Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI

Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the ra...

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Autores principales: Bertelli, Elena, Mercatelli, Laura, Marzi, Chiara, Pachetti, Eva, Baccini, Michela, Barucci, Andrea, Colantonio, Sara, Gherardini, Luca, Lattavo, Lorenzo, Pascali, Maria Antonietta, Agostini, Simone, Miele, Vittorio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8792745/
https://www.ncbi.nlm.nih.gov/pubmed/35096605
http://dx.doi.org/10.3389/fonc.2021.802964
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author Bertelli, Elena
Mercatelli, Laura
Marzi, Chiara
Pachetti, Eva
Baccini, Michela
Barucci, Andrea
Colantonio, Sara
Gherardini, Luca
Lattavo, Lorenzo
Pascali, Maria Antonietta
Agostini, Simone
Miele, Vittorio
author_facet Bertelli, Elena
Mercatelli, Laura
Marzi, Chiara
Pachetti, Eva
Baccini, Michela
Barucci, Andrea
Colantonio, Sara
Gherardini, Luca
Lattavo, Lorenzo
Pascali, Maria Antonietta
Agostini, Simone
Miele, Vittorio
author_sort Bertelli, Elena
collection PubMed
description Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.
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spelling pubmed-87927452022-01-28 Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI Bertelli, Elena Mercatelli, Laura Marzi, Chiara Pachetti, Eva Baccini, Michela Barucci, Andrea Colantonio, Sara Gherardini, Luca Lattavo, Lorenzo Pascali, Maria Antonietta Agostini, Simone Miele, Vittorio Front Oncol Oncology Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability. Frontiers Media S.A. 2022-01-13 /pmc/articles/PMC8792745/ /pubmed/35096605 http://dx.doi.org/10.3389/fonc.2021.802964 Text en Copyright © 2022 Bertelli, Mercatelli, Marzi, Pachetti, Baccini, Barucci, Colantonio, Gherardini, Lattavo, Pascali, Agostini and Miele https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Bertelli, Elena
Mercatelli, Laura
Marzi, Chiara
Pachetti, Eva
Baccini, Michela
Barucci, Andrea
Colantonio, Sara
Gherardini, Luca
Lattavo, Lorenzo
Pascali, Maria Antonietta
Agostini, Simone
Miele, Vittorio
Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI
title Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI
title_full Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI
title_fullStr Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI
title_full_unstemmed Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI
title_short Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI
title_sort machine and deep learning prediction of prostate cancer aggressiveness using multiparametric mri
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8792745/
https://www.ncbi.nlm.nih.gov/pubmed/35096605
http://dx.doi.org/10.3389/fonc.2021.802964
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