C M L Zegers1, J Posch2, A Traverso2, D Eekers2, A A Postma3, W Backes3, A Dekker2, W van Elmpt2. 1. Department of Radiation Oncology (Maastro), Maastricht University Medical Center+, GROW School for Developmental Biology and Oncology, Maastricht, the Netherlands. Electronic address: karen.zegers@maastro.nl. 2. Department of Radiation Oncology (Maastro), Maastricht University Medical Center+, GROW School for Developmental Biology and Oncology, Maastricht, the Netherlands. 3. Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, MHeNs School for Mental Health and Neuroscience, Maastricht, the Netherlands.
Abstract
PURPOSE: Magnetic Resonance Imaging (MRI) provides an essential contribution in the screening, detection, diagnosis, staging, treatment and follow-up in patients with a neurological neoplasm. Deep learning (DL), a subdomain of artificial intelligence has the potential to enhance the characterization, processing and interpretation of MRI images. The aim of this review paper is to give an overview of the current state-of-art usage of DL in MRI for neuro-oncology. METHODS: We reviewed the Pubmed database by applying a specific search strategy including the combination of MRI, DL, neuro-oncology and its corresponding search terminologies, by focussing on Medical Subject Headings (Mesh) or title/abstract appearance. The original research papers were classified based on its application, into three categories: technological innovation, diagnosis and follow-up. RESULTS: Forty-one publications were eligible for review, all were published after the year 2016. The majority (N = 22) was assigned to technological innovation, twelve had a focus on diagnosis and seven were related to patient follow-up. Applications ranged from improving the acquisition, synthetic CT generation, auto-segmentation, tumor classification, outcome prediction and response assessment. The majority of publications made use of standard (T1w, cT1w, T2w and FLAIR imaging), with only a few exceptions using more advanced MRI technologies. The majority of studies used a variation on convolution neural network (CNN) architectures. CONCLUSION: Deep learning in MRI for neuro-oncology is a novel field of research; it has potential in a broad range of applications. Remaining challenges include the accessibility of large imaging datasets, the applicability across institutes/vendors and the validation and implementation of these technologies in clinical practise.
PURPOSE: Magnetic Resonance Imaging (MRI) provides an essential contribution in the screening, detection, diagnosis, staging, treatment and follow-up in patients with a neurological neoplasm. Deep learning (DL), a subdomain of artificial intelligence has the potential to enhance the characterization, processing and interpretation of MRI images. The aim of this review paper is to give an overview of the current state-of-art usage of DL in MRI for neuro-oncology. METHODS: We reviewed the Pubmed database by applying a specific search strategy including the combination of MRI, DL, neuro-oncology and its corresponding search terminologies, by focussing on Medical Subject Headings (Mesh) or title/abstract appearance. The original research papers were classified based on its application, into three categories: technological innovation, diagnosis and follow-up. RESULTS: Forty-one publications were eligible for review, all were published after the year 2016. The majority (N = 22) was assigned to technological innovation, twelve had a focus on diagnosis and seven were related to patient follow-up. Applications ranged from improving the acquisition, synthetic CT generation, auto-segmentation, tumor classification, outcome prediction and response assessment. The majority of publications made use of standard (T1w, cT1w, T2w and FLAIR imaging), with only a few exceptions using more advanced MRI technologies. The majority of studies used a variation on convolution neural network (CNN) architectures. CONCLUSION: Deep learning in MRI for neuro-oncology is a novel field of research; it has potential in a broad range of applications. Remaining challenges include the accessibility of large imaging datasets, the applicability across institutes/vendors and the validation and implementation of these technologies in clinical practise.
Authors: Elise Marechal; Adrien Jaugey; Georges Tarris; Michel Paindavoine; Jean Seibel; Laurent Martin; Mathilde Funes de la Vega; Thomas Crepin; Didier Ducloux; Gilbert Zanetta; Sophie Felix; Pierre Henri Bonnot; Florian Bardet; Luc Cormier; Jean-Michel Rebibou; Mathieu Legendre Journal: Clin J Am Soc Nephrol Date: 2021-12-03 Impact factor: 8.237