External validation of deep learning-based contouring of head and neck organs at risk

Brunenberg, E.; Steinseifer, Isabell K.; Bosch, Sven van den; Kaanders, Johannes H.A.M.; Brouwer, Charlotte L.; Gooding, Mark J.; Elmpt, Wouter van; Monshouwer, R.

doi:10.1016/j.phro.2020.06.006

Cited by 45 publications

(46 citation statements)

References 29 publications

(19 reference statements)

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“…Deep learning-based auto-segmentation has been widely investigated in head & neck, lung, and prostate cancers and has demonstrated clinically relevant impact with regard to saving time and mitigating inter-observer variability [23,24,34]. Although several studies have reported the feasibility of the deep learning-based approach for the breast, training and testing has only been performed for ipsilateral breast CTVs [35,36].…”

Section: Discussionmentioning

confidence: 99%

“…In radiation oncology, there are numerous areas in which AI is applicable, such as target and normal tissue segmentation, dose optimization, decision support systems, application of predictive models, and quality assurance [20][21][22]. Auto-contouring tools have been adopted by an increasing number of physicians and have resulted in improved efficiency, particularly for OARs in head and neck cancer and target volume in prostate cancer [23,24]. As there is a paucity of data regarding the autosegmentation of target volumes and OARs in breast RT planning, we attempted to train a deep learning-based auto-segmentation model for target volumes and OARs for breast cancer and evaluated its clinical utility from a clinician's perspective.…”

Section: Introductionmentioning

confidence: 99%

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Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery

et al. 2021

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Background In breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) segmentation for treatment planning increases physicians’ workload and inter-physician variability considerably. In this study, we evaluated the potential benefits of deep learning-based auto-segmented contours by comparing them to manually delineated contours for breast cancer patients. Methods CTVs for bilateral breasts, regional lymph nodes, and OARs (including the heart, lungs, esophagus, spinal cord, and thyroid) were manually delineated on planning computed tomography scans of 111 breast cancer patients who received breast-conserving surgery. Subsequently, a two-stage convolutional neural network algorithm was used. Quantitative metrics, including the Dice similarity coefficient (DSC) and 95% Hausdorff distance, and qualitative scoring by two panels from 10 institutions were used for analysis. Inter-observer variability and delineation time were assessed; furthermore, dose-volume histograms and dosimetric parameters were also analyzed using another set of patient data. Results The correlation between the auto-segmented and manual contours was acceptable for OARs, with a mean DSC higher than 0.80 for all OARs. In addition, the CTVs showed favorable results, with mean DSCs higher than 0.70 for all breast and regional lymph node CTVs. Furthermore, qualitative subjective scoring showed that the results were acceptable for all CTVs and OARs, with a median score of at least 8 (possible range: 0–10) for (1) the differences between manual and auto-segmented contours and (2) the extent to which auto-segmentation would assist physicians in clinical practice. The differences in dosimetric parameters between the auto-segmented and manual contours were minimal. Conclusions The feasibility of deep learning-based auto-segmentation in breast RT planning was demonstrated. Although deep learning-based auto-segmentation cannot be a substitute for radiation oncologists, it is a useful tool with excellent potential in assisting radiation oncologists in the future. Trial registration Retrospectively registered.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery

et al. 2021

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“…Supervised ML algorithms use training data with known input (predictors) and output (responses) values, to detect patterns and correlation through the learning process [7] , which can then be used to predict whether investigator contours “pass” or “fail” pre-trial outlining exercises. Whilst several studies have investigated the use of AI for auto-segmentation contouring in radiotherapy planning [8] , [9] , [10] , the use of ML to assess TV and OAR contour conformity is limited.…”

Section: Introductionmentioning

confidence: 99%

Automatic evaluation of contours in radiotherapy planning utilising conformity indices and machine learning

Terparia

Mir

Tsang

et al. 2020

Physics and Imaging in Radiation Oncology

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Background and purpose: Peer-review of Target Volume (TV) and Organ at Risk (OAR) contours in radiotherapy planning are typically conducted visually; this can be time consuming and subject to interobserver variation. This study investigated automatic evaluation of contouring using conformity indices and supervised machine learning. Methods: A total of 393 contours from 253 Stereotactic Ablative Body Radiotherapy (SABR) benchmark cases (adrenal gland, liver, pelvic lymph node and spine), delineated by 132 clinicians from 25 centres, were visually evaluated for conformity against gold standard contours. Contours were scored as "pass" or "fail" on visual peer review and six Conformity Indices (CIs) were applied. CI values were mapped to pass/fail scores for each contour and used to train supervised machine learning models. A 5-fold cross validation method was employed to determine the predictive accuracies of each model. Results: The stomach structure produced models with the highest predictive accuracy overall (96% using Support Vector Machine and Ensemble models), whilst the liver GTV produced models with the lowest predictive accuracy (76% using Logistic Regression). Predictive accuracies across all models ranged from 68-96% (68-87% for TV and 71-96% for OARs). Conclusions: Although a final visual review by an experienced clinician is still required, the automatic contour evaluation method could reduce the time for benchmark case reviews by identifying gross contouring errors. This method could be successfully implemented to support departmental training and the continuous assessment of outlining for clinical staff in the peer-review process, to reduce interobserver variability in contouring and improve interpretation of radiological anatomy.

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“…On the other side, errors in image acquisition and quantification impact directly on the accuracy of radiotherapy delivery. Two papers exploiting technological advancements in imaging to develop new and more automated strategies for OAR and metastatic lymph node contouring have recently been published in our journal [2] , [3] .…”

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confidence: 99%

“…Such independent validation is of crucial importance to ensure freedom from dependencies on institutional image acquisition settings. Further in this issue, Gurney-Champion et al combined 3D CNN models with quantitative information from diffusion-weighted MR images to achieve automatic contouring of metastatic lymph nodes in patients with head and neck cancer [3] . This study aimed at developing a highly reproducible method for lymph node segmentation in order to objectively analyze sequential information from quantitative information assessed during each fraction of radiotherapy delivery.…”

mentioning

confidence: 99%

Future directions on the merge of quantitative imaging and artificial intelligence in radiation oncology

Redalen

Thorwarth

2020

Physics and Imaging in Radiation Oncology

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External validation of deep learning-based contouring of head and neck organs at risk

Cited by 45 publications

References 29 publications

Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery

Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery

Automatic evaluation of contours in radiotherapy planning utilising conformity indices and machine learning

Future directions on the merge of quantitative imaging and artificial intelligence in radiation oncology

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