Machine learning applications in prostate cancer magnetic resonance imaging

With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI). After defining the difference between ML and classical rule-based algorithms and the distinction among supervised, unsupervi...

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Vydáno v:European radiology experimental Ročník 3; číslo 1; s. 35 - 8
Hlavní autoři: Cuocolo, Renato, Cipullo, Maria Brunella, Stanzione, Arnaldo, Ugga, Lorenzo, Romeo, Valeria, Radice, Leonardo, Brunetti, Arturo, Imbriaco, Massimo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 07.08.2019
Springer Nature B.V
SpringerOpen
Témata:
Artificial intelligence
Diagnostic Radiology
Humans
Imaging
Internal Medicine
Interventional Radiology
Machine Learning
Magnetic Resonance Imaging
Male
Medicine
Medicine & Public Health
Narrative Review
Neuroradiology
NMR
Nuclear magnetic resonance
Prostate
Prostate cancer
Prostatic neoplasms
Prostatic Neoplasms - diagnostic imaging
Radiology
Radiomics
Ultrasound
Prostatic neoplasms
Magnetic resonance imaging
Prostate
Machine learning
Radiomics
ISSN:2509-9280, 2509-9280
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Shrnutí:With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI). After defining the difference between ML and classical rule-based algorithms and the distinction among supervised, unsupervised and reinforcement learning, we explain the characteristic of deep learning (DL), a particular new type of ML, including its structure mimicking human neural networks and its ‘black box’ nature. Differences in the pipeline for applying ML and DL to prostate MRI are highlighted. The following potential clinical applications in different settings are outlined, many of them based only on MRI-unenhanced sequences: gland segmentation; assessment of lesion aggressiveness to distinguish between clinically significant and indolent cancers, allowing for active surveillance; cancer detection/diagnosis and localisation (transition versus peripheral zone, use of prostate imaging reporting and data system (PI-RADS) version 2), reading reproducibility, differentiation of cancers from prostatitis benign hyperplasia; local staging and pre-treatment assessment (detection of extraprostatic disease extension, planning of radiation therapy); and prediction of biochemical recurrence. Results are promising, but clinical applicability still requires more robust validation across scanner vendors, field strengths and institutions.
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ISSN:2509-9280
2509-9280
DOI:10.1186/s41747-019-0109-2