Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617

Thor, Maria; Apte, Aditya; Haq, Rabia; Iyer, Aditi; LoCastro, Eve; Deasy, Joseph O.

doi:10.1016/j.ijrobp.2020.11.011

Cited by 42 publications

(38 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, in clinical trials including radiotherapy, standardization of treatment is problematic because of the variability in delineating the target and OARs [ 18 ]. In the RTOG 0617 trial [ 19 ], a radiation dose-escalation trial of non-small cell lung cancer, an analysis using deep-learning segmented hearts revealed that the actual heart doses were higher than originally reported owing to inconsistent and insufficient manual heart segmentation. Our results demonstrated that the ACS could solve this issue.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy

et al. 2021

View full text Add to dashboard Cite

Purpose To study the performance of a proposed deep learning-based autocontouring system in delineating organs at risk (OARs) in breast radiotherapy with a group of experts. Methods Eleven experts from two institutions delineated nine OARs in 10 cases of adjuvant radiotherapy after breast-conserving surgery. Autocontours were then provided to the experts for correction. Overall, 110 manual contours, 110 corrected autocontours, and 10 autocontours of each type of OAR were analyzed. The Dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to compare the degree of agreement between the best manual contour (chosen by an independent expert committee) and each autocontour, corrected autocontour, and manual contour. Higher DSCs and lower HDs indicated a better geometric overlap. The amount of time reduction using the autocontouring system was examined. User satisfaction was evaluated using a survey. Results Manual contours, corrected autocontours, and autocontours had a similar accuracy in the average DSC value (0.88 vs. 0.90 vs. 0.90). The accuracy of autocontours ranked the second place, based on DSCs, and the first place, based on HDs among the manual contours. Interphysician variations among the experts were reduced in corrected autocontours, compared to variations in manual contours (DSC: 0.89–0.90 vs. 0.87–0.90; HD: 4.3–5.8 mm vs. 5.3–7.6 mm). Among the manual delineations, the breast contours had the largest variations, which improved most significantly with the autocontouring system. The total mean times for nine OARs were 37 min for manual contours and 6 min for corrected autocontours. The results of the survey revealed good user satisfaction. Conclusions The autocontouring system had a similar performance in OARs as that of the experts’ manual contouring. This system can be valuable in improving the quality of breast radiotherapy and reducing interphysician variability in clinical practice.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy

et al. 2021

View full text Add to dashboard Cite

show abstract

“…One of the advantages of using an automatic contouring model is that intra-and inter-observer variability is eliminated. This is a major benefit when compared to choosing a group of experts to contour a dataset because systematic contour differences between experts might be reflected in systematic differences in the dosimetric parameters and all other computations made using those contours [31]. Moreover, these algorithms can be easily deployed in any given institute, avoiding the time and monetary costs of manual contouring following the same atlas and contouring directions.…”

Section: Discussionmentioning

confidence: 99%

Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images: Dosimetric validation and reader study based clinical acceptability testing

Fernandes

Bussink²,

Stam

et al. 2021

Radiotherapy and Oncology

View full text Add to dashboard Cite

Background and purpose: Large radiotherapy (RT) planning imaging datasets with consistently contoured cardiovascular structures are essential for robust cardiac radiotoxicity research in thoracic cancers. This study aims to develop and validate a highly accurate automatic contouring model for the heart, cardiac chambers, and great vessels for RT planning computed tomography (CT) images that can be used for dose-volume parameter estimation. Materials and methods: A neural network model was trained using a dataset of 127 expertly contoured planning CT images from RT treatment of locally advanced non-small-cell lung cancer (NSCLC) patients. Evaluation of geometric accuracy and quality of dosimetric parameter estimation was performed on 50 independent scans with contrast and without contrast enhancement. The model was further evaluated regarding the clinical acceptability of the contours in 99 scans randomly sampled from the RTOG-0617 dataset by three experienced radiation oncologists. Results: Median surface dice at 3 mm tolerance for all dedicated thoracic structures was 90% in the test set. Median absolute difference between mean dose computed with model contours and expert contours was 0.45 Gy averaged over all structures. The mean clinical acceptability rate by majority vote in the RTOG-0617 scans was 91%. Conclusion: This model can be used to contour the heart, cardiac chambers, and great vessels in large datasets of RT planning thoracic CT images accurately, quickly, and consistently. Additionally, the model can be used as a time-saving tool for contouring in clinic practice.

show abstract

“…Automation not only leads to efficiency gains but also, generally, leads to more standardized/ontological data collection, which in turn may lead to better prognostication and prediction. As an example, a recent paper using AI-based automated heart segmentation led to a better prediction of dose-related cardiac toxicity in a pivotal trial on advanced lung cancer patients (RTOG 0617) compared to human heart segmentations, likely due to interobserver variation [ 24 ].…”

Section: Unmet Clinical Needs In the Management Of La-nsclc Patients: Role Of Imaging Adaptive Rt And Biomarkersmentioning

confidence: 99%

Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers

Hope

Verduin

Choudhury

et al. 2021

Cancers

View full text Add to dashboard Cite

Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients’ data (imaging, electronic health records, patients’ reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.

show abstract

Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617

Cited by 42 publications

References 19 publications

Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy

Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy

Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images: Dosimetric validation and reader study based clinical acceptability testing

Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers

Contact Info

Product

Resources

About