Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer

Sande, Dennis van de; Sharabiani, M.; Bluemink, Hanneke; Kneepkens, Esther; Bakx, Nienke; Hagelaar, E.; Sangen, Maurice van der; Theuws, Jacqueline; Hurkmans, Coen

doi:10.1016/j.phro.2021.11.007

Cited by 23 publications

(13 citation statements)

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“…In this paper, we suggest a methodology of using machine learning to help patients and doctors identify the appropriate treatment plan. For the case study, we have used breast cancer (Reddy et al, 2018 ; Song et al, 2021 ; van de Sande et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Using machine learning for healthcare treatment planning

Dubey

Tiwari

Singh

et al. 2023

Front. Artif. Intell.

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We present a methodology for using machine learning for planning treatments. As a case study, we apply the proposed methodology to Breast Cancer. Most of the application of Machine Learning to breast cancer has been on diagnosis and early detection. By contrast, our paper focuses on applying Machine Learning to suggest treatment plans for patients with different disease severity. While the need for surgery and even its type is often obvious to a patient, the need for chemotherapy and radiation therapy is not as obvious to the patient. With this in mind, the following treatment plans were considered in this study: chemotherapy, radiation, chemotherapy with radiation, and none of these options (only surgery). We use real data from more than 10,000 patients over 6 years that includes detailed cancer information, treatment plans, and survival statistics. Using this data set, we construct Machine Learning classifiers to suggest treatment plans. Our emphasis in this effort is not only on suggesting the treatment plan but on explaining and defending a particular treatment choice to the patient.

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Section: Methodsmentioning

confidence: 99%

Using machine learning for healthcare treatment planning

Dubey

Tiwari

Singh

et al. 2023

Front. Artif. Intell.

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“…The objective is to forecast the ideal volumetric dose for a patient using CT images, target contours, and important OAR contours 10–14 . These dose predictions are then used by the planning team to identify DVH objectives, for use in dose mimicking algorithms, or for MLC sequence predictions 15–17 …”

Section: Introductionmentioning

confidence: 99%

Standardizing and improving dose predictions for head and neck cancers using complete sets of OAR contours

Buatti,

Kirby,

Stathakis

et al. 2023

Medical Physics

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BackgroundRadiotherapy dose predictions have been trained with data from previously treated patients of similar sites and prescriptions. However, clinical datasets are often inconsistent and do not contain the same number of organ at risk (OAR) structures. The effects of missing contour data in deep learning‐based dose prediction models have not been studied.PurposeThe purpose of this study was to investigate the impacts of incomplete contour sets in the context of deep learning‐based radiotherapy dose prediction models trained with clinical datasets and to introduce a novel data substitution method that utilizes automated contours for undefined structures.MethodsWe trained Standard U‐Nets and Cascade U‐Nets to predict the volumetric dose distributions of patients with head and neck cancers (HNC) using three input variations to evaluate the effects of missing contours, as well as a novel data substitution method. Each architecture was trained with the original contour (OC) inputs, which included missing information, hybrid contour (HC) inputs, where automated OAR contours generated in software were substituted for missing contour data, and automated contour (AC) inputs containing only automated OAR contours. 120 HNC treatments were used for model training, 30 were used for validation and tuning, and 44 were used for evaluation and testing. Model performance and accuracy were evaluated with global whole body dose agreement, PTV coverage accuracy, and OAR dose agreement. The differences in these values between dataset variations were used to determine the effects of missing data and automated contour substitutions.ResultsAutomated contours used as substitutions for missing data were found to improve dose prediction accuracy in the Standard U‐Net and Cascade U‐Net, with a statistically significant difference in some global metrics and/or OAR metrics. For both models, PTV coverage between input variations was unaffected by the substitution technique. Automated contours in HC and AC datasets improved mean dose accuracy for some OAR contours, including the mandible and brainstem, with a greater improvement seen with HC datasets. Global dose metrics, including mean absolute error, mean error, and percent error were different for the Standard U‐Net but not for the Cascade U‐Net.ConclusionAutomated contours used as a substitution for contour data improved prediction accuracy for some but not all dose prediction metrics. Compared to the Standard U‐Net models, the Cascade U‐Net achieved greater precision.

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“…These activities have resulted in dedicated thematic special issues on radiotherapy physics and imaging topics [3] , [4] as well as conference highlight issues reporting in paper form the very best physics and imaging research that was presented at the conference [5] , [6] , [7] , [8] . Also this editorial accompanies the special issue of physics and imaging highlight papers from the ESTRO 2021 conference, with important papers from across our field already published [9] , [10] , and several other strong papers soon to appear.…”

mentioning

confidence: 99%