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Breast Cancer Recurrence Prediction with Machine Learning

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Mohammad Owrang looks at the significance of the non-clinical prognostic factors of age, ethnicity, and marital status in finding the prognosis for breast cancer patients

Image courtesy of interviewee. March 25, 2024

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The presentation discusses the importance of medical prognostication, particularly in relation to breast cancer diagnosis and recurrence. The speaker acknowledges the contributions of graduate students to the research and emphasizes the need for accurate predictions regarding complications and survival rates in breast cancer patients.

The speaker outlines key factors affecting breast cancer outcomes, including clinical information (tumor size, grade, lymph node involvement) and non-clinical factors (patient age, race, genetics). The significance of predicting recurrence is highlighted due to breast cancer's high prevalence and associated mortality.

The presentation details the classification of recurrence into local, regional, and distant categories, explaining the various symptoms and implications of each. The research aims to explore new prediction techniques, using data mining and machine learning to identify risk factors and improve prognostic models.

The methodology includes understanding the data attributes, conducting feature selection, and applying several predictive modeling techniques (e.g., decision trees, support vector machines). The final model demonstrates decision trees as the most accurate for predicting breast cancer recurrence, though the speaker notes challenges such as inconsistent data collection across different healthcare facilities and the need for more comprehensive datasets.

Future directions suggest the necessity of standardizing data collection and increasing patient sample sizes to enhance the validity of predictive models in clinical settings. The presentation concludes with a call for ongoing research and collaboration to improve breast cancer treatment and management strategies.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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I just wanna in advance, I want to thank a couple

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of our graduate student, master student who graduated

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and then participated in this, paper

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and presentation, Farba, Ari and Ginger Shores.

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Basically, as far

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as the medical prognostication is concerned, it,

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it is really very important part of the medicine these days.

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Basically, it is, it is the science of

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estimating the complication, you know, in, in,

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in the disease and the recurrence of the disease,

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and be able to make a prediction as you know,

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how long the patient can survive, basically.

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So it is, I mean, main part of the, treatment plan

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because they have to know, the doctors have to know that,

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you know, how severe is the breast cancer

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for a particular patient and able to make it a plan.

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There are several factors affecting, you know, the,

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the breast cancer basically, as far

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as the diagnosis is concerned.

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Uh, clinical information such as tumor size, tumor grade,

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number of lymph nodes that are very important.

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Some of them are nonclinical, like age of the patient, race

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and ethnicity of the patient,

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basically sometimes genetic information.

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I mean, it has something to do

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with the family history and that kind of thing.

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So because of the, you know, high prevalence

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and mortality of, of breast cancer among women,

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basically it is very important to be able to predict

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as when, breast cancer can come back

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after the treatment.

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It, it, it will come back for some people.

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So we wanna know, define

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or explore new techniques in order

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to be able to predict that.

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And, and once we have that, then, then people, you know,

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the doctors can make a plan to prevent that treatment

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that are appropriate in order to,

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prevent such thing to recur.

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Again, basically the, the main thing, a again,

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let me briefly explain that.

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Once the breast cancer is diagnosed, then they make a plan

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for the treatment based on, you know, the thing

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that they discovered that, so they make the treatment,

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treatment could be radiation, chemotherapy,

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basically hormone therapy,

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and in order to remove the cancer cell,

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but sometimes some

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of them still stay in their skin basically.

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And, and then over the years,

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or you know, again months, they start to grow

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and then, you know, multiply and,

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and then suddenly they come back.

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That's what their breast cancer recurrence is.

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Now when they come back,

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there are different ways then they can come back.

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If they come back to the same place that they know,

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they initial, like, so

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where breast cancer was diagnosed,

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we call them local breast cancer recurrence.

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If they all in the, sort,

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near lymph nodes basically, and,

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and then they try to infect them basically then they call

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them regional, you know, sort of a thing.

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Uh, breast cancer recurrence.

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And of course in worsts case, if they start to spread

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to other part of the body like a lung and kidney

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and then bone basically, and,

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and then they call it distant, basically distant.

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A lot of time they occur for the late breast cancer, like

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after 10 years, you know, that kind of thing.

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And of course, the doctors have some sort

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of a guideline in terms of to recognize which one is which,

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you know, so the way, local one, you know, you,

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you have, inflammatory breast,

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basically redness in there and,

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and then sort of where, for the regional, you know, you,

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you basically have a lump basically on your neck,

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over there or, or sort of way even on the breast,

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basically, you can feel it.

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The distant one is really very bad.

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You know, you have shortness of breaths, basically you,

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you lose appetite, you know, basically to,

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eat anything and then you lose weight

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and then you get, sometimes even in most cases

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you get seizure basically.

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So, it becomes very critical.

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A lot of, things nowadays, people are looking at it,

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the early breast cancer, you know, so of recurrence

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or late breast cancer recurrence later on, I'll mention that

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as part of the, you know, existing, you know, work

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that people are doing that there are several work as far

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as the breast cancer diagnosis.

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A lot of things are about the diagnosis.

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We are pretty good with that.

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I mean, the existing work, the physician,

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they all know how to do that.

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Basically, as far as the breast cancer recurrence are,

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are concerned, basically there are several world, some

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of them, they are looking at a small amount

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of the data basically.

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And, and then trying,

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to model the breast cancer prediction to see that,

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what are, you know, who is at risk.

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Basically there are existing work that, try

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to find what are the factors that

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influence the breast cancer recurrence factors,

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for instance, ethnicity race or,

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or tumor size, tumor grade or, or your eating habit.

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You know, lots of other things that, you know,

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they wanna see that what really causes, you know,

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the breast cancer to come back, basically.

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So they are, you know, so we're trying to find factors

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that contribute to the breast cancer recurrence.

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There are several work that study the data mining

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and machine learning techniques to compare them to see that,

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like which one is better moderate, basically,

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which one can predict, you know,

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the breast cancer recurrence basically, you know, and

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therefore that would be a good one, you know, for them

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to sort of recommend.

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Recent years people are looking at two other major thing,

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early breast cancer recurrence

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and late breast cancer recurrence early.

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They are talking about few months, one year,

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two years to three years.

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Most of them, they, you know, they say that, you know,

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after the treatment, all the things,

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and then, you know, the,

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the breast cancer come back relapse, I mean the,

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relapse, you know, survival free basically ends

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and then patient is start to show the symptom

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for the breast cancer to coming back basically.

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And that way basically, you know, they,

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they make a plan in terms of what to do, the treatment.

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Sometimes this thing happen to younger patient,

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basically they could happen that generally, you know,

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lymph node, the lymph node

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or the protective mechanism if they are infected,

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you know, that's what it is.

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You know, the breast cancer is a good chance to come back.

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The other, things, other, reports

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or looking at the late breast cancer, late breast cancer,

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we are talking about after 10 years,

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after 20 years, I guess for the,

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for a long time people always thought

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that the physician thought

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that breast cancer can come back within five years.

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If it doesn't come back within five years, then,

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then you are fine, but not is,

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this is not the case these days.

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I mean, after 20 years even, they have been incidents

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that breast cancer can come back and basically,

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and those are the one that,

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really you are talking about,

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the tumor size is very big, like 20 millimeters, that

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that's a huge, you know, shoulder weight, size

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of the cancer basically.

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So those are the ones that a pretty good chance, you know,

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after 15 years, 20 years, they come back.

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So that, and you can see that it becomes really important

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for the, you know, for the people for, you know,

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medicine to understand that when this is gonna come back so

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that they can hopefully make a proper plan for that

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in our study, again, pretty much like, you know, other,

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existing work, basically, if you are trying to predict

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the breast cancer recurrence, you know,

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what are the factors affecting that?

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Basically, basically we have

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three steps, at least we, we follow the main steps in order

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to, you know, accomplish that.

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Basically in, in this study, first we wanted

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to understand the data.

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I mean, it's fundamental, you know, to understand

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what are the attributes that we have, what are the features

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that we have and,

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and how they are related to each other, basically.

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So we, we generally, did a little bit

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of data visualization, we call that,

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which basically means you try to look at it as how these,

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how age got something to do

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with the breast cancer recurrence, younger age,

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older age, middle age.

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Basically. You look at that, you look at it the tumor size,

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you know how it is related to the breast cancer recurrence,

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how the, you know, number of lymph nodes are related to

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that basically.

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And, and, and then get a better feeling as, you know, what,

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what are the data we have?

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And of course, it's always in, in, data, medical data.

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There are something that are missing

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that you have to take care of them.

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There are, missing attribute, missing values

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that you have to eliminate them because you cannot use them.

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There are, you know, missing information there.

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Once you have the, the data basically looked at it

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and understand a little bit about, you know,

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how these things are related to each other,

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then we use some techniques called feature

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or attribute selection.

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Uh, basically it means we wanna look at it among the 32, 35

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or four 40, you know, attributes that we have.

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What are the one that are more important?

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What are the one, I mean, what are the things

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that are more important

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or lead to having a breast cancer recurrence, basically?

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Uh, and, and, and therefore,

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we use different techniques.

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For instance, recursive, you know, removal

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of the, you know, feature.

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It means every time we look at it to see

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that if something is not significant, we remove them

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and then come back again, do one more time, look at it

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and say that, okay, you know, based on the data

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that we have, another feature is not that important.

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We remove that. It could be anything.

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It could be age, it could be, you know, sort

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of a tumor size, whatever, if they are not that important.

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So gradually we wanna have some of them, like six of them,

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10 of them, that we think that they are gonna be important.

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So that's recursive, you know, technique that we use it.

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There is another technique called Pearson, you know,

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so the way, a scheme in order to the, so we remove that.

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Basically what it does, it looks at it, the features to see

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how they are related to each other,

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how they are correlating each other.

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In other words, two pieces

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of the attributes may be really close to each other, and

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therefore you don't need both of them.

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You can eliminate one of them

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because the other one is not going to give you any,

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any chance really.

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So that's how you sort of reduce some

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of the, you know, attributes.

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And, and then from that makes it a smaller,

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because a smaller set of

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good attributes give you a better accuracy when you have too

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many, too many attributes there

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that participate in breast cancer,

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then we call them overfeeding.

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And then you really, you don't know which one is, you know,

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more important, that kind of thing.

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But few of them really become a focused one, and

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therefore it gives a better

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accuracy when we sort of create our model.

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So you do that, there are, again, other technique,

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decision tree, you know, that kind of thing.

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Sometime they use that in order to sort of,

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eliminate some of the irrelevant

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or less important, you know, sort of thing.

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Even people can look at it.

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I mean, scientists can look at it,

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the attributes and, and make a decision.

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For instance, I look at it patient ID for instance,

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patient Id got nothing to do, you know,

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with the breast cancer recurrence or,

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or most probably the address also is not that important.

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You know, it may not, basically,

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although you know, where you work could be, you know,

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has some significance in there and, and, or,

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or different techniques that, for instance, you use in order

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to calculate something or,

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or to see that what is the size of the,

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for instance, the tumor size.

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Well, that's not important.

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I mean, the, the size of the tumor is important, not

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how you get that basically.

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So you could eliminate them

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and gradually, hopefully you will, you'll be able to,

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to remove some of the thing

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and have a good set of the things.

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So once you have that, we did all these different five,

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six different techniques,

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and then we took a union of all of them.

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Union means combine them to see that

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what are the attributes, what are the features that all

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of these techniques have in common?

12:33.905 --> 12:37.125
All of them say for instance, you know,

12:37.265 --> 12:39.525
is important, estrogen, that's a receptor.

12:39.955 --> 12:42.605
They all said that the tumor size is important and

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therefore we know that, okay, this is important

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because it's not like one technique said it is important.

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Five of them, they said too, are important.

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Then we'll take it as it important.

12:52.235 --> 12:55.685
Therefore, once we have that, we sort of, out

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of the 30, 32 attributes, now we have like a five, six

12:59.285 --> 13:03.125
of them that are significantly contributing

13:03.225 --> 13:05.565
to the breast cancer to recur, basically.

13:05.785 --> 13:09.325
So that, that basically, simplify that.

13:09.425 --> 13:11.285
Of course, our, our,

13:11.595 --> 13:13.885
make it our job a little bit easier as far

13:13.885 --> 13:15.845
as prediction model is concerned.

13:16.025 --> 13:17.645
So that's what, for that one,

13:17.945 --> 13:20.605
let me mention one more thing about the,

13:20.865 --> 13:21.885
the prediction thing.

13:22.015 --> 13:23.965
Again, different, model.

13:24.105 --> 13:28.405
We use that support, you know, so do it, vector machine,

13:28.465 --> 13:31.405
we use that decision three, we use that,

13:31.625 --> 13:34.565
and random forest, we use that neural network.

13:34.705 --> 13:37.725
We use that. All of these, basically,

13:37.875 --> 13:41.285
they have different way of looking at the thing.

13:41.945 --> 13:43.125
Our main concept

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or concept was that we have a, sort of a data

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that was supervised.

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Basically we were doing a supervised, you know, sort of a,

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machine learning because we knew about the data.

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We knew that there was a breast cancer recurrence

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and then all these different attributes.

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So it was a supervised learning basically.

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Uh, and then when we eliminate, you know, some

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of these attributes, it makes our training easier

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because we have few things, you know,

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to teach this machine that you know, how to look at it

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and decide the breast cancer recurrence.

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Now, after doing all that, our model set

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that decision three is the best one

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with more accuracy, basically.

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I mean, that's the thing. And,

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and in fact, one of the problem

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that we have is the lack of, as far as the future direction,

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conclusion, whatever is concerned, one

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of the main problem is

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that there is no standard in gathering the data.

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Every hospital have their own sort of a set of attributes.

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Uh, some people, they have some information, they say, well,

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you know, we have information about the ethnicity

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race, that kind of thing.

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Another dataset does not have that.

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Some of them, they use different techniques in order

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to gather the information.

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Another one does not have that.

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Some, most of them, they don't have genetic

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information basically.

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So in other words, when you don't have a unified

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or universal data collection technique,

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then it's gonna be hard to define a model that works

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for everybody, basically.

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So that's, that's the big problem.

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Another major issue is the number of patients.

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Most of the data that are available out there, they are,

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few of them, like there are smaller one three, some

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of the existing work they had 1,400.

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Ours was not too bad. 2,500, you know, we had that.

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But again, there is always missing information, that kind

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of things in, in there.

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So the thing that we have

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to look at it in the future is basically having more data,

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a standard in order so

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that all everyone use the same standard, to sort the way

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getting the data from the patient.

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Of course, some of them unfortunately are not available,

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like genetic information is not available.

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But the literature, of course, you ask a physician,

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they all know that if, if a patient has

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that means most probably the mother had also breast cancer,

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basically because it's a genetic thing.

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Uh, it could be there basically,

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but that information is not there.

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So whatever we decide, if we say

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that decision three is the best,

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but decision three is the best for our dataset,

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not with anybody else.

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And, and that's, that's the thing that, you know,

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we have to work on it.

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I mean, they, you know, sort of way the clinical, you know,

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sort of hospital, they have to make sure that, you know,

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they adopt some sort of a, universal, you know,

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mechanism in order to, you know, create the data so

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that we all work on the same, dataset.

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And then if we find something

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that could be applied in other things,

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pretty much the same thing.

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For instance, when when you create a vaccine for,

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for one place, you know, you wanna see that,

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does it work in, in, sort United State and does, or,

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or old people or younger people.

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That's, that's how it is because it's not a unique thing.

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It doesn't work for everybody.

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And so you have to have a different thing.

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Uh, overall, I guess if I wanna summarize that, it means,

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a lot of, I mean, we, we see

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that patients are very worried about the, you know,

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breast cancer recurrence.

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I mean, they, they do the treatment

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and then they think that they're all done.

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There are some, sort of

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with the breast cancer called triple negative breast cancer,

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very, you know, sort of a, aggressive sort

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of a type of the breast cancer.

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And then it sort of makes it very hard as far

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as the treatment or concern.

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I mean, they, they recur for sure, basically.

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Uh, and, and then it is hard really to, treat them,

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I guess a little bit of the, you know, again,

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What we need more data.

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I mean, that's the whole thing.

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And, and, you know, not too many of them are available,

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you know, outside as a open source basically.

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But I mean, this is the things that I'm, I'm sure that,

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you know, people are gonna be working on it

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because that's, that's what it is, medicine,

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and we are looking for every little bit

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of these things are gonna help us.

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