Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review

• Tipo: Outra Publicação em Revista Científica Internacional
• Data: 2020-09

Autores

Gonçalo Almeida (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Journal of Medical Systems

Vol. 44

Páginas: 1-15

ISSN: 0148-5598

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus - 3 Citações

Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências médicas e da saúde

Outras Informações

ID Authenticus: P-00S-NJE

DOI: 10.1007/s10916-020-01641-3

Abstract (EN): Radiation oncology for prostate cancer is important as it can decrease the morbidity and mortality associated with this disease. Planning for this modality of treatment is both fundamental, time-consuming and prone to human-errors, leading to potentially avoidable delays in start of treatment. A fundamental step in radiotherapy planning is contouring of radiation targets, where medical specialists contouring, i.e., segment, the boundaries of the structures to be irradiated. Automating this step can potentially lead to faster treatment planning without a decrease in quality, while increasing time available to physicians and also more consistent treatment results. This can be framed as an image segmentation task, which has been studied for many decades in the fields of Computer Vision and Machine Learning. With the advent of Deep Learning, there have been many proposals for different network architectures achieving high performance levels. In this review, we searched the literature for those methods and describe them briefly, grouping those based on Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). This is a booming field, evidenced by the date of the publications found. However, most publications use data from a very limited number of patients, which presents an obstacle to deep learning models training. Although the performance of the models has achieved very satisfactory results, there is still room for improvement, and there is arguably a long way before these models can be used safely and effectively in clinical practice.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15