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- W2898021589 abstract "In tumor therapy, estimating tumor growth is crucial to get an early information regarding tumor therapy response and, if neccessary, adapt therapy. We propose a novel deep learning based algorithm using deep convolutional sparse autoencoders to find a minimal representation of tumor shape and texture for colorectal liver metastases. Furthermore, we provide a prediction of future lesion growth based on single slice CT tumor images which prospectively can be used as a prognosis for physicians. The state of the art in tumor treatment assessment for solid tumors mainly uses tumor diameter in single CT slices as the treatment response criterion (RECIST). However, whereas the correlation between RECIST and final treatment outcome was shown to be significant, its effect size is still limited. With our approach we achieve a Matthews correlation coefficient of 52.0% in predicting tumor treatment response compared to 28.2% with radiologic assessment, as well as an AUC of 0.814 opposed to 0.698." @default.
- W2898021589 created "2018-10-26" @default.
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- W2898021589 date "2018-07-01" @default.
- W2898021589 modified "2023-10-01" @default.
- W2898021589 title "TumorEncode - Deep Convolutional Autoencoder for Computed Tomography Tumor Treatment Assessment" @default.
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- W2898021589 doi "https://doi.org/10.1109/ijcnn.2018.8489193" @default.
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