MEDICAL SCORING FOR BREAST CANCER RECURRENCE. Nurul Husna bt Jamian UiTM (Perak), Tapah Campus

Transcription

1 MEDICAL SCORING FOR BREAST CANCER RECURRENCE Nurul Husna bt Jamian UiTM (Perak), Tapah Campus

2 SAS GLOBAL FORUM 2014

3 Overview Introduction Methodology Analysis and Findings Conclusion Q&A

4 INTRODUCTION

5 Breast Cancer A malignant (cancer) tumour that starts from cells of the breast (American Cancer Society, 2010; National Cancer Institute, 2012).

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7 Breast Cancer Recurrence (BCR) The return of breast cancer after primary treatment that can recur within the first three to five years (Imaginis, 2000). The Preliminary Report in 2008 by National Cancer Patient Registry (NCPR), Malaysia reported 108 out of 154 patients with follow-up information (1 st June - 31 st Dec. 2008) showed 94.4% patients are disease free while 5.6% patients are recurrent disease.

8 Problem Statement There is still lack of studies on the risk factors of breast cancer recurrence in Malaysia where most of the studies focus on survival rate of breast cancer and its risk factors (Norsa adah et al., 2005; Mohammed et al., 2009; Ong & Yip C.H, 2003). However, breast cancer recurrence studies have been mostly conducted in developing countries such as in United States, Japan and Canada. Objectives 1. To develop a BCR-scorecard model for predicting the risk of breast cancer recurrence. 2. To identify the important risk factors of breast cancer recurrence among Malaysian women.

9 METHODOLOGY

10 Data Source Total of 1149 breast cancer patients Diagnosed and undergo treatment at Department of Surgery in Hospital Kuala Lumpur (HKL) Data ( ) patients files Data ( ) National Cancer Patient Registry (NCPR) Data collection procedures: Population 1149 Sample 454 Register with NMRR Approval from: CRC, MREC, Director of HKL, Head of Surgery Department and Record Unit Get data from HKL

11 Theoretical Framework

12 Analysis Procedures and Process of Developing Medical Scoring Models Descriptive Analysis SAS Enterprise Miner 7.1 BCR-scorecard Logistic Regression Models Comparison Process Flow Diagram

13 ANALYSIS AND FINDINGS

14 Descriptive Analysis

15 Descriptive Analysis Result

16 BCR-scorecard Model

17 Grouping: Information Value (IV) (Screenshot of Output Variables) Stage, histological type, vascular invasion, race and tumour size are included in the BCR-Scorecard model since the IV for these variables greater than 0.1

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19 Determination of Cut off Score (Threshold) Screenshot for Kolmogorov Smirnov Table The cut-off score is 140 due to the highest K-S statistic, means the minimum acceptable level of risk. Patients who score above 140 will not recur while those who score below 140 have higher chances of recurrence.

20 Scaling the Scorecard Variable Group Attribute Scorecard Points Histological 1 type 2 Race Stage Tumour size Vascular 1 invasion 2 Lobular infiltrating Ductal infiltrating Indian Malay Chinese I III II IV 6cm and above 3cm-5.9cm Less than 3cm Absent Present The lowest scorecard point of each attribute indicates the highest risk of recurrence.

21 Example: Patient Attributes Calculation Score A Malay Stage IV Tumour size (3cm-5.9cm) Ductal carcinoma High Risk B Indian Lobular infiltrating Absent of vascular invasion Stage III Patient A has higher risk of recurrence since the score obtained less than 140 (92<140). Patient B has lower risk of recurrence since the score calculated greater than 140 (155>140).

22 Models Comparison

23 Classification Table Results Model Validation Misclassification Accuracy BCR-scorecard Logistic regression Interpretation BCR-scorecard (18%) less than logistic regression model (23%) BCR-scorecard model (82%) greater than logistic regression model (77%). The BCR-scorecard was better predictive model than logistic regression. However, some data need to be collected to verify this result.

24 CONCLUSION

25 BCR-scorecard model has better predictive ability with lower misclassification rate (18%) compared to Logistic Regression model (23%). Five important risk factors were identified in predicting recurrence status: histological type race stage tumour size vascular invasion

26 THANK YOU

27 Data Partition

28 Split the original data into a training and a validation data set randomly. A typical partition proposed is at ratio 70:30 which is 70% for training and 30% for validation (SAS Institute Inc., 2009; Perline, 2011) Training data set to build the model Validation data set to assess the models Data Partition Result Partition Recurrence Remission Total Training (70%) Validation (30%) Total

29 Grouping: Weight of Evidence (WOE) Stage Variables Groups Attributes WOE Histological type Vascular invasion I III II IV Lobular infiltrating Ductal infiltrating Absent Present (+) Relatively Low risk: stage I & III, lobular infiltrating, absent of vascular invasion, Indian, Malay and tumour size (<3cm). (-) Relatively High risk: stage II & stage IV, ductal infiltrating, present of vascular invasion, Chinese and tumour size ( 6cm) and (3cm-5.9cm). Race Indian Malay Chinese Tumour size cm and above 3cm-5.9cm Less than 3cm

30 Scaling the Scorecard The value of odds is 50, scorecard points (score) is 200 and points of double odds is 20.

31 Logistic Regression Model

32 Logistic regression model in medical scoring is to determine the probability of the patients getting recurrence P(Y=1). The probability of a value of one for the dichotomous outcome P(Y=1) given as (Yap et al., 2011): Wald s test H 0 : β i = 0 (the independent variable have no effect on Breast Cancer Recurrence) H i : β i 0 (the independent variable have an effect on Breast Cancer Recurrence) 2 Reject H 0 if Wald statistic > or p-value < 0.05 X,1 Odds ratio Measure of association in term of how much each significant independent variable more likely or unlikely towards outcome of interest (Y=1).

33 Variable p-value Odd ratio Intercept Age (Less than 40 years ) * Age (40 59 years) Marital Status(Single) Race(Malay) Race(Chinese) ** Family History(Yes) Stage(I) Stage(II) * Stage(III) Tumour Size(Less than 3cm ) Tumour Size(3cm-5.9cm) Histological Grade(Grade I) Histological Grade(Grade II) Histological Type(Ductal ** infiltrating) Lymph Nodes Status(0) Lymph Nodes Status(1-3) Lymph Nodes Status(4-9) Vascular Invasion (Present) Estrogen Receptors (Positive) Progesterone Receptors * (Positive) Cerb2 (Positive) **P<0.001 *p<0.05 Significant variables: age group race stage histological type progesterone receptors The odds ratio indicates that: The prevalence of breast cancer recurrence higher among younger patients. Chinese patients are more likely to have breast cancer recurrence as compared to other races. Patients diagnosed at stage II are more likely to recur than stage IV Ductal infiltrating carcinoma patients are more likely to recur than lobular infiltrating carcinoma patients. Positive progesterone receptors are more likely to recur compared to patients with negative progesterone receptors.

34 Models Comparison Receiver Operating Characteristic (ROC) curve Predicted Recurrence Remission Classification Table Actual Recurrence Remission True Positive (TP) False Negative (FN) False Positive (FP) True Negative (TN) Total Actual TP+FN FP+TN Total Predicted TP+FP FN+TN TP+FP+ FN+TN The greater the area under the curve, the better is the model.

35 Receiver Operating Characteristic (ROC) BCRscorecard Logistic regression BCR-scorecard model has higher overall accuracy of the test.

36 Classification Table Results Model Validation Misclassification Accuracy Sensitivity Specificity BCR-scorecard Logistic regression Interpretation BCR-scorecard (18%) less than logistic regression model (23%) BCR-scorecard model (82%) greater than logistic regression model (77%). Logistic regression could predict recurrence (13%) higher than BCRscorecard (4%). BCR-scorecard could predict remission (97%) higher than logistic regression (90%). The BCR-scorecard was better predictive model than logistic regression. However, some data need to be collected to verify this result.