Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.
Gonçalo AlmeidaJoão Manuel R S TavaresPublished in: Journal of medical systems (2020)
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.
Keyphrases
- deep learning
- prostate cancer
- magnetic resonance imaging
- computed tomography
- machine learning
- artificial intelligence
- contrast enhanced
- clinical practice
- convolutional neural network
- end stage renal disease
- early stage
- radical prostatectomy
- big data
- endothelial cells
- high resolution
- chronic kidney disease
- newly diagnosed
- emergency department
- positron emission tomography
- ejection fraction
- mass spectrometry
- prognostic factors
- weight loss
- peritoneal dialysis
- magnetic resonance
- squamous cell carcinoma
- rectal cancer
- drug induced
- patient reported
- quality improvement
- virtual reality