Evidence map›Paper›PMID 29790078›Full record

ArticleInternational journal of computer assisted radiology and surgery2018

A PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR images.

Mohammed El Adoui, Stylianos Drisis, Mohammed Benjelloun

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Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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6 citing papers in PubMed.

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5 · Who and what money

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3 authors.

Mohammed El AdouiComputer Science Unit, Faculty of Engineering, University of Mons, Mons, Belgium. Mohammed.Eladoui@umons.ac.be.ORCID http://orcid.org/0000-0003-1891-2318
Stylianos DrisisJules Bordet Institute, Brussels, Belgium.
Mohammed BenjellounComputer Science Unit, Faculty of Engineering, University of Mons, Mons, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aims to provide and optimize a performing algorithm for predicting the breast cancer response rate to the first round of chemotherapy using Magnetic Resonance Imaging (MRI). This provides an early recognition of breast tumor reaction to chemotherapy by using the Parametric Response Map (PRM) method.

methodsPRM may predict the breast cancer response to chemotherapy by analyzing voxel-by-voxel temporal intra-tumor changes during one round of chemotherapy. Indeed, the tumor recognizes intra-tumor changes concerning its vascularity, which is an important criterion in the present study. This method is mainly based on spatial image affine registration between the breast tumor MRI volumes, acquired before and after the first cycle of chemotherapy, and region growing segmentation of the tumor volume. To evaluate our method, we used a retrospective study of 40 patients provided by a collaborating institute.

resultsPRM allows a color map to be created with the percentages of positive, negative and stable breast tumor response during the first round of chemotherapy, identifying each region with its response rate. We assessed the accuracy of the proposed method using technical and medical validation methods. The technical validation was based on landmarks-based registration and fully manual segmentation. The medical evaluation was based on the accuracy calculation of the standard reference of anatomic pathology. The p-values and the Area Under the Curve (AUC) of the Receiver Operating Characteristics were calculated to evaluate the proposed PRM method.

conclusionWe performed and evaluated the proposed PRM method to study and analyze the behavior of a tumor during the first round of chemotherapy, based on the intra-tumor changes of MR breast tumor images. The AUC obtained for the PRM method is considered as relevant in the early prediction of breast tumor response.

Indexed as

Magnetic Resonance ImagingTumor BurdenAlgorithmsAntineoplastic AgentsBreast NeoplasmsFemaleHumansRetrospective StudiesTreatment OutcomeAntineoplastic AgentsAffine registrationImage classificationImage registrationImage segmentationMRI breast cancerPRM

Identifiers

PMID29790078

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