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Analyzing breast tumor heterogeneity to predict the response to chemotherapy using 3D MR images registration

Published: 21 July 2017 Publication History

Abstract

Breast tumor structure is extremely heterogeneous. This heterogeneity changes spatially during chemotherapy treatment. This was correlated with how the tumor reacts to Neoadjuvant chemotherapy (treatment presiding surgery). A significant number of studies adopting Magnetic Resonance Imaging (MRI) have looked into the quantification of intratumor heterogeneity of breast cancer. Nevertheless, a limited number of them are interested chiefly in evaluating breast cancer heterogeneity as index of response to treatment. In this paper, we present a new approach that compares breast tumor heterogeneity degree, using acquired images before and after chemotherapy. The purpose of our study is to help radiologists to predict the effectiveness of Neoadjuvant chemotherapy as soon as possible. Indeed, many patients with breast cancer have a non-responding tumor type to chemotherapy. However, these patients still requiring avoidable chemotherapies during a long time, which causes several undesirable effects. We evaluate our study using a data set constructed by Dynamic Contrast Enhanced (DCE-MRI) and Diffusion Weighed sequences of MRI (DW-MRI), consisting of 64 adult patients. Our approach consists to apply a volumetric registration between images acquired before and after chemotherapy to quantify induced changes on the tumor by the first chemotherapy session. The first step of our approach is to segment the volume of interest (VOI). Then, a coherent volumetric registration will provide to make a voxel by voxel comparison of breast tumor volume. Therefore, The breast tumor response rate to the first chemotherapy session was obtained, by comparing each voxel intensity in DCE-MR and DW-MRI sequences before and after the chemotherapy. This approach will not only provide the breast tumor response degree to chemotherapy, but also monitoring tumor regions that have responded, not responded and tumor regions that have recognized disease progression during chemotherapy session. This will allow radiologists and oncologists to decide if the patient will continue to require chemotherapy, or applying other alternative solutions, without wasting time in unnecessary chemotherapy sessions.

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        cover image ACM Other conferences
        ICSDE '17: Proceedings of the 2017 International Conference on Smart Digital Environment
        July 2017
        245 pages
        ISBN:9781450352819
        DOI:10.1145/3128128
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Publication History

        Published: 21 July 2017

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        Author Tags

        1. MRI
        2. breast cancer
        3. image registration
        4. neoadjuvant chemotherapy
        5. parametric response map
        6. tumor response predection

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        ICSDE '17 Paper Acceptance Rate 36 of 139 submissions, 26%;
        Overall Acceptance Rate 68 of 219 submissions, 31%

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        • (2024)Individual tooth segmentation in human teeth images using pseudo edge-region obtained by deep neural networksSignal Processing: Image Communication10.1016/j.image.2023.117076120(117076)Online publication date: Jan-2024
        • (2023)Explainable deep learning approach to predict chemotherapy effect on breast tumor’s MRIState of the Art in Neural Networks and Their Applications10.1016/B978-0-12-819872-8.00014-8(147-156)Online publication date: 2023
        • (2022)New Explainable Deep CNN Design for Classifying Breast Tumor Response Over Neoadjuvant ChemotherapyCurrent Medical Imaging Formerly Current Medical Imaging Reviews10.2174/157340561866622080312442619:5(526-533)Online publication date: May-2022
        • (2020)Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR imagesInternational Journal of Computer Assisted Radiology and Surgery10.1007/s11548-020-02209-9Online publication date: 16-Jun-2020
        • (2019)MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN ArchitecturesComputers10.3390/computers80300528:3(52)Online publication date: 29-Jun-2019
        • (2019)Deep Learning approach predicting breast tumor response to neoadjuvant treatment using DCE-MRI volumes acquired before and after chemotherapyMedical Imaging 2019: Computer-Aided Diagnosis10.1117/12.2505887(90)Online publication date: 13-Mar-2019
        • (2019)Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challengesArtificial Intelligence Review10.1007/s10462-019-09716-5Online publication date: 25-May-2019
        • (2019)Predict Breast Tumor Response to Chemotherapy Using a 3D Deep Learning Architecture Applied to DCE-MRI DataBioinformatics and Biomedical Engineering10.1007/978-3-030-17935-9_4(33-40)Online publication date: 13-Apr-2019
        • (2018)A PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR imagesInternational Journal of Computer Assisted Radiology and Surgery10.1007/s11548-018-1790-y13:8(1233-1243)Online publication date: 22-May-2018

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