immortalized".

So once the phenomenon was known and the puzzle was well-characterized, a number of hypotheses were advanced to explain the anomalous observation.  They focused on the exceptions to the Hayflick limit rules, cells that never stop growing, which we call immortalized cells.  These cells were almost universally taken from either cells infected with specific viruses, or from very active cancer cells.

So what do these two conditions have in common?  Both had damage to their DNA in certain specific regions that mediated cell division and self-destruct mechanisms.  Most cells recognize their genetic damage as a good reason to self-destruct, to undergo apoptosis.  These cells no longer had the ability to respond to that self-destruct signal.  That alone wouldn’t have been enough, though.  They had also acquired an enzyme that was able to add new sequences to the ends of their chromosomes.   These sequences are called telomeres.

Telomeres are to the chromosome as the aglet (the little plastic or metal collar) is to the shoelace.  The telomere prevents the DNA strand from “unraveling” by providing a long string of very tightly bound base pairs.  One of the consequences of copying your DNA is that a little chunk from the very end of each of your 46 chromosomes is lost every time they are copied.  When you are a fetus, you have nice long telomeres securing your chromosome ends.  As you age, as the result of replicating your DNA, they get shorter.  Eventually, they become too short to hold the ends together, and we think that’s why some cells must stop replicating.

Enter telomerase, a cellular enzyme that extends the length again.  In my next post, I’ll examine what it does in the cell; whether it can be harnessed to make us essentially immortal, and we’ll examine some of the claims of futurists and anti-aging researchers.  We’ll also look at why evolution favors our aging and eventual death, and those rare human genomes that have achieved near immortality.

I’ve just use something I learned on “Phineas and Ferb” in a science posting. Day complete.

Electronic cigarettes

The video that YouTube considers SPAM.

I think it’s going to be impossible to please all the people all the time on this topic.  Comments are open and welcome.  My “short-version” position for the TL;DNW (too long, did not watch) crowd is:

“Quitting cigarette smoking can only be a good thing, however not all nicotine replacements have met the same standards of evidence and testing.  Electronic cigarettes may yet be found to be effective and safe replacements for smoking, the evidence isn’t yet sufficient. but they should be regulated under the highest standards of safety and quality: the federal medical device codes.”