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Abstract #1112

Eigentumors of dynamic contrast-enhanced MR images of the breast for prediction of treatment failure

Hui Shan Chan1, Claudette Loo2, and Kenneth Gilhuijs1

1Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, 2Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands

A method is proposed for predicting long-term treatment failure using “eigentumors”: principal components computed from volumes surrounding breast tumors in contrast-enhanced images. The dataset contains pre-treatment scans of 563 consecutively included patients with early-stage breast cancer with median follow-up of 86 months. Principal components of washin and washout in box-shaped regions surrounding the tumors were computed, and LASSO and logistic regression were used to construct a model for predicting the probability of treatment failure. ROC analysis yields a bootstrapped performance of 0.73, and bootstrapped Kaplan-Meier survival curves based on the model’s outcome show significant separation (χ=32.89, P < 0.0001).

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