Streamlining Thoracic Radiotherapy Quality assurance: One-Class Classification for Automated OAR Contour Assessment Article Swipe
Yihao Zhao
,
Cuiyun Yuan
,
Ying Liang
,
Yang Li
,
Chunxia Li
,
Man Zhao
,
Jun Hu
,
Ningze Zhong
,
Wei Liu
,
Chenbin Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1177/15330338251345895
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1177/15330338251345895
Purpose Automating quality assurance (QA) for contours generated by automatic algorithms is critical in radiotherapy treatment planning. Manual QA is tedious, time-consuming, and prone to subjective experiences. Automatic segmentation reduces physician workload and improves consistency. However, an effective QA process for these automatic contours remains an unmet need in clinical practice. Materials and Methods The patient data used in this study was derived from the AAPM Thoracic Auto-Segmentation Challenge dataset, including left and right lungs, heart, esophagus, and spinal cord. Two groups of organ-at-risk (OAR) were generated. A ResNet-152 network was used as a feature extractor, and a one-class support vector machine (OC-SVM) was employed to classify contours as ‘high’ or ‘low’ quality. To evaluate the generalizability, we generated low-quality contours using translation and resizing techniques and assessed correlations between detection limits and metrics such as volume, Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD). Results The proposed OC-SVM model outperformed binary classifiers n metrics such as balanced accuracy and area under the receiver operating characteristic curve (AUC) . It demonstrated superior performance in detecting various types of contour errors while maintaining high interpretability. Strong correlations were observed between detection limits and contour metrics. Conclusion Our proposed model integrates an attention mechanism with a one-class classification framework to automate QA for OAR delineations. This approach effectively detects diverse types of contour errors with high accuracy, significantly reducing the burden on physicians during radiotherapy planning.
Related Topics
Concepts
Artificial intelligence
Computer science
Quality assurance
Interpretability
Segmentation
Support vector machine
Pattern recognition (psychology)
Hausdorff distance
Receiver operating characteristic
Similarity (geometry)
Feature (linguistics)
Metric (unit)
Machine learning
Medicine
Image (mathematics)
Operations management
Linguistics
Economics
External quality assessment
Philosophy
Pathology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/15330338251345895
- OA Status
- gold
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410605183
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410605183Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/15330338251345895Digital Object Identifier
- Title
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Streamlining Thoracic Radiotherapy Quality assurance: One-Class Classification for Automated OAR Contour AssessmentWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-01Full publication date if available
- Authors
-
Yihao Zhao, Cuiyun Yuan, Ying Liang, Yang Li, Chunxia Li, Man Zhao, Jun Hu, Ningze Zhong, Wei Liu, Chenbin LiuList of authors in order
- Landing page
-
https://doi.org/10.1177/15330338251345895Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1177/15330338251345895Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Quality assurance, Interpretability, Segmentation, Support vector machine, Pattern recognition (psychology), Hausdorff distance, Receiver operating characteristic, Similarity (geometry), Feature (linguistics), Metric (unit), Machine learning, Medicine, Image (mathematics), Operations management, Linguistics, Economics, External quality assessment, Philosophy, PathologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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32Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.‘low’ | 111 |
| abstract_inverted_index.Automating | 1 |
| abstract_inverted_index.Conclusion | 199 |
| abstract_inverted_index.ResNet-152 | 88 |
| abstract_inverted_index.algorithms | 10 |
| abstract_inverted_index.esophagus, | 76 |
| abstract_inverted_index.extractor, | 95 |
| abstract_inverted_index.generated. | 86 |
| abstract_inverted_index.integrates | 203 |
| abstract_inverted_index.physicians | 235 |
| abstract_inverted_index.similarity | 138 |
| abstract_inverted_index.subjective | 25 |
| abstract_inverted_index.techniques | 125 |
| abstract_inverted_index.‘high’ | 109 |
| abstract_inverted_index.classifiers | 157 |
| abstract_inverted_index.coefficient | 139 |
| abstract_inverted_index.effectively | 220 |
| abstract_inverted_index.low-quality | 119 |
| abstract_inverted_index.maintaining | 186 |
| abstract_inverted_index.performance | 177 |
| abstract_inverted_index.translation | 122 |
| abstract_inverted_index.consistency. | 34 |
| abstract_inverted_index.correlations | 128, 190 |
| abstract_inverted_index.demonstrated | 175 |
| abstract_inverted_index.experiences. | 26 |
| abstract_inverted_index.outperformed | 155 |
| abstract_inverted_index.radiotherapy | 14, 237 |
| abstract_inverted_index.segmentation | 28 |
| abstract_inverted_index.delineations. | 217 |
| abstract_inverted_index.organ-at-risk | 83 |
| abstract_inverted_index.significantly | 230 |
| abstract_inverted_index.characteristic | 170 |
| abstract_inverted_index.classification | 210 |
| abstract_inverted_index.time-consuming, | 21 |
| abstract_inverted_index.Auto-Segmentation | 67 |
| abstract_inverted_index.generalizability, | 116 |
| abstract_inverted_index.interpretability. | 188 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.18841853 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |