Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer

McIntosh, Chris; Conroy, Leigh; Tjong, Michael C.; Craig, Tim; Bayley, Andrew; Catton, Charles; Gospodarowicz, Mary K.; Helou, Joelle; Isfahanian, Naghmeh; Kong, Vickie; Lam, Tony K.T.; Raman, Srinivas; Warde, Padraig; Chung, Peter; Berlín, Alejandro; Purdie, Thomas

doi:10.1038/s41591-021-01359-w

Cited by 116 publications

(60 citation statements)

References 36 publications

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“…Recently, McIntosh et al . published a study about the clinical integration of an AI model for prostate cancer, including quantitative and qualitative review [ 21 ]. In two phases, a retrospective simulation and a prospective deployment study phase, 89% of plans generated by the AI model were deemed to be clinically acceptable, which is comparable to our results.…”

Section: Discussionmentioning

confidence: 99%

Clinical evaluation of two AI models for automated breast cancer plan generation

et al. 2022

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Background Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of the predicted dose distribution to a deliverable plan is lacking. This study evaluates two different, deliverable AI plans in terms of their clinical acceptability based on quantitative parameters and qualitative evaluation by four radiation oncologists. Methods For 20 left-sided node-negative breast cancer patients, treated with a prescribed dose of 40.05 Gy, using tangential beam intensity modulated radiotherapy, two model-based treatment plans were evaluated against the corresponding manual plan. The two models used were an in-house developed U-net model and a vendor-developed contextual atlas regression forest model (cARF). Radiation oncologists evaluated the clinical acceptability of each blinded plan and ranked plans according to preference. Furthermore, a comparison with the manual plan was made based on dose volume histogram parameters, clinical evaluation criteria and preparation time. Results The U-net model resulted in a higher average and maximum dose to the PTV (median difference 0.37 Gy and 0.47 Gy respectively) and a slightly higher mean heart dose (MHD) (0.01 Gy). The cARF model led to higher average and maximum doses to the PTV (0.30 and 0.39 Gy respectively) and a slightly higher MHD (0.02 Gy) and mean lung dose (MLD, 0.04 Gy). The maximum MHD/MLD difference was ≤ 0.5 Gy for both AI plans. Regardless of these dose differences, 90–95% of the AI plans were considered clinically acceptable versus 90% of the manual plans. Preferences varied between the radiation oncologists. Plan preparation time was comparable between the U-net model and the manual plan (287 s vs 253 s) while the cARF model took longer (471 s). When only considering user interaction, plan generation time was 121 s for the cARF model and 137 s for the U-net model. Conclusions Two AI models were used to generate deliverable plans for breast cancer patients, in a time-efficient manner, requiring minimal user interaction. Although the AI plans resulted in slightly higher doses overall, radiation oncologists considered 90–95% of the AI plans clinically acceptable.

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Section: Discussionmentioning

confidence: 99%

Clinical evaluation of two AI models for automated breast cancer plan generation

et al. 2022

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“…Recently Xia et al [20] presented a proof-of-concept study for a similar AI-based planning approach for rectal cancer, where 80% of the automatically contoured and planned cases would have been accepted without manual fine-tuning for clinical treatment. Nevertheless, as demonstrated recently by McIntosh et al [15] acceptance of AI-based plans during a retrospective testing phase and a prospective phase may be different, where plans are intended to be used for clinical real-life treatments. As the physicians were aware of the retrospective character, the acceptance of our proposed autonomous workflow might be more optimistic as compared to a prospective scenario.…”

Section: Discussionmentioning

confidence: 99%

“…This process is time-consuming and requires human interaction by an experienced user and hence quality can vary significantly [1]. Nevertheless, as process automation and the use of artificial intelligence (AI) for medical applications gained attention in recent years, several approaches were introduced to automate organ segmentation [2][3][4][5][6][7] or RT planning [8][9][10][11][12][13][14][15][16]. Some of these approaches are commercially available and were recently evaluated for clinical usage [9,[11][12][13][14][15][16].…”

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confidence: 99%

“…Nevertheless, as process automation and the use of artificial intelligence (AI) for medical applications gained attention in recent years, several approaches were introduced to automate organ segmentation [2][3][4][5][6][7] or RT planning [8][9][10][11][12][13][14][15][16]. Some of these approaches are commercially available and were recently evaluated for clinical usage [9,[11][12][13][14][15][16]. Furthermore, the potential of AI in healthcare has been discussed extensively [17][18][19].…”

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confidence: 99%

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Clinical evaluation of autonomous, unsupervised planning integrated in MR-guided radiotherapy for prostate cancer

Künzel¹,

Nachbar²,

Hagmüller³

et al. 2022

Radiotherapy and Oncology

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“…Due to the power of ML methods in developing accurate prediction systems that can identify complex and nonlinear patterns in different data types, they have recently been used to improve the healthcare systems in enhancing the diagnosis process and drug discovery [38,39], as well as in deciding a suitable treatment plan [40][41][42][43]. The term ML represents all traditional ML methods, such as the support vector machine (SVM), neural network (NN), trees, random forest (RF), and K-nearest neighbor (KNN), including deep learning (DL) methods, which are just NNs with a very deep structure.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients

Chato

Latifi

2021

JPM

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Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A novel radiomic feature extraction method is proposed and developed on the basis of volumetric and location information of brain tumor subregions extracted from MRI scans. This method is based on calculating the volumetric features from two brain sub-volumes obtained from the whole brain volume in MRI images using brain sectional planes (sagittal, coronal, and horizontal). Many experiments are conducted on the basis of various ML methods and combinations of feature extraction methods to develop the best OST system. In addition, the feature fusions of both radiomic and non-imaging features are examined to improve the accuracy of the prediction system. The best performance was achieved by the neural network and feature fusions.

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Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer

Cited by 116 publications

References 36 publications

Clinical evaluation of two AI models for automated breast cancer plan generation

Clinical evaluation of two AI models for automated breast cancer plan generation

Clinical evaluation of autonomous, unsupervised planning integrated in MR-guided radiotherapy for prostate cancer

Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients

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