scholarly journals Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study

JMIR Cancer ◽  
10.2196/18143 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e18143
Author(s):  
Teresa A'mar ◽  
J David Beatty ◽  
Catherine Fedorenko ◽  
Daniel Markowitz ◽  
Thomas Corey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Predictive Value ◽  
Puget Sound ◽  
Cancer Recurrence ◽  
Cancer Registries ◽  
Population Based ◽  
Medical Claims ◽  
Predicted Probability ◽  
Second Breast Cancer

Background There is a need for automated approaches to incorporate information on cancer recurrence events into population-based cancer registries. Objective The aim of this study is to determine the accuracy of a novel data mining algorithm to extract information from linked registry and medical claims data on the occurrence and timing of second breast cancer events (SBCE). Methods We used supervised data from 3092 stage I and II breast cancer cases (with 394 recurrences), diagnosed between 1993 and 2006 inclusive, of patients at Kaiser Permanente Washington and cases in the Puget Sound Cancer Surveillance System. Our goal was to classify each month after primary treatment as pre- versus post-SBCE. The prediction feature set for a given month consisted of registry variables on disease and patient characteristics related to the primary breast cancer event, as well as features based on monthly counts of diagnosis and procedure codes for the current, prior, and future months. A month was classified as post-SBCE if the predicted probability exceeded a probability threshold (PT); the predicted time of the SBCE was taken to be the month of maximum increase in the predicted probability between adjacent months. Results The Kaplan-Meier net probability of SBCE was 0.25 at 14 years. The month-level receiver operating characteristic curve on test data (20% of the data set) had an area under the curve of 0.986. The person-level predictions (at a monthly PT of 0.5) had a sensitivity of 0.89, a specificity of 0.98, a positive predictive value of 0.85, and a negative predictive value of 0.98. The corresponding median difference between the observed and predicted months of recurrence was 0 and the mean difference was 0.04 months. Conclusions Data mining of medical claims holds promise for the streamlining of cancer registry operations to feasibly collect information about second breast cancer events.

scholarly journals Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study (Preprint)

2020 ◽  
Author(s):  
Teresa A'mar ◽  
J David Beatty ◽  
Catherine Fedorenko ◽  
Daniel Markowitz ◽  
Thomas Corey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Predictive Value ◽  
Puget Sound ◽  
Cancer Recurrence ◽  
Cancer Registries ◽  
Population Based ◽  
Medical Claims ◽  
Predicted Probability ◽  
Second Breast Cancer

BACKGROUND There is a need for automated approaches to incorporate information on cancer recurrence events into population-based cancer registries. OBJECTIVE The aim of this study is to determine the accuracy of a novel data mining algorithm to extract information from linked registry and medical claims data on the occurrence and timing of second breast cancer events (SBCE). METHODS We used supervised data from 3092 stage I and II breast cancer cases (with 394 recurrences), diagnosed between 1993 and 2006 inclusive, of patients at Kaiser Permanente Washington and cases in the Puget Sound Cancer Surveillance System. Our goal was to classify each month after primary treatment as pre- versus post-SBCE. The prediction feature set for a given month consisted of registry variables on disease and patient characteristics related to the primary breast cancer event, as well as features based on monthly counts of diagnosis and procedure codes for the current, prior, and future months. A month was classified as post-SBCE if the predicted probability exceeded a probability threshold (PT); the predicted time of the SBCE was taken to be the month of maximum increase in the predicted probability between adjacent months. RESULTS The Kaplan-Meier net probability of SBCE was 0.25 at 14 years. The month-level receiver operating characteristic curve on test data (20% of the data set) had an area under the curve of 0.986. The person-level predictions (at a monthly PT of 0.5) had a sensitivity of 0.89, a specificity of 0.98, a positive predictive value of 0.85, and a negative predictive value of 0.98. The corresponding median difference between the observed and predicted months of recurrence was 0 and the mean difference was 0.04 months. CONCLUSIONS Data mining of medical claims holds promise for the streamlining of cancer registry operations to feasibly collect information about second breast cancer events.

scholarly journals Correction: Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study (Preprint)

2020 ◽  
Author(s):  
Teresa A'mar ◽  
J David Beatty ◽  
Catherine Fedorenko ◽  
Daniel Markowitz ◽  
Thomas Corey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Health Research ◽  
Cancer Recurrence ◽  
Cancer Registries ◽  
Population Based ◽  
Breast Cancer Recurrence ◽  
Kaiser Permanente ◽  
Medical Claims ◽  
Research Institute

UNSTRUCTURED In “Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study (J Med Internet Res Ca 2020;6(2):e18143)” the authors noted two errors. The metadata erroneously listed only Drs. A’mar and Etzioni as having contributed equally; this has been corrected to reflect that Drs. A’mar, Chubak, and Etzioni contributed equally. In addition, Dr. Chubak’s affiliation has been corrected from “Washington Health Research Institute, Kaiser Permanente” to “Kaiser Permanente Washington Health Research Institute.”

scholarly journals Correction: Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study

JMIR Cancer ◽  
10.2196/23821 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e23821
Author(s):  
Teresa A'mar ◽  
J David Beatty ◽  
Catherine Fedorenko ◽  
Daniel Markowitz ◽  
Thomas Corey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Cancer Recurrence ◽  
Cancer Registries ◽  
Population Based ◽  
Breast Cancer Recurrence ◽  
Medical Claims

Use of Truquant BR radioimmunoassay for early detection of breast cancer recurrence in patients with stage II and stage III disease.

1997 ◽  
Vol 15 (6) ◽  
pp. 2322-2328 ◽  
Author(s):  
D W Chan ◽  
R A Beveridge ◽  
H Muss ◽  
H A Fritsche ◽  
G Hortobagyi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Predictive Value ◽  
Cox Regression ◽  
Recurrent Disease ◽  
Cancer Recurrence ◽  
Stage Ii ◽  
Stage Iii ◽  
Antigen Test ◽  
Cox Regression Analysis ◽  
Test Result

PURPOSE The Truquant BR radioimmunoassay (RIA) (Biomira Diagnostics Inc, Rexdale, Canada) uses the monoclonal antibody B27.29 to quantitate the MUC-1 gene product (CA 27.29 antigen) in serum. We evaluated CA 27.29 antigen in a controlled, prospective clinical trial for its ability to predict relapse in stage II and stage III breast cancer patients. PATIENTS AND METHODS Over a 2-year period, 166 patients who had completed therapy for stage II (80.1%) or III (19.9%) breast cancer and were clinically free of disease were serially tested for CA 27.29 antigen levels. The study was double-masked and cancer recurrence was documented based on clinical findings. Patients with two consecutive CA 27.29 antigen test results above the upper limit of normal were considered positive. RESULTS The Truquant BR RIA had a sensitivity of 57.7%, specificity of 97.9%, positive predictive value of 83.3%, and negative predictive value of 92.6%. The recurrence rate was 15.7%. A Cox regression analysis showed that the only variable to correlate with recurrent disease was the CA 27.29 antigen test result. Patients with a positive test result had increased odds of having a recurrence (odds ratio, 6.8; P < .00001). The test was effective in predicting recurrence in patients with both distant and locoregional disease. In a subgroup of patients with bone pain, CA 27.29 antigen level was found to identify reliably patients who would subsequently develop recurrent disease. CONCLUSION These data demonstrate that the Truquant BR RIA can be used as an aid to predict recurrent breast cancer in patients with stage II and III disease.

scholarly journals The effect of 14-3-3ζ expression on tamoxifen resistance and breast cancer recurrence: a Danish population-based study

2017 ◽  
Vol 165 (3) ◽  
pp. 633-643 ◽  
Author(s):  
Jake E. Thistle ◽  
Ylva Hellberg ◽  
Kristina Mortensen ◽  
Stephen Hamilton–Dutoit ◽  
Anders Kjærsgaard ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tamoxifen Resistance ◽  
Cancer Recurrence ◽  
Population Based ◽  
Breast Cancer Recurrence ◽  
Danish Population ◽  
Population Based Study

scholarly journals Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis

2020 ◽  
Vol 6 (3) ◽  
pp. 313
Author(s):  
Zuhaira Muhammad Zain ◽  
Mona Alshenaifi ◽  
Abeer Aljaloud ◽  
Tamadhur Albednah ◽  
Reham Alghanim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Medical Information ◽  
Cancer Recurrence ◽  
Principal Component ◽  
Component Analysis ◽  
Breast Cancer Recurrence ◽  
F Measure

Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely.

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