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Lee Radigan: A Reflection on my (6th) Year as an Undergrad at the University of Toronto

My name is Lee Radigan and I am a non-degree student pursuing admittance to the Biostatistics Master’s program at the Dalla Lana School of Public Health.  After returning for my 6th year studying statistics at The University of Toronto, I thought that this was a perfect time to reflect on my progress.
Since September, I have been working under Dr. Pascal Tyrrell’s guidance on a project aimed at helping the Department of Medical Imaging report agreement in their research.  To do this, I created a flow chart to help guide the reader towards the proper method of agreement.  Along with this, I conducted a simulation looking at a specific question pertaining to the Department.
Initially, I was tasked with combing through various papers on the theory of agreement and making sense of all the different published work that was out there.  There are many different approaches and different ways of looking at reporting agreement, so it was quite difficult to figure out when and where to properly use every single approach.  After reading and re-reading each paper, as well as consulting the MiData team, I started to develop a thorough understanding of what agreement was, why it is important to report it, and how to go about reporting it appropriately.
Next, a flow chart was required to summarize what I had learned from the literature.  This was not an easy task, because it forced me to dig really deep and make sure that every node in my flow chart was well thought out and appropriate.  After many iterations and adjustments, I created a detailed chart that walks the reader from their initial research question up to the required agreement statistic.
My final task was to conduct a simulation that would test the question: Can a group of less experienced student raters be as accurate as a smaller set of more experience expert raters?  And if so, how many students?  And under what conditions?  This was a very fun and informative task for me as I was able to conduct my first simulation.  During this experience, my biggest difficulty was justifying my choices of parameters within the simulation.  When conducting a simulation you have freedom to choose how it is going to work, but you must be careful to be able to back up each and every parameter choice.  The simulation ended up showing that: the larger the disparity between the rating errors of the student and expert raters, the more students it takes to match the accuracy of the experts, confirming my intuition.
There are many things that I wish to expand on with respect to my project in future.  I want to create a user friendly app that will be even easier and more compact than my flow chart.  Additionally, I want to try to get my paper published.  To do this I will need to look further into my simulation and consider a more broad range of student/expert scenarios that likely will occur in practice.  I will also need to further refine my definitions and understanding of each concept of agreement.
This year has truly been the best of my life and I can largely attribute that to Dr. Pascal and the MiData team.  I look forward to contributing to Medical Imaging research and to many more learning experiences.
Time to enjoy the summer as I embark on yet another exciting experience as a student Statistical Analyst at the CAMH Nicotine Dependence Clinic as a summer placement!
Lee Radigan
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ROP299 2017-2018: A Medical Imaging Journey from a Humanities Perspective

My name is Samantha Santoro, and I am completing my second year in the English and Biology majors at the University of Toronto, St. George. A rather unconventional combination, when reviewing past students of Dr. Tyrrell’s lab. I was a 2017-2018 Research Opportunity Program (ROP) student in Dr. Pascal Tyrrell’s lab, and my work chiefly consisted of evaluating the internal vessel wall volumes of carotid arteries in a particular cohort of patients provided by the ongoing prospective CAIN study. My ROP was in the field of Medical Imaging. I am the co-president of the student club known as Watsi, with the main chapter based in San Francisco. I am also a special contributor to the Rare Disease Review, along with volunteering at an amalgamation of charity walks and fundraisers.

My ROP project was a turbulent experience – although that word is typically associated with a negative connotation, I regard my ROP299Y1 as one of the most humbling, interesting, and educative experiences that I have had thus far – most definitely not negative. However, to say everything went smoothly would be discrediting the lessons I learned from when things were not idyllic and smooth. My project, as aforementioned, statistically analyzed data provided by patients part of the CAIN study (an analysis that could not have existed without Dr. Tyrrell’s generous and unwavering support). My study determined that patients who were found to have IPH, or what is known as intraplaque hemorrhage, when I analyzed their MRIs, were also found to have increased vessel wall volume. This conclusion is incredibly significant, as IPH is a surrogate marker for atherosclerosis and could potentially be an indicator for patients at risk of future cerebrovascular events (namely, ischemic stroke). As strokes are currently the number three killer in the U.S and Canada alone, and heart disease number one, having a potential indicator for patients at risk of stroke would greatly benefit clinicians in their practice, as well as patients themselves.

As aforementioned, studies similar to my own are currently underway by the Canadian Atherosclerosis Imaging Network, furthering the important research in this field. The VBIRG (Vascular Biology Imaging Research Group) was the lab in which I primarily worked throughout the course of my ROP, at Sunnybrook Hospital. Moreover, I also worked on systematic reviews and reports outside of the focus of my project, in the fields of medical ethics and AI in the radiology workplace – both of which were opportunities provided to me by Dr. Tyrrell, and both of which were incredibly valuable experiences, allowing for me to broaden my knowledge of certain areas of medicine and science that are developing and expanding.

Although my project was littered with its own respective difficulties – a substantial number of drafts throughout each step of the program (more than I had ever made, even being an English student); a reluctant, but later fulfilling, acquaintanceship with the post-processing software VesselMass; and several late nights learning about the field of statistics – it is in light of these difficulties, and at present having overcome them throughout my ROP, that I remember Dr. Paul Kalanithi’s words in his memoir When Breath Becomes Air: “It occurred to me that my relationship with statistics changed as soon as I became one”. He, too, had studied Biology and English. I may not have played a lead role in the statistics I had been working with, but I can now say that understanding what they meant and how they were formulated has generated a deep respect in me for the field of statistics.

My poster was on display at the 2018 Research Opportunity Undergraduate Fair. Special thanks to Mariam Afshin, my supervisor at Sunnybrook Hospital; Bowen Zhang, for answering each question I had while at Sunnybrook; John, and the rest of the lab team; and Dr. Pascal Tyrrell, for answering my email last February and holding my interview on the same day as my Chemistry exam. Never before had I met such an – in a word – outstanding professor, and I dare say that I will never meet one like him throughout the rest of my academic journey.

Samantha Santoro

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MiVIP meets AI…

Well, I think it was inevitable. My data science lab has slowly crossed over to the dark side into the world of  Machine Learning and Artificial Intelligence.


Let me apologize for being MIA for so long. Life has been pretty hectic these past months as I have been building the MiDATA program here in the Department of Medical Imaging at the University of Toronto. The good news is that the MiVIP program will now be inviting students to participate in machine learning and artificial intelligence in medical image research.


This summer will include the launch our our MiStats+ML program where we will have students from the department of statistical sciences, computer sciences, and life sciences all work together on ML/AI projects in the MiDATA lab.


Stay tuned as we ramp up and get back to some our previous threads like MiWORD of the day…




See you in the blogosphere,




Pascal

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Lessons Along the Way

https://betakit.com/startupcfo-explains-the-long-windy-road-to-a-closed-funding-round/
 
 
With summer almost here, it’s a good time to reflect on lessons learned from the academic year gone by. Since September, I’ve been working under Dr. Pascal Tyrrell’s supervision on a systematic review (SR) project investigating sample size determination methods (SSDMs) in machine learning (ML) applied to medical imaging. Shout out to the Department of Statistical Sciences where I completed my independent studies course! Here, I share important lessons I learned in the hopes that they may resonate with you.
 
Despite being a stats student (as you know from my previous posts!), I was initially new to ML and confronted with the task of critically reviewing theoretically-dense primary articles. I came to appreciate the first step was to develop a solid background – starting from high-level YouTube videos and lessons on DataCamp, to reading ML blogs and
review articles – all until I was confident enough to evaluate articles on my own. For me, the key to learning a complex subject was to build on foundational concepts and keep things as clear as possible. As Einstein once said: “If you can’t explain it simply, you don’t understand it well enough”.
 
Next, it was time to conduct a systematic search. The University of Toronto library staff were especially helpful at guiding me in use of OVID Medline and Embase, databases with methodical search procedures and a careful search syntax relying on various operators. To be thorough, we also sent a request out to the rest of our research team, who hand-searched through their own stash of literature. Along the way, we garnered support from the university, successfully receiving the Undergraduate Research
Fund grant. The lessons for me here? The importance of seeking expert help where appropriate, and that being resourceful can pay off (literally)! Finally, I valued our strong team culture, without which none of this would have been possible.
 
While working on the SR, I also conducted a subsampling experiment using a medical imaging dataset, testing the effect of class imbalance on a classifier’s performance. Hands-on/practical experiences are critical in developing a more nuanced understanding of subject material – in my case, an understanding that translated to my SR.
 
So now you are probably wondering about the results! The subsampling experiment helped us develop a model for the deleterious effect of class imbalance on classification accuracy and demonstrated that this effect was sensitive to total sample size. Meanwhile in our SR, we observed great variability in SSDMs and model assessment measures, calling for the need to standardize reporting practices.
 
That was a whirlwind recap of the year and I hope some of the lessons I learned resonate with you!
 
See you in the
blogosphere,
 
Indranil Balki
 
A special thanks to Dr. Pascal Tyrrell, as well as Dr.
Afsaneh Amirabadi & Team

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“Hour of Code… Part Deux” at the University of Toronto

What hour of code? What code?

The Hour of Code is a global movement reaching tens of millions of students in 180+ countries. The purpose is to to demystify “code”, to show that anybody can learn the basics, and to broaden participation in the field of computer science. Please see here for more info.

I belong to Code.org a non-profit dedicated to expanding access to computer science, and increasing participation by women and underrepresented students of color.

On Thursday, December 7th, 2017 at 9:30AM we will be hosting our second 
 
“MiDATA Hour of Code at UofT“

What is the purpose of this event?

To engage young minds and help them see the exciting possibilities computer programming can offer them in their future careers.

Who is coming?

Over 100 students (grades 7 to 11) from Central Toronto Academy (TDSB), St Francis Assisi and St Ignatius of Loyola (TCDSB).

Who will be engaging them (so far)?

University of Toronto:
MiDATA (Data Science unit from the Department of Medical Imaging)
*Prof Pascal Tyrrell – Director, Data Science

*Prof Anne Martel – Medical Biophysics (Machine Learning)

*Daniel Eftekhari – MSc student (medical image machine learning)

*Dr Mariam Afshin – Research Physicist

*Rasha Mahmood – VBIRG

Department of Medical Imaging
*Dr Alan Moody – Radiologist and Chair of the Department

Department of Statistical Sciences
*Prof Paul Corey – Senior Biostatistician
*Chris Meaney – Biostatistician

Department of Computer Science
*Prof Steve Engels

 

Industry:
IBM Watson Health and Merge Healthcare

   *George Gorthy – Senior Sales Consultant IBM Watson Health Imaging
    *Marwan Sati – VP of Development, Clinical Speciality Solutions, Merge Healthcare
*Aditya  Sriram – Developer, IBM Watson Health Imaging

Microsoft (Big Data and Analytics)
* Sage Franch – Microsoft Canada

SAS Canada
*Mark Morreale – Lead, Academic Program

Community:
Ladies Learning Code – Yaa Otchere

 

Industry Support:
Google

Tyrrell lab students and Computer Science undergraduates will be acting as ambassadors.

Where and when will the event be held…exactly?

University College Media Room (RM 140 and RM148) from 9:30 AM to 1 PM

University College, University of Toronto
15 King’s College Circle
Toronto, Ontario

 

Interested in participating? Contact me at pascal.tyrrell@utoronto.ca!

See you all there,

Pascal Tyrrell

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The Ram-ifications of Risk




In the final installment of this series, I want to discuss how we can use the Ratios of Risk in a clinical context. To recap, we previously discussed an absolute measure of risk difference (appropriately called the risk difference or RD), as well as a relative measure of risk difference (relative risk or RR). 

To see how we can apply these risks, let’s tweak our original example. Let’s assume that smartphone thumb could potentially lead to loss of thumb function (not really, don’t worry!). Let’s also suppose that surgery is a possible treatment for smartphone thumb, and the following results were obtained after a trial.
Surgery
No Surgery (control)
Totals
Retained 
thumb function
7
6
13
Lost thumb function
3
4
7
Totals
10
10
20


The big question is: how good an option is surgery?
Let’s calculate the RD (note that the “risk” here is of losing thumb function): 4/10 – 3/10= 0.1

In other words, there is a 10% greater risk of loosing thumb function if you did not have the surgery. Based on this information alone (or by calculating the RR and OR), we might be quick to conclude that surgery is a great intervention.

But before we do that, let’s calculate another statistic, which will prove to be very useful: it’s called the number needed to treat (or NNT), and is given by 1/RD. The NNT is the number of patients that must be treated for 1 additional patient to derive some benefit (retain an intact and functioning thumb). In our case, NNT = 1/0.1 = 10. So, in order save 1 patient from loosing his thumb, another 9 will have had to undergo surgery with no apparent benefit. As you can see, the NNT sheds a very humbling light on our intervention. The ideal NNT is equal to 1. Beyond that, we must keep in mind that the additional patients undergoing the treatment have been exposed to all the negative side effects, without the intended benefit.

Throughout this series we discussed the meaning of risk, how it can be used for comparison (the various ratios of risk), and finally its application in a clinical setting (the ramifications of risk). After all these posts, smartphone thumb may have started to seem like a very real threat. But I think you should be fine…. as long as you know the risks!


So what’s up with the Dodge Ram ad (I am actually a F150 guy myself)? Well I just thought it went well with ramifications of risk. Cheesy I know. But who knows maybe it will help you to remember…


See you in the blogosphere,




Indranil Balki and Pascal Tyrrell
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The Ratios of Risk With a Zip!

With summer here, I think it’s time that we continued our discussion on risk. No, I’m not talking about the dangers of your favourite adventure sport… but then I just got back from a trip to Costa Rica as part of the Canadian delegation for the Gateway to Trade  project and I, of course, went ziplining! Awesome. 


It’s been a few months so I recommend catching up on Risky Business – Is it all Relative? and Happy New Year and Enjoy some AR&R. 


Before we get started I want to introduce a student of mine, Indranil Balki, who has agreed to come aboard and help me write this blog. Life is busy for me and I feel bad that I can’t post as much as I would like. So look to find Indranil signing off with me at the bottom of these posts.

When we last left off, we were interested in the idea of comparing risks – how many times more likely is it for a smartphone user to develop smartphone thumb than for a non-smartphone user?


We touched on one way to compare the two groups in the last post, by finding the risk difference A/(A+B) – C/(C+D). But to answer our question, we need a ratio. It turns out that this is called (helpfully) a risk ratio, or relative risk (RR). The RR is given by A/(A+B) divided by C/(C+D). A RR basically compares the risk in the exposed (smartphone owners) and unexposed conditions. 


For example, let’s say that 20% of smartphone users developed smartphone thumb and 10% of non-smartphone users developed smartphone thump. Then the RR is 2, meaning that you are twice as likely to get the disease if you own a smartphone than if you don’t.


Well wasn’t that an elegant way to compare the risks between two groups? As you might have guessed, a RR of 1 shows no difference in risk between the groups, and an RR > 2 or <0.5 is usually considered statistically significant.


So let’s say you meet a friend at school and he finally reveals that he has smartphone thumb (don’t worry, it’s not contagious – I think!). Since you’ve been following this blog, you immediately wonder, what’s the probability that he has a smartphone? To answer this reverse question, it turns out that you technically need what is called an odds ratio (OR). The OR is comparable to the RR if the prevalence of the disease is low. But it is a slightly different way to compare risks.


Given that you have smartphone thumb, the odds that you had the exposure are given by the probability that you had a smartphone (A/A+C), divided by the probably that you didn’t have it (C/A+C). This simplifies to A/C. Similarly, the odds of exposure in those without smartphone thumb is B/D. The odds ratio then, is calculated by dividing these 2 odds. OR = A/C ÷ B/D. An OR of, say 3, tells us that there’s a 3 times higher chance your friend has a smartphone than he doesn’t.


Well, that might have been a tough post! Take some time to think about it, have a gander at some ziplining in Costa Rica here and don’t spend too much time on your smartphone…



See you in the blogosphere,




Indranil Balki and Pascal Tyrrell





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A Medical Ethics ROP Journey with Jayun Bae

Jayun Bae – ROP299Y 2016-17
My name is Jayun Bae and I am completing my second year in the Neuroscience and Bioethics majors at the University of Toronto, St. George. I was a 2016-2017 Research Opportunity Program (ROP) student in Dr. Pascal Tyrrell’s lab, working on a study that investigated the ethics of sharing patient data with private organizations (see my timeline above). I am a member of the Hart House Debating Club and an events associate for the Life Science Student Network. 
                                                               
My ROP project was necessitated by the partnership proposed by the Medical image Networking Enterprise (MiNE) that would establish a data-sharing relationship between public and private sector organizations. The ethical concerns with the partnership involved patient consent, privacy, and financial gain – but there were also issues that I
uncovered throughout the project. It quickly became clear that the answers could not be found through an examination of precedence or legal documents, because many of the research actions that would take place (specifically involving private organizations) fell in the grey area between what was legal and what was ethical. For example, the Personal Information Protection and Electronic Documents Act (PIPEDA) and Personal Health Information Protection Act (PHIPA) are two guidelines for organizations to follow when handling patient data – but neither are able to clearly and positively dictate how this partnership should operate.
Therefore, I developed a study that would seek expert opinions through the administration of a survey. I conducted interviews at Sunnybrook Health Sciences Centre and the University of Toronto and performed qualitative data analysis. My ROP project was presented at the ROP Poster Fair and the Victoria College Research Day events. The ROP was an extremely valuable experience in gaining research skills, and I’m grateful to
Dr. Tyrrell for the guidance and mentorship. The project is not yet completed, so I am looking forward to continuing the study beyond the scope of the ROP.   Please have a look at my poster from the 2017 ROP Research Day below:
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MRI, Statistics, Carotid Arteries, and 1000 Cups of Coffee with George Wang

GeorgeWang – ROP299Y 2016-17
I’m George. I have recently completed my 2nd year undergrad at the University of Toronto studying physiology and physics. In the fall-winter term of 2016-17 I had the privilege to work in Pascal’s group, looking into carotid artery MRI and using the volume of the carotid artery vessel wall as a marker for atherosclerosis. Having an acquired interest in medical imaging and a previous summer position working with PET, I saw this as an excellent opportunity to expand my knowledge of the field while having the chance to be exposed to clinical research methods. Above is my account of how the year went in a nutshell.
 
Have a look at my poster from the ROP Research Day below…
 
 
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Happy New Year and Enjoy Some AR&R…

Or Attributable Risk Reduction…

First let me wish you all a fantastic New Year! Last year was crazy and I think this year is looking like it will be more of the same…


So in a previous post called Risky Business: Is It All Relative? we started talking about risk. We agreed that in lay terms a risk is generally associated with a bad event. However, a risk in statistical terms refers simply to the probability (usually statistical probability value between 0 and 1) that an event will occur, whether it be a good or a bad event.


We also defined the risk of “smartphone thumb” as the number of new cases of smartphone thumb (the outcome) in a given period of time divided by the total number of people who own a smartphone (the exposure) and are at risk. This was called the cumulative incidence or absolute risk. Now what if we wanted to compare this risk to people who did not receive a smartphone for their birthday or Christmas for that matter? Let’s look at the results in a contingency table:


So, the absolute risk of smartphone thumb is A/(A+B) and similarly for those sad people without a smartphone their risk is C/(C+D). Now your chances of developing smartphone thumb are not necessarily 0 as maybe you are an avid gamer and play a little too much Xbox on the weekends. The reduction in risk can be expressed as the risk difference (also called the attributable risk reduction – ARR) and can be calculated as RD = A/(A+B) – C/(C+D). We can also estimate the proportion of cases of smartphone thumb among smartphone users that can be attributed to smartphone use by calculating the attributable risk percent: [RD/ A/(A+B)] x 100.


Let’s say 20% of smartphone users develop smartphone thumb whereas only 10% or non-smartphone users do. The RD is then equal to 10% (0.2 – 0.1 *100). The reduction in the chances of experiencing smartphone thumb who own a smartphone is the AR% which in this case is 50% (0.1/0.2*100).

That was easy. What’s next? Well, what if we want to know how many times more likely is it for a smartphone user to develop smartphone thumb than for a non-smartphone user? Let’s talk about that next post.






For now, decompress listening to “Under my Thumb” by the Rolling Stones. Classic…




See you in the blogosphere,


Pascal Tyrrell