Predictions of the pathological response to neoadjuvant chemotherapy in patients with primary breast cancer using a data mining technique

Nomogram, a standard technique that utilizes multiple characteristics to predict efficacy of treatment and likelihood of a specific status of an individual patient, has been used for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. The aim of this study was to deve...

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Bibliographic Details
Published in:Breast cancer research and treatment 2012-07, Vol.134 (2), p.661-670
Main Authors: Takada, M., Sugimoto, M., Ohno, S., Kuroi, K., Sato, N., Bando, H., Masuda, N., Iwata, H., Kondo, M., Sasano, H., Chow, L. W. C., Inamoto, T., Naito, Y., Tomita, M., Toi, M.
Format: Article
Language:English
Subjects:
Adjuvant treatment
Adult
Aged
Area Under Curve
Biological and medical sciences
Breast cancer
Breast Neoplasms - drug therapy
Cancer
Cancer patients
Cancer research
Cancer therapies
Carcinoma, Ductal, Breast - drug therapy
Care and treatment
Chemotherapy
Chemotherapy, Adjuvant
Clinical outcomes
Computer Simulation
Data Interpretation, Statistical
Data Mining
Decision Trees
Female
Gynecology. Andrology. Obstetrics
Humans
Logistic Models
Mammary gland diseases
Medical sciences
Medicine
Medicine & Public Health
Methods
Middle Aged
Models, Biological
Multivariate Analysis
Neoadjuvant Therapy
Nomograms
Oncology
Preclinical Study
Predictions
Retrospective Studies
ROC Curve
Surgery
Treatment Outcome
Tumors
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Description
Summary:Nomogram, a standard technique that utilizes multiple characteristics to predict efficacy of treatment and likelihood of a specific status of an individual patient, has been used for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. The aim of this study was to develop a novel computational technique to predict the pathological complete response (pCR) to NAC in primary breast cancer patients. A mathematical model using alternating decision trees, an epigone of decision tree, was developed using 28 clinicopathological variables that were retrospectively collected from patients treated with NAC ( n  = 150), and validated using an independent dataset from a randomized controlled trial ( n  = 173). The model selected 15 variables to predict the pCR with yielding area under the receiver operating characteristics curve (AUC) values of 0.766 [95 % confidence interval (CI)], 0.671–0.861, P value 
ISSN:0167-6806
1573-7217
DOI:10.1007/s10549-012-2109-2