Cervical Cancer Case Study

Supervising Professor: Dr. P.D.M. Macdonald

Team Members: Christine Calzonetti, Simo Goshev, Rongfang Gu, Shahidul Mohammad Islam, Amanda Lafontaine, Marcus Loreti, Maria

Porco, William Volterman, Qihao Xie-McMaster University--McMaster University-

•To determine which of the documented variables are useful for predicting recurrence of the disease

•To evaluate the extent to which tumor size, in particular, predicts the recurrence of the disease

Objectives:

Graphical Analysis

•The majority of patients observed were between the ages of 35 and 50

•No significant difference between relapse and non-relapse patients

20

30

40

50

60

70

Non-relapse Relapse

Boxplot of Age of Patient for Relapse Vs. Non-relapse Patients

Ag

e o

f P

atie

nt

at

time

of

Dia

gn

osi

s (Y

ea

rs)

  Mean

Median

Standard deviationNon-

relapse42.08 40 11.04

Relapse 42.04 39 11.17

•Similar means

•Dissimilar boxplots possibly due to outliers

•Missing values in the relapse group may have affected the outcome

01

02

03

04

05

0

Non-relapse Relapse

Boxplot of Tumor Depth for Relapse Vs. Non-relapse Patients

De

pth

of T

um

or

at

Dia

gn

osis

(m

m)

  Mean

Median

Standard deviationNon-

relapse6.76 5 6.83

Relapse 7.71 11 10.11

•A great disparity exists between the means and variability of relapse and non-relapse patients

•Relapse patients had larger tumor sizes upon diagnosis, suggesting that tumor size should be considered an important prognostic factor

02

04

06

0

Non-relapse Relapse

Boxplot of Tumor Size for Relapse Vs. Non-relapse Patients

Siz

e o

f Tu

mo

r a

t D

iag

no

sis

(mm

)

  Mean Median Standard deviation

Non-relapse 8.07 0 10.17

Relapse 18.86 20 16.31

01

Lymph Node Status for Non-Relapse Patients Lymph Node Status for Relapse Patients

•The difference in pie charts indicates that there are more cancerous cells found in the lymph nodes of patients who relapsed

•The statistical significance is unclear

•Relapse patients had a greater quantity of cells deemed “worse”

0

1

2 3

Cell Grade for Non-Relapse Patients

01

2

3

Cell Grade for Relapse Patients

0

1245

Disease Status for Non-Relapse Patients

012

3

Disease Status for Relapse Patients

•Recorded at the time of follow up appointment (therefore cannot be used as a diagnostic factor)

•Most non-relapse patients have no presence of disease at last follow up appointment

•In relapse patients, approx. ½ died of disease, ¼ are alive with disease, ¼ are alive with no evidence of disease

Results and

Conclusions

Survival Plot of Cervical Cancer Data

•Survival plot of data indicates that most relapses occur during the first three years after surgery, it is highly unlikely that relapse will occur after eight years

•The exponential curve deviated away from the survival curve at the tail end due to the patients who will never relapse

Time

Small (0-10mm)

Medium (11-30mm)

Large (30+mm)

•Recurrence time for large group considerably lower than medium

•Clear distinction between medium and small

•The patients in the different size groups had noticeably different mean times to recur

Survival Analysis yielded the following results:

•Significant difference between medium and small groups

•Significant difference between large and small groups

•Same results found using Weibull distribution in place of exponential distribution

A survival analysis of the data on S-Plus where the exponential distribution was assumed produced the following output:

Value Std. Error z p

(Intercept) 9.275 0.128 72.21 0.00e+000

cutsize1 -0.552 0.139 -3.97 7.06e-005

cutsize2 -0.670 0.100 -6.67 2.48e-011

A step-wise regression analysis of the data on S-Plus where the exponential distribution was assumed produced the following output:

Value Std. Error z p

(Intercept) 11.0113 0.8091 13.6092 3.53e-042

cutsize1 -0.0615 0.1796 -0.3425 7.32e-001

cutsize2 -0.3278 0.1618 -2.0256 4.28e-002

lymph -0.7694 0.4661 -1.6508 9.88e-002

depth -0.0703 0.0146 -4.7977 1.61e-006

grad -0.5229 0.2063 -2.5349 1.12e-002

age 0.0142 0.0145 0.9758 3.29e-001

rad 0.0245 0.2966 0.0827 9.34e-001

Regression Analysis yielded the following results:

Initial variables:

Final variables: Value Std. Error z p

(Intercept) 11.5989 0.5526 20.99 8.03e-098

cutsize1 -0.0660 0.1786 -0.37 7.12e-001

cutsize2 -0.3292 0.1609 -2.05 4.08e-002

lymph -0.7735 0.3973 -1.95 5.16e-002

depth -0.0666 0.0141 -4.71 2.46e-006

grad -0.5378 0.2063 -2.61 9.15e-003

•Initial analysis showed that possible prognostic factors were Size, Lymph Nodes, Tumor Depth and Cell Grade

•Cox’s Proportional Hazard reaffirmed that Size, Depth and Cell Grade were important diagnostic factors, but Lymph Nodes are only significant at the 10% level