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Evaluating tumour b...
Evaluating tumour bounding options for deep learning-based axillary lymph node metastasis prediction in breast cancer
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- Gravina, Michela (författare)
- University of Naples Federico Ii, Dept. of Electrical Engineering and Information Technology, Naples, Italy
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- Cordelli, Ermanno (författare)
- University of Rome Campus Bio-Medico, Unit of Computer Systems and Bioinformatics, Dept. of Engineering, Roma, Italy
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- Santucci, Domiziana (författare)
- University of Rome Campus Bio-Medico, Unit of Computer Systems and Bioinformatics, Dept. of Engineering, Roma, Italy; Sant'Anna Hospital, San Fermo della Battaglia, Department of Radiology, Italy
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- Soda, Paolo (författare)
- Umeå universitet,Radiofysik,University of Rome Campus Bio-Medico, Unit of Computer Systems and Bioinformatics, Dept. of Engineering, Roma, Italy
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- Sansone, Carlo (författare)
- University of Naples Federico Ii, Dept. of Electrical Engineering and Information Technology, Naples, Italy
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University of Naples Federico Ii, Dept of Electrical Engineering and Information Technology, Naples, Italy University of Rome Campus Bio-Medico, Unit of Computer Systems and Bioinformatics, Dept. of Engineering, Roma, Italy (creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- Engelska.
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Ingår i: 2022 26th International Conference on Pattern Recognition (ICPR). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665490627 ; , s. 4335-4342
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
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- The involvement of axillary lymph node metastasis in breast cancer is one of the most important independent prognostic factors. While the metastasis of lymph node depends on primary tumour intrinsic behaviour, morphology and angioinvasivity, the involvement of the peritumoral tissue by the neoplastic cells also provides useful information for the potential tumour aggressiveness. The lymph node status is currently evaluated by histological invasive procedures with possible complications, asking for introducing safer approaches. Among different imaging techniques, the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) highlights physiological and morphological characteristics, reflecting breast lesions behaviour and aggressiveness. In the recent years, deep learning (DL) approaches, such as Convolutional Neural Networks, gained increasing popularity for biomedical image processing. Thanks to their ability to autonomously learn from images the set of features for the specific task to solve, they allow finding non-invasive alternatives to the standard procedures used up to now. This paper aims to evaluate the applicability of DL approaches for the axillary lymph node metastasis prediction, considering primary tumour DCE-MRI sequence. Differently from other work in the literature, we include a detailed analysis of healthy tissue influence in lymph node tumour spread through the evaluation of different tumour bounding options. Promising results are reported on a dataset of 153 patients with 155 malignant lesions.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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