A blind randomized validated convolutional neural network for auto‐segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy
Abstract Background Delineation of clinical target volume (CTV) for radiotherapy is a time‐consuming and labor‐intensive work. This study aims to propose a novel convolutional neural network (CNN)‐based model for fast auto‐segmentation of CTV. To evaluate its performance and clinical utility, a blin...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Cancer Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/cam4.4441 |
| _version_ | 1819096086470459392 |
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| author | Yijun Wu Kai Kang Chang Han Shaobin Wang Qi Chen Yu Chen Fuquan Zhang Zhikai Liu |
| author_facet | Yijun Wu Kai Kang Chang Han Shaobin Wang Qi Chen Yu Chen Fuquan Zhang Zhikai Liu |
| author_sort | Yijun Wu |
| collection | DOAJ |
| description | Abstract Background Delineation of clinical target volume (CTV) for radiotherapy is a time‐consuming and labor‐intensive work. This study aims to propose a novel convolutional neural network (CNN)‐based model for fast auto‐segmentation of CTV. To evaluate its performance and clinical utility, a blind randomized validation method was used. Methods Our proposed model was based on the generally accepted U‐Net architecture using computed tomography slices with CTV contours delineated by experienced radiation clinicians from 135 rectal patients receiving neoadjuvant radiotherapy. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (95HD) were used to measure segmentation performance. The validated dataset of additional 20 patients for clinical evaluation by 10 experienced oncology clinicians from 7 centers was randomly and blindly divided into two groups for clinicians' scoring and Turing test, respectively. Second evaluation was performed with different randomization after 2 weeks. Results The mean DSC and 95HD values of the proposed model were 0.90 ± 0.02 and 8.11 ± 1.93 mm for CTV of rectal cancer patients, respectively. The average time for automatic segmentation in the validation groups was 15 s per patient. By clinicians' scoring, the AI model performed better than manually delineating, though the differences were not significant (Week 0: 2.59 vs. 2.52, p = 0.086; Week 2: 2.55 vs. 2.47, p = 0.115). Additionally, the mean positive rates in the Turing test were 40.5% in Week 0 and 45.2% in Week 2, which demonstrated the great intelligence of our model. Conclusions Our proposed model can be used clinically for assisting contouring of CTVs in rectal cancer patients receiving neoadjuvant radiotherapy, which improves the efficiency and consistency of radiation clinicians' work. |
| first_indexed | 2024-12-21T23:53:36Z |
| format | Article |
| id | doaj.art-b1a4b8c2634d48959cbc95145a7a64c4 |
| institution | Directory Open Access Journal |
| issn | 2045-7634 |
| language | English |
| last_indexed | 2024-12-21T23:53:36Z |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj.art-b1a4b8c2634d48959cbc95145a7a64c42022-12-21T18:45:52ZengWileyCancer Medicine2045-76342022-01-0111116617510.1002/cam4.4441A blind randomized validated convolutional neural network for auto‐segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapyYijun Wu0Kai Kang1Chang Han2Shaobin Wang3Qi Chen4Yu Chen5Fuquan Zhang6Zhikai Liu7Department of Radiation Oncology Peking Union Medical College Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Beijing ChinaDepartment of Radiation Oncology Peking Union Medical College Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Beijing ChinaDepartment of Radiation Oncology Peking Union Medical College Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Beijing ChinaMedMind Technology Co., Ltd. Beijing ChinaMedMind Technology Co., Ltd. Beijing ChinaMedMind Technology Co., Ltd. Beijing ChinaDepartment of Radiation Oncology Peking Union Medical College Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Beijing ChinaDepartment of Radiation Oncology Peking Union Medical College Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Beijing ChinaAbstract Background Delineation of clinical target volume (CTV) for radiotherapy is a time‐consuming and labor‐intensive work. This study aims to propose a novel convolutional neural network (CNN)‐based model for fast auto‐segmentation of CTV. To evaluate its performance and clinical utility, a blind randomized validation method was used. Methods Our proposed model was based on the generally accepted U‐Net architecture using computed tomography slices with CTV contours delineated by experienced radiation clinicians from 135 rectal patients receiving neoadjuvant radiotherapy. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (95HD) were used to measure segmentation performance. The validated dataset of additional 20 patients for clinical evaluation by 10 experienced oncology clinicians from 7 centers was randomly and blindly divided into two groups for clinicians' scoring and Turing test, respectively. Second evaluation was performed with different randomization after 2 weeks. Results The mean DSC and 95HD values of the proposed model were 0.90 ± 0.02 and 8.11 ± 1.93 mm for CTV of rectal cancer patients, respectively. The average time for automatic segmentation in the validation groups was 15 s per patient. By clinicians' scoring, the AI model performed better than manually delineating, though the differences were not significant (Week 0: 2.59 vs. 2.52, p = 0.086; Week 2: 2.55 vs. 2.47, p = 0.115). Additionally, the mean positive rates in the Turing test were 40.5% in Week 0 and 45.2% in Week 2, which demonstrated the great intelligence of our model. Conclusions Our proposed model can be used clinically for assisting contouring of CTVs in rectal cancer patients receiving neoadjuvant radiotherapy, which improves the efficiency and consistency of radiation clinicians' work.https://doi.org/10.1002/cam4.4441deep learningrectal cancerneoadjuvant radiotherapyconvolutional neural networkclinical evaluation |
| spellingShingle | Yijun Wu Kai Kang Chang Han Shaobin Wang Qi Chen Yu Chen Fuquan Zhang Zhikai Liu A blind randomized validated convolutional neural network for auto‐segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy Cancer Medicine deep learning rectal cancer neoadjuvant radiotherapy convolutional neural network clinical evaluation |
| title | A blind randomized validated convolutional neural network for auto‐segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy |
| title_full | A blind randomized validated convolutional neural network for auto‐segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy |
| title_fullStr | A blind randomized validated convolutional neural network for auto‐segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy |
| title_full_unstemmed | A blind randomized validated convolutional neural network for auto‐segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy |
| title_short | A blind randomized validated convolutional neural network for auto‐segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy |
| title_sort | blind randomized validated convolutional neural network for auto segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy |
| topic | deep learning rectal cancer neoadjuvant radiotherapy convolutional neural network clinical evaluation |
| url | https://doi.org/10.1002/cam4.4441 |
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