This document describes the development of a nomogram to predict breast cancer survival using demographic, diagnostic, and treatment data from the SEER database. The authors selected 13 variables related to survival time, age, race, cancer stage and other factors. Different classification models were tested including logistic regression, naive bayes, decision trees, and random forests. Cox proportional hazards and Aalen's additive regression models were also used. The results were used to build a "calculator" or nomogram to estimate survival time for individuals based on their characteristics. Limitations include the nomogram not being a substitute for medical advice and potential missing or incomplete data in the SEER database.

1. A Data-Driven
Nomogram for
Breast Cancer
Survival
Capstone by Lisa Federer, Bret Gaulin, Michal Haskell, Andy
Pollack, and Carmen Smiley

2. The Problem
Image source:

3. 1 in 8 US women will be diagnosed with
breast cancer during their lifetime.

4. Image source: http://www.pathophys.org/wp-content/uploads/2012/12/breastcancer-copy.png

5. Our hypothesis
Certain demographic, diagnostic, and treatment parameters
can reliably predict survival time for women with breast
cancer. Using this knowledge, we could build a “calculator” to
estimate survival time for individuals.

6. The data
http://seer.cancer.gov/

7. Data exploration and variable
selection
• Survival time (months)
• Age at diagnosis
• Year of birth
• Race
• Origin (Hispanic recode)
• Stage
• Histology
• Tumor extent
• Number of primary tumors
• Laterality
• ER Status
• PR Status
• Radiation therapy
146 variables in
SEER database
13 variables
of interest

8. Classification by survival time

9. Classification model results
Model Precision Recall f-score Support
Logistic
Regression
0 0.65 0.43 0.23 146
1 0.55 0.37 0.44 416
2 0.55 0.76 0.63 471
Average 0.56 0.56 0.54 1033
Naïve Bayes
0 0.39 0.16 0.23 162
1 0.63 0.3 0.4 402
2 0.5 0.84 0.63 466
Average 0.54 0.84 0.63 1030
Decision Tree
0 0.3 0.39 0.34 131
1 0.76 0.75 0.75 416
2 0.8 0.75 0.78 485
Average 0.72 0.7 0.71 1032
Random Forest
0 0.58 0.43 0.5 183
1 0.73 0.75 0.74 414
2 0.71 0.78 0.74 440
Average 0.7 0.7 0.7 1037
K Neighbors
0 0.53 0.33 0.41 150
1 0.83 0.82 0.82 425
2 0.77 0.88 0.82 472
Average 0.76 0.77 0.76 1047

10. Regression and survival models
Cox proportional hazards model
𝜆 𝑡 = 𝑏0 𝑡 exp(𝑏1 𝑥1 + ⋯ + 𝑏 𝑁 𝑥 𝑛)
Aalen’s additive model
𝜆 𝑡 = 𝑏0 𝑡 + 𝑏1 𝑡 𝑥1 + ⋯ + 𝑏 𝑁 (𝑡)𝑥 𝑇)

11. Nomogram demonstration

12. Limitations
Not a substitute for medical advice

13. Limitations
Missing or incomplete data in SEER database

14. Questions?