Radiation oncology (London, England), 2021-10, Vol.16 (1), p.203-203, Article 203
2021
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Details
- Autor(en) / Beteiligte
- Titel
- Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy
- Ist Teil von
- Radiation oncology (London, England), 2021-10, Vol.16 (1), p.203-203, Article 203
- Ort / Verlag
- England: BioMed Central Ltd
- Erscheinungsjahr
- 2021
- Quelle
- MEDLINE
- Beschreibungen/Notizen
- 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. 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. 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. 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 1748-717XeISSN: 1748-717XDOI: 10.1186/s13014-021-01923-1Titel-ID: cdi_doaj_primary_oai_doaj_org_article_6271a3efee73416d825437dfc3e439ef
- Format
- –
- Schlagworte
- Accuracy, Autocontouring, Breast, Breast cancer, Breast Neoplasms - pathology, Breast Neoplasms - radiotherapy, Cancer therapies, Care and treatment, Contouring, Contours, CT imaging, Deep Learning, Dose-response relationship (Biochemistry), Esophagus, Female, Health aspects, Humans, Image Processing, Computer-Assisted - methods, Lungs, Machine learning, Medical imaging, Methods, Metric space, Neural networks, Observer Variation, Organs, Organs at risk, Organs at Risk - radiation effects, Performance evaluation, Polls & surveys, Radiation Oncologists - standards, Radiation therapy, Radiotherapy, Radiotherapy Dosage, Radiotherapy Planning, Computer-Assisted - methods, Radiotherapy, Adjuvant - methods, Radiotherapy, Intensity-Modulated - methods, Simulation, Spinal cord, Thyroid gland, User satisfaction, Variation