Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review
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IMAGE & SIGNAL PROCESSING
Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review ˜ Manuel R.S. Tavares2 Gonc¸alo Almeida1 · Joao Received: 27 May 2020 / Accepted: 5 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Computer vision · Medical imaging · Machine learning · Segmentation · Radiotherapy
Introduction Prostate cancer is the most prevalent non-cutaneous cancer and the second leading cause of cancer death in men. It is estimated that about 1 in 9 men will be diagnosed with prostate cancer in their lifetime [1, 2]. However, with
This article belongs to the Topical Collection: Image & Signal Processing Jo˜ao Manuel R.S. Tavares
[email protected] Gonc¸alo Almeida [email protected] 1
Instituto de Ciˆencia e Inovac¸a˜ o em Engenharia Mecˆanica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
2
Instituto de Ciˆencia e Inovac¸a˜ o em Engenharia Mecˆanica e Engenharia Industrial, Departamento de Engenharia Mecˆanica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
appropriate treatment, the 5-year survival rate is 98.2% [3, 4]. Radiation Therapy (RT) is a fundamental part of modern cancer care, serving more than 1.5 million patients worldwide [5], and the demand is projected to increase by 16% by 2025 [6]. It is considered that half of all cancer patients would benefit from radiotherapy in the course of their disease, but in Eur
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