Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network

Liu, Zhikai; Liu, FangJie; Chen, Wanqi; Tao, Yinjie; Liu, Xia; Zhang, Fuquan; Shen, Jing; Guan, Hui; Zhen, Hongnan; Wang, Shaobin; Chen, Qi; Chen, Yu; Hou, Xiaorong

doi:10.2147/cmar.s330249

Cited by 15 publications

(8 citation statements)

References 35 publications

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“…Performance was comparable between CTV right and left breast in studies that reported CTV performance in both breasts. 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 Dai et al performed a multi-institutional study that reported better performance for DLAS models on planning CT scans compared with scanning CT scans. 21 Lymph node CTVs are the most difficult fields for DLAS models.…”

Section: Resultsmentioning

confidence: 99%

Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions

Matoska,

Patel,

Liu

et al. 2024

Advances in Radiation Oncology

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Section: Resultsmentioning

confidence: 99%

Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions

Matoska,

Patel,

Liu

et al. 2024

Advances in Radiation Oncology

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“…Similar results were reported for the left breast (DSC = 0.9 ± 0.03, HD95 = 4.3 ± 1.7 mm). Liu et al (2021) proposed U-ResNet to improve the efficiency and accuracy of breast CTV and OARs delineation compared to those generated by U-Net. To avoid the vanishing gradients of deep convolutional networks, ResNet was incorporated as the encoder.…”

Section: Discussionmentioning

confidence: 99%

A deep learning approach for automatic delineation of clinical target volume in stereotactic partial breast irradiation (S-PBI)

et al. 2023

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Accurate and efficient delineation of the clinical target volume (CTV) is of utmost significance in post-operative breast cancer radiotherapy. However, CTV delineation is challenging as the exact extent of microscopic disease encompassed by CTV is not visualizable in radiological images and remains uncertain. We proposed to mimic physicians’ contouring practice for CTV segmentation in Stereotactic Partial Breast Irradiation (S-PBI) where CTV is derived from tumor bed volume (TBV) via a margin expansion followed by correcting the extensions for anatomical barriers of tumor invasion (e.g., skin, chest wall). We proposed a deep-learning model, where CT images and the corresponding TBV masks formed a multi-channel input for a 3D U-Net based architecture. The design guided the model to encode the location-related image features and directed the network to focus on TBV to initiate CTV segmentation. Gradient weighted Class Activation Map (Grad-CAM) visualizations of the model predictions revealed that the extension rules and geometric/anatomical boundaries were learnt during model training to assist the network to limit the expansion to a certain distance from the chest wall and the skin. We retrospectively collected 175 prone CT images from 35 post-operative breast cancer patients who received 5-fraction partial breast irradiation (PBI) regimen on GammaPod. The 35 patients were randomly split into training (25), validation (5) and test (5) sets. Our model achieved mean (standard deviation) of 0.94 (0.02), 2.46 (0.5) mm, and 0.53 (0.14) mm for Dice similarity coefficient, 95th percentile Hausdorff distance, and average symmetric surface distance respectively on the test set. The results are promising for improving the efficiency and accuracy of CTV delineation process during on-line treatment planning procedure.

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“…As stated, artificial intelligence profoundly modified the exercise of radiation oncologists. With numerous companies and research teams being involved in this field [ 16 , 17 , 18 , 19 , 20 ], the viewpoint of future clinicians was needed.…”

Section: Discussionmentioning

confidence: 99%

Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective

Bourbonne

Laville

Wagneur

et al. 2023

Cancers

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Introduction: Segmentation of organs at risk (OARs) and target volumes need time and precision but are highly repetitive tasks. Radiation oncology has known tremendous technological advances in recent years, the latest being brought by artificial intelligence (AI). Despite the advantages brought by AI for segmentation, some concerns were raised by academics regarding the impact on young radiation oncologists’ training. A survey was thus conducted on young french radiation oncologists (ROs) by the SFjRO (Société Française des jeunes Radiothérapeutes Oncologues). Methodology: The SFjRO organizes regular webinars focusing on anatomical localization, discussing either segmentation or dosimetry. Completion of the survey was mandatory for registration to a dosimetry webinar dedicated to head and neck (H & N) cancers. The survey was generated in accordance with the CHERRIES guidelines. Quantitative data (e.g., time savings and correction needs) were not measured but determined among the propositions. Results: 117 young ROs from 35 different and mostly academic centers participated. Most centers were either already equipped with such solutions or planning to be equipped in the next two years. AI segmentation software was mostly useful for H & N cases. While for the definition of OARs, participants experienced a significant time gain using AI-proposed delineations, with almost 35% of the participants saving between 50–100% of the segmentation time, time gained for target volumes was significantly lower, with only 8.6% experiencing a 50–100% gain. Contours still needed to be thoroughly checked, especially target volumes for some, and edited. The majority of participants suggested that these tools should be integrated into the training so that future radiation oncologists do not neglect the importance of radioanatomy. Fully aware of this risk, up to one-third of them even suggested that AI tools should be reserved for senior physicians only. Conclusions: We believe this survey on automatic segmentation to be the first to focus on the perception of young radiation oncologists. Software developers should focus on enhancing the quality of proposed segmentations, while young radiation oncologists should become more acquainted with these tools.

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Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network

Cited by 15 publications

References 35 publications

Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions

Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions

A deep learning approach for automatic delineation of clinical target volume in stereotactic partial breast irradiation (S-PBI)

Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective

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