A prediction model for breast cancer recurrence after adjuvant hormone therapy

2008 ◽  
Vol 23 (4) ◽  
pp. 199-206 ◽  
Author(s):  
P. Boracchi ◽  
D. Coradini ◽  
S. Antolini ◽  
S. Oriana ◽  
Ruggero Dittadi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Model ◽  
Hormone Therapy ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Adjuvant Hormone Therapy

A Prediction Model for Breast Cancer Recurrence after Adjuvant Hormone Therapy

2008 ◽  
Vol 23 (4) ◽  
pp. 199-206 ◽  
Author(s):  
P. Boracchi ◽  
D. Coradini ◽  
L. Antolini ◽  
S. Oriana ◽  
R. Dittadi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Model ◽  
Hormone Therapy ◽  
Cancer Recurrence ◽  
Training Data ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Adjuvant Hormone Therapy ◽  
Estrogen Receptor Positive ◽  
Clinical Records

Hormone therapy with tamoxifen has long been the established adjuvant treatment for node-positive, estrogen–receptor-positive breast cancer in postmenopausal women. Since 30–40% of these patients fail to respond, reliable outcome prediction is necessary for successful treatment allocation. Using pathobiological variables (available in most clinical records: tumor size, nodal involvement, estrogen and progesterone receptor content) from 596 patients recruited at a comprehensive cancer center, we developed a prediction model which we validated in an independent cohort of 175 patients recruited at a general hospital. Calculated at 3 and 4 years of follow-up, the discrimination indices were 0.716 [confidence limits (CL) 0.641, 0.752] and 0.714 (CL 0.650, 0.750) for the training data, and 0.726 (CL 0.591, 0.769) and 0.677 (CL 0.580, 0.745) for the testing data. Waiting for more effective approaches from genomic and proteomic studies, a model based on consolidated pathobiological variables routinely assessed at relatively low costs may be considered as the reference for assessing the gain of new markers over traditional ones, thus substantially improving the conventional use of prognostic criteria.

scholarly journals Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine

2012 ◽  
Vol 15 (2) ◽  
pp. 230 ◽  
Author(s):  
Woojae Kim ◽  
Ku Sang Kim ◽  
Jeong Eon Lee ◽  
Dong-Young Noh ◽  
Sung-Won Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Support Vector ◽  
Recurrence Prediction
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