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2016 | OriginalPaper | Buchkapitel

Prostate Cancer: Improved Tissue Characterization by Temporal Modeling of Radio-Frequency Ultrasound Echo Data

verfasst von : Layan Nahlawi, Farhad Imani, Mena Gaed, Jose A. Gomez, Madeleine Moussa, Eli Gibson, Aaron Fenster, Aaron D. Ward, Purang Abolmaesumi, Hagit Shatkay, Parvin Mousavi

Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

Verlag: Springer International Publishing

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Abstract

Despite recent advances in clinical oncology, prostate cancer remains a major health concern in men, where current detection techniques still lead to both over- and under-diagnosis. More accurate prediction and detection of prostate cancer can improve disease management and treatment outcome. Temporal ultrasound is a promising imaging approach that can help identify tissue-specific patterns in time-series of ultrasound data and, in turn, differentiate between benign and malignant tissues. We propose a probabilistic-temporal framework, based on hidden Markov models, for modeling ultrasound time-series data obtained from prostate cancer patients. Our results show improved prediction of malignancy compared to previously reported results, where we identify cancerous regions with over 88 % accuracy. As our models directly represent temporal aspects of the data, we expect our method to be applicable to other types of cancer in which temporal-ultrasound can be captured.

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Fußnoten
1
The study was approved by the institutional research ethics board and the patients provided consent to participate.
 
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Metadaten
Titel
Prostate Cancer: Improved Tissue Characterization by Temporal Modeling of Radio-Frequency Ultrasound Echo Data
verfasst von
Layan Nahlawi
Farhad Imani
Mena Gaed
Jose A. Gomez
Madeleine Moussa
Eli Gibson
Aaron Fenster
Aaron D. Ward
Purang Abolmaesumi
Hagit Shatkay
Parvin Mousavi
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-46720-7_75