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Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models

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Abstract

Objectives

Multiparametric MRI (mp-MRI) has been significantly used for detection, localization and staging of Prostate cancer (PCa). However, all the assessment suffers from poor reproducibility among the readers. The aim of this study was to evaluate radiomics models to diagnose PCa using high-resolution T2-weighted (T2-W) and dynamic contrast-enhanced (DCE) MRI.

Materials and methods

Thirty two patients who had high prostate specific antigen level were recruited. The prostate biopsies considered as the reference to differentiate between 66 benign and 36 malignant prostate lesions. 181 features were extracted from each modality. K-nearest neighbors, artificial neural network, decision tree, and linear discriminant analysis were used for machine-learning study. The leave-one-out cross-validation method was used to prevent overfitting and build robust models.

Results

Radiomics analysis showed that T2-W images were more effective in PCa detection compare to DCE images. Local binary pattern features and speeded up robust features had the highest ability for prediction in T2-W and DCE images, respectively. The classifier fusion using decision template method showed the highest performance with accuracy, specificity, and sensitivity of 100%.

Discussion

The findings of this framework provide researchers on PCa with a promising method for reliable detection of prostate lesions in MR images by fused model.

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Acknowledgements

The authors would like to thank Dr. Anahita Fathi Kazerooni for her kind and thoughtful consultations. In addition, authors would like to thank the Quantitative MR Imaging and Spectroscopy Group (QMISG), Miss Hanieh Mobarak Salari in particular, for all the coordination and cooperation in this project.

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Authors and Affiliations

  1. Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Ghazaleh Jamshidi & Farshid Babapour Mofrad

  2. Department of Radiology Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Ali Abbasian Ardakani

  3. Department of Radiology, School of Medicine, Hazrat Rasoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran

    Mahyar Ghafoori

  4. Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran

    Hamidreza Saligheh Rad

  5. Quantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran

    Hamidreza Saligheh Rad

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  1. Ghazaleh Jamshidi
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  2. Ali Abbasian Ardakani
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  3. Mahyar Ghafoori
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  4. Farshid Babapour Mofrad
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Correspondence to Hamidreza Saligheh Rad.

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Jamshidi, G., Abbasian Ardakani, A., Ghafoori, M. et al. Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models. Magn Reson Mater Phy 36, 55–64 (2023). https://doi.org/10.1007/s10334-022-01037-z

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  • DOI: https://doi.org/10.1007/s10334-022-01037-z

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