6533b829fe1ef96bd12899a7

RESEARCH PRODUCT

A boosting approach for prostate cancer detection using multi-parametric MRI

Jordi FreixenetDésiré SidibéRobert MartíPaul WalkerJoan C. VilanovaFabrice MeriaudeauJoan MassichGuillaume Lemaitre

subject

medicine.medical_specialty02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingcomputer.software_genremulti-parametric MRI03 medical and health sciencesProstate cancer0302 clinical medicineVoxelArea under curve0202 electrical engineering electronic engineering information engineeringmedicine[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingMulti parametricmedicine.diagnostic_testbusiness.industry020207 software engineeringMagnetic resonance imagingmedicine.diseaseprostate cancer3. Good healthMultiple factorsComputer-aided diagnosis030220 oncology & carcinogenesisGradient boostingcomputer-aided diagnosisGradient boostingRadiologybusinesscomputer[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing

description

International audience; Prostate cancer has been reported as the second most frequently diagnosed men cancers in the world. In the last decades, new imaging techniques based on MRI have been developed in order to improve the diagnosis task of radiologists. In practise, diagnosis can be affected by multiple factors reducing the chance to detect potential lesions. Computer-aided detection and computer-aided diagnosis have been designed to answer to these needs and provide help to radiologists in their daily duties. In this study, we proposed an automatic method to detect prostate cancer from a per voxel manner using 3T multi-parametric Magnetic Resonance Imaging (MRI) and a gradient boosting classifier. The best performances are obtained using all multi-parametric information as well as zonal information. The sensitivity and specificity obtained are 94.7% and 93.0%, respectively and an Area Under Curve (AUC) of 0.968.

https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01235890