Machine learning applications in radiation oncology: Current use and needs to support clinical implementation

Abstract

Background and purpose: The use of artificial intelligence (AI)/ machine learning (ML) applications in radiation oncology is emerging, however no clear guidelines on commissioning of ML-based applications exist. The purpose of this study was therefore to investigate the current use and needs to support implementation of ML-based applications in routine clinical practice. Materials and methods: A survey was conducted among medical physicists in radiation oncology, consisting of four parts: clinical applications (1), model training, acceptance and commissioning (2), quality assurance (QA) in clinical practice and General Data Protection Regulation (GDPR) (3), and need for education and guidelines (4). Survey answers of medical physicists of the same radiation oncology centre were treated as a separate unique responder in case reporting on different AI applications. Results: In total, 213 medical physicists from 202 radiation oncology centres were included in the analysis. Sixtynine percent (1 4 7) was using (37%) or preparing (32%) to use ML in clinic, mostly for contouring and treatment planning. In 86%, human observers were still involved in daily clinical use for quality check of the output of the ML algorithm. Knowledge on ethics, legislation and data sharing was limited and scattered among responders. Besides the need for (implementation) guidelines, training of medical physicists and larger databases containing multicentre data was found to be the top priority to accommodate the further introduction of ML in clinical practice. Conclusion: The results of this survey indicated the need for education and guidelines on the implementation and quality assurance of ML-based applications to benefit clinical introduction.

Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers

“…With increasing interest and uptake of machine learning applications and auto-segmentation in Radiation Oncology [ 3 ], literature to help promote and guide the commissioning and clinical implementation of these algorithms is becoming more readily available [ 5 ]. While machine learning auto-segmentation is widely hypothesized to be associated with workflow benefits and time savings, limited prospective data exists to confirm this claim.…”

“…Auto-segmentation solutions are frequently explored to alleviate workload pressures [ 1 ], and deep learning-based auto-segmentation is thought to provide improved results over atlas-based methods [ 2 ]. Despite its potential utility, deep learning-based auto-segmentation is not yet widely used in clinical practice [ 3 ]. One possible factor associated with the slow adoption is the current lack of knowledge and guidelines regarding the commissioning and implementation of such machine learning applications [ 3 ].…”

“…Despite its potential utility, deep learning-based auto-segmentation is not yet widely used in clinical practice [ 3 ]. One possible factor associated with the slow adoption is the current lack of knowledge and guidelines regarding the commissioning and implementation of such machine learning applications [ 3 ].…”

Training and Validation of Deep Learning-Based Auto-Segmentation Models for Lung Stereotactic Ablative Radiotherapy Using Retrospective Radiotherapy Planning Contours

“…With increasing interest in deep learning across Radiation Oncology (33,34), auto-segmentation solutions are frequently being explored for use in radiotherapy planning (7). This study adds to the limited literature available intended to aid in the development of these algorithms.…”

First experience of autonomous, un-supervised treatment planning integrated in adaptive MR-guided radiotherapy and delivered to a patient with prostate cancer

“…The process of radiotherapy (RT) treatment planning from simulation using computed tomography (CT) and/or magnetic resonance imaging (MRI) to the application of the first treatment fraction is currently a multi-step routine process requiring human interactions, which is fragmented and time-consuming and thus plan quality can vary significantly [1]. Recently, the potentials of artificial intelligence (AI) in healthcare to automatize, standardize and speed-up processes has been discussed extensively [2][3][4][5][6]. In RT, the automation of single workflow steps has recently been investigated by several groups, especially with respect to automatic contouring and plan optimization [7][8][9][10][11][12][13][14][15][16].…”