|Poster to be presented at the Department of Medical Imaging Resident Achievement Day 2016
Where have a I been you ask? At my desk putting this program together! I apologize for being MIA for the past month or so but I it has been a busy time nurturing this fledgling program of MiNE (pun intended!).
Here is the premise:
Bridging the gap between clinical expertise and the science of managing and analyzing medical imaging data is challenging. To provide direction for data management as well as the analysis and reporting of research findings, we are in the process of introducing a data science unit – MiDATA – offering users an environment geared towards a “soup to nuts” approach to medical imaging research methodology and statistics. The Department of Medical Imaging of the University of Toronto is one of the largest in North America with a clinical faculty of more than 184 faculty, 60 residents and 80 fellows based at nationally and internationally renowned hospitals conducting cutting edge clinical research in the greater Toronto area. The challenge of any successful research and educational program is bridging the “know-do” gap. The goal of MiDATA is to facilitate impactful research through the efficient and creative use of a mentored learning environment.
Shout out to our collaborators the Division of Biostatistics from the Dalla Lana School of Public Health!
Tomorrow is the official unveiling at the 2016 Department of Medical Imaging Resident Achievement Day. I thought I would share with you our poster as a sneak peek…
Once you have digested its contents have a listen to Paper Planes by M.I.A. to decompress and…
… I’ll see you in the blogosphere (or at tomorrow’s event!)
An interesting quotation from the Hebrew bible. Basically it means that I have sufficient for my needs and I am good with that. So, where am I going with this you ask? Well, let me introduce you to my program MiCUP – Medical imaging Collaborative Undergraduate Program.
The goal of the program is to bring together students from the faculty of Arts and Sciences and my faculty (Medicine) to learn about medical research in the world of medical imaging. I have a sprinkling of students every term from various programs such as Research Opportunity Program, Independent Studies, Youth Study Program, and MiVIP. It is only a modest number of students BUT provides ample brain power to get some really cool research done. My cup certainly runneth over.
Have a look below at the timelines from my two recent ROP students.
Great work Kevin and Sylvia!!!
See you in the blogosphere,
|Kevin Chen ROP F/W 2014
|Sylvia Urbanik F/W 2014
No, I did not say “bodily functions”. That is discussed in another blog. We’re talking math today.
So, my son was doing his homework the other night and yelled out from his room:”Daaaadddyyyy!!! Do you know what a parabola is?” For those of you who do not have teenage children this is code for “can you help me with my homework”. After reliving a few high school memories that came along with the word “parabola” I wondered over to his room to see what the latest homework challenge was going to be…
When helping my kids with their homework, I often think of how important and still relevant some of the basic math is we learnt in high school. I would like to talk a little about basic functions and how they are still used well after you have handed in your last math homework assignment.
Many (most?) scientific laws are expressed as relations between two or more variables – often physical quantities. Next comes the chicken or the egg conundrum. Were the results from an experiment used to formulate “empirical laws” or did we use existing knowledge and math to come up with new theories – that we will invariably later have to test. Welcome to the world of research!
If two variables are related in such a way that one of them (the dependent or response variable) is determined when the other is known (the independent or explanatory variable), then there exists what is termed a functional relationship between the variables.
y = f(x)
For example the relationship of height to weight in humans. In general, the taller we are the heavier we get. This results in what is called a straight-line relationship.
But not all relationships are linear. How about if we were to throw a ball up into the air and measure it’s trajectory? It would look a little like the picture on the left.
Although initially the value of the height of the ball increases with time, there comes a point when the ball stops rising and starts to fall back down to earth. The resulting curve is called – you guessed it – a parabola.
The math functions for the parabola and that of the straight line are actually related. Yes, I am serious! They both belong to the family of math functions called polynomials. In my next posts I will talk a little about how we describe these functions and how we can put them to work for us in the world of medical research.
For now, decompress watching this hilarious movie trailer Biloxi Blues which is all about basic training (you can now relate) and…
… I’ll see you in the blogosphere,
You are thinking about pursuing studies in medicine. You have enrolled in all the necessary courses at school to qualify you for the grueling application process and you are actively looking for volunteer opportunities. So why the need to be active in your community?
Today, I want to talk a little about the history of medicine. Around 3000 BC (and no I was not alive then if you are wondering) the middle east was a hotbed for civilizations who were in transition from being mainly nomadic to more settled. This “land between the rivers” – Mesopotamia – was ruled by many successive great kingdoms including the Akkadian, Babylonian, and Assyrian empires. Thanks to many archaeological and written remains we have discovered that healing practices indeed existed and were established during these times.
Mesopotamian medicine was predominantly religious and was delivered by a team of healers: the seers who would diagnose based on divination, the exorcists who would expel demons, and finally the physician priests who actually treated the sick mostly with charms, drugs, and some surgical procedures. OK, so this intensely codified approach (which meant very little opportunity for discussion) to healing that dominated the Mesopotamian kingdoms would not be able to adapt or improve much over time and would ultimately not contribute much to the Greek rational medicine that would come a later and evolve into today’s medicine.
So why is it important? For two reasons:
Firstly, by understanding the history of medicine you will better appreciate the importance of your role as a physician in your community – regardless if you are a primary care physician on the front line or a radiologist who works in the back ground. What is important is to feel connected and part of your community.
Secondly, it is interesting to see that though Mesopotamian medicine recognized very early on that factors like cold, alcohol, and unhygienic conditions affected health, they were enable to advance and evolve their medicine as Ancient Greece did through ongoing experimentation and discussion. Moral of the story? Medical research rocks!
Do you remember the Babylon 5 series? It came many, many, many years later! Have a peek to decompress and…
… I’ll see you in the blogosphere.
So, let’s say you have invited everyone over for the big game on Sunday (Superbowl 49) but you don’t have a big screen TV. Whoops! That sucks. Time to go shopping. Here’s the rub: which one to get? There are so many to chose from and only a little time to make the decision. Here is what you do:
1- call your best friends to help you out
2- make a list of all neighboring electronics stores
3- Go shopping!
OK, that sounds like a good plan but it will take an enormous amount of time to perform this task all together and more importantly your Lada only seats 4 comfortably and you are 8 buddies.
As you are a new research scientist (see here for your story) and you have already studied the challenges of assessing agreement (see here for a refresher) you know that it is best for all raters to assess the same items of interest. This is called a fully crossed design. So in this case you and all of your friends will assess all the TVs of interest. You will then make a decision based on the ratings. Often, it is of interest to know and to quantify the degree of agreement between the raters – your friends in this case. This assessment is the inter-rater reliability (IRR).
As a quick recap,
Observed Scores = True Score + Measurement Error
Reliability = Var(True Score)/ Var(True Score) + Var(Measurement Error)
Fully crossed designs allow you to assess and control for any systematic bias between raters at the cost of an increase in the number of assessments made.
The problem today is that you want to minimize the number of assessments made in order to save time and keep your buddies happy. What to do? Well, you will simply perform a study where different items will be rated by different subsets of raters. This is a “not fully crossed” design!
However, you must be aware that with this type of design you are at risk of underestimating the true reliability and therefore must, therefore, perform alternative statistics.
I will not go into statistical detail (today anyway!) but if you are interested have a peek here. The purpose of today’s post was simply to bring to your attention that you need to be very careful when assessing agreement between raters when NOT performing a fully crossed design. The good news is that there is a way to estimate reliability when you are not able to have all raters assess all the same subjects.
Now you can have small groups of friends who can share the task of assessing TVs. This will result in less assessments, less time to complete the study, and – most importantly – less use of your precious Lada!
Your main concern, as you are the one to make the purchase of the TV, is still: can you trust your friends assessment score of TVs you did not see? But now you have a way to determine if you and your friends are on the same page!
Maybe this will avoid you and your friends having to Agree to Disagree as did Will Ferrell in Anchorman…
Listen to an unreleased early song by Katy Perry Agree to Disagree, enjoy the Superbowl (and Katy Perry) on Sunday and…
…I’ll see you in the blogosphere!
Classic Seth Rogan movie. Today we will be talking about good neighbors as a followup to my first post “What cluster Are You From?“. If you want to learn a little about bad neighbors watch the trailer to the movie Neighbors.
So let’s say you are working with a large amount of data that contains many, many variables of interest. In this situation you are most likely working with a multidimensional model. Multivariate analysis will help you make sense of multidimensional space and is simply defined as a situation when your analysis incorporates more than 1 dependent variable (AKA response or outcome variable).
*** Stats jargon warning***
Mulitvariate analysis can include analysis of data covariance structures to better understand or reduce data dimensions (PCA, Factor Analysis, Correspondence Analysis) or the assignment of observations to groups using a unsupervised methodology (Cluster Analysis) or a supervised methodology (K Nearest Neighbor or K-NN). We will be talking about the later today.
*** Stats-reduced safe return here***
Classification is simply the assignment of previously unseen entities (objects such as records) to a class (or category) as accurately as possible. In our case, you are fortunate to have a training set of entities or objects that have already been labelled or classified and so this methodology is termed “supervised”. Cluster analysis is unsupervised learning and we will talk more about this in a later post.
Let’s say for example you have made a list of all of your friends and labeled each one as “Super Cool”, “Cool”, or “Not cool”. How did you decide? You probably have a bunch of attributes or factors that you considered. If you have many, many attributes this process could be daunting. This is where k nearest neighbor or K-NN comes in. It considers the most similar other items in terms of their attributes, looks at their labels, and gives the unassigned object the majority vote!
This is how it basically works:
1- Defines similarity (or closeness) and then, for a given object, measures how similar are all the labelled objects from your training set. These become the neighbors who each get a vote.
2- Decides on how many neighbors get a vote. This is the k in k-NN.
3- Tallies the votes and voila – a new label!
All of this is fun but will be made much easier using the k-NN algorithm and your trusty computer!
So, now you have an idea about supervised learning technique that will allow you to work with a multidimensional data set. Cool.
Listen to Frank Sinatra‘s The Girl Next Door to decompress and I’ll see you in the blogosphere…
Just got back from the RSNA! Wow what a big conference – 56,000 people this year. McCormick place in Chicago, Illinois (where the conference is held) feels like an airport it is so big.
Love Chicago. Great city.
Of course, I had the pleasure of attending a bunch of great presentations and today I will introduce you to one of them. Tina Binesh Marvasti (say that 7 times fast!) presented on the topic of Haptoglobin. No, not Hobgoblin (not sure who that is? See here) or his infamous green predecessor (see here).
So, what is Haptoglobin you ask? It is a serum protein that binds free hemoglobin – resulting from the breakdown of red blood cells – and functions to prevent loss of iron (contained in the heme group) through the kidneys and to protect tissues from the highly reactive heme groups. Essentially a housekeeping protein that helps to recycle hemoglobin as part of the red blood cell life cycle. Now what if your ability to clean-up free hemoglobin was impaired? Well, quite simply you would be putting at risk those sensitive tissues that come into contact with free hemoglobin.
One important example of this is vessel walls affected by atheroma (AKA plaque). Sometimes these atheroma can bleed (called intraplaque hemorrhage or IPH) which worsens the whole situation. Typically, your body responds by sending the clean-up crew including the Hobgoblin (or haptoglobin, I always get these two confused).
When people have the recessive genotype (Hp 2-2) of the Hp gene they produce less haptoglobin and therefore are at increased risk of damage from free hemoglobin (or more specifically the heme groups).
Tina and friends hypothesized the following:
And she found that having the recessive Hp2-2 genotype was associated with a higher prevalence of IPH in a group of 80 patients (average age of 73 yrs). She also found that the IPH volume of Hp2-2 patients worsened over time.
So what is the take home? The Haptoglobin genotype is associated with IPH which is a biomarker of high risk vascular disease and could identify populations at higher risk of developing cardiovascular events.
Now for the fun part (see the rules here), using Haptoglobin in a sentence by the end of the day:
Serious: Hey Bob, did you know that a recessive haptoglobin genetype may contribute to an increased risk of cerebrovascular disease?
Less serious: My GP suggested that based on my recessive hobgoblin genotype I should consider a healthier lifestyle. Funny, I always figured Doc Ock to be the one to watch for…
OK, watch the Spider-man 2 trailer to decompress and I’ll see you in the blogosphere…
This week and I had the pleasure of presenting to the Division of Rheumatology Research Rounds – University of Toronto. They were a fantastic audience who asked questions and appeared to be very engaged. Shout out to the Rheumatology gang!
So, I was asked to talk about a statistical methodology called Cluster Analysis. I thought I would start a short series on the topic for you guys. Don’t worry I will keep the stats to a minimum as I always do!
Complex information can always be best recognized as patterns. The first picture below on the left certainly helps you realize that it is not a simple task to know someone at a glance.
Now, I guess it doesn’t help that many of you have never met me either! However, you can appreciate that things get a little easier when the same portrait is presented in the usual manner – upright!
This is an interesting example where the information is identical, however, our ability to intuitively recognize a pattern (me!) appears to be restricted to situations that we are familiar with.
This intuition often fails miserably when abstract magnitudes (numbers!) are involved. I am certain most of us can relate to that.
The good news is that with the advent of crazy powerful personal computers we can benefit from complex and resource intensive mathematical procedures to help us make sense of large scary looking data sets.
So, when would you use this kind of methodology you ask? I’ll tell you…
1 – Detection of subgroups/ clusters of entities (ie: items, subjects, users…) within your data set.
2 – Discovery of useful, possibly unexpected, patterns in data.
OK, time for some homework. Try to think of times when you could apply this kind of analysis.
I’ll start you off with an example that you can relate to. Every time you go to YouTube and search for your favorite movie trailer you get a long list of other items on the right that YouTube thinks may be of interest to you. How do you think they do that? By taking into account things like keywords, popularity, and user browser history (and many, many more variables) and using cluster analysis of course! You and your interests belong to a cluster. Cool!
In this series, we will delve into this fun world of working with patterns in data.
Now that you have peace of mind, listen to The Grapes of Wrath…
See you in the blogosphere,