The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer
Abstract Purpose To investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer. Methods and materials Twenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radio...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2021-06-01
|
| Series: | Radiation Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13014-021-01837-y |
| _version_ | 1818461972159528960 |
|---|---|
| author | Hongbo Guo Jiazhou Wang Xiang Xia Yang Zhong Jiayuan Peng Zhen Zhang Weigang Hu |
| author_facet | Hongbo Guo Jiazhou Wang Xiang Xia Yang Zhong Jiayuan Peng Zhen Zhang Weigang Hu |
| author_sort | Hongbo Guo |
| collection | DOAJ |
| description | Abstract Purpose To investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer. Methods and materials Twenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radiotherapy in our department were enrolled in this study. Two deep learning-based auto-segmentation systems, including an in-house developed system (FD) and a commercial product (UIH), were used to generate two auto-segmented OARs sets (OAR_FD and OAR_UIH). Treatment plans based on auto-segmented OARs and following our clinical requirements were generated for each patient on each OARs sets (Plan_FD and Plan_UIH). Geometric metrics (Hausdorff distance (HD), mean distance to agreement (MDA), the Dice similarity coefficient (DICE) and the Jaccard index) were calculated for geometric evaluation. The dosimetric impact was evaluated by comparing Plan_FD and Plan_UIH to original clinically approved plans (Plan_Manual) with dose-volume metrics and 3D gamma analysis. Spearman’s correlation analysis was performed to investigate the correlation between dosimetric difference and geometric metrics. Results FD and UIH could provide similar geometric performance in parotids, temporal lobes, lens, and eyes (DICE, p > 0.05). OAR_FD had better geometric performance in the optic nerves, oral cavity, larynx, and femoral heads (DICE, p < 0.05). OAR_UIH had better geometric performance in the bladder (DICE, p < 0.05). In dosimetric analysis, both Plan_FD and Plan_UIH had nonsignificant dosimetric differences compared to Plan_Manual for most PTV and OARs dose-volume metrics. The only significant dosimetric difference was the max dose of the left temporal lobe for Plan_FD vs. Plan_Manual (p = 0.05). Only one significant correlation was found between the mean dose of the femoral head and its HD index (R = 0.4, p = 0.01), there is no OARs showed strong correlation between its dosimetric difference and all of four geometric metrics. Conclusions Deep learning-based OARs auto-segmentation for NPC and rectal cancer has a nonsignificant impact on most PTV and OARs dose-volume metrics. Correlations between the auto-segmentation geometric metric and dosimetric difference were not observed for most OARs. |
| first_indexed | 2024-12-14T23:54:38Z |
| format | Article |
| id | doaj.art-418523ec5e32487e9b540adadf28479c |
| institution | Directory Open Access Journal |
| issn | 1748-717X |
| language | English |
| last_indexed | 2024-12-14T23:54:38Z |
| publishDate | 2021-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Radiation Oncology |
| spelling | doaj.art-418523ec5e32487e9b540adadf28479c2022-12-21T22:43:09ZengBMCRadiation Oncology1748-717X2021-06-0116111410.1186/s13014-021-01837-yThe dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancerHongbo Guo0Jiazhou Wang1Xiang Xia2Yang Zhong3Jiayuan Peng4Zhen Zhang5Weigang Hu6Department of Radiation Oncology, Fudan University Shanghai Cancer CenterDepartment of Radiation Oncology, Fudan University Shanghai Cancer CenterDepartment of Radiation Oncology, Fudan University Shanghai Cancer CenterDepartment of Radiation Oncology, Fudan University Shanghai Cancer CenterDepartment of Radiation Oncology, Fudan University Shanghai Cancer CenterDepartment of Radiation Oncology, Fudan University Shanghai Cancer CenterDepartment of Radiation Oncology, Fudan University Shanghai Cancer CenterAbstract Purpose To investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer. Methods and materials Twenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radiotherapy in our department were enrolled in this study. Two deep learning-based auto-segmentation systems, including an in-house developed system (FD) and a commercial product (UIH), were used to generate two auto-segmented OARs sets (OAR_FD and OAR_UIH). Treatment plans based on auto-segmented OARs and following our clinical requirements were generated for each patient on each OARs sets (Plan_FD and Plan_UIH). Geometric metrics (Hausdorff distance (HD), mean distance to agreement (MDA), the Dice similarity coefficient (DICE) and the Jaccard index) were calculated for geometric evaluation. The dosimetric impact was evaluated by comparing Plan_FD and Plan_UIH to original clinically approved plans (Plan_Manual) with dose-volume metrics and 3D gamma analysis. Spearman’s correlation analysis was performed to investigate the correlation between dosimetric difference and geometric metrics. Results FD and UIH could provide similar geometric performance in parotids, temporal lobes, lens, and eyes (DICE, p > 0.05). OAR_FD had better geometric performance in the optic nerves, oral cavity, larynx, and femoral heads (DICE, p < 0.05). OAR_UIH had better geometric performance in the bladder (DICE, p < 0.05). In dosimetric analysis, both Plan_FD and Plan_UIH had nonsignificant dosimetric differences compared to Plan_Manual for most PTV and OARs dose-volume metrics. The only significant dosimetric difference was the max dose of the left temporal lobe for Plan_FD vs. Plan_Manual (p = 0.05). Only one significant correlation was found between the mean dose of the femoral head and its HD index (R = 0.4, p = 0.01), there is no OARs showed strong correlation between its dosimetric difference and all of four geometric metrics. Conclusions Deep learning-based OARs auto-segmentation for NPC and rectal cancer has a nonsignificant impact on most PTV and OARs dose-volume metrics. Correlations between the auto-segmentation geometric metric and dosimetric difference were not observed for most OARs.https://doi.org/10.1186/s13014-021-01837-yTreatment planningDosimetricDeep learningAuto-segmentation |
| spellingShingle | Hongbo Guo Jiazhou Wang Xiang Xia Yang Zhong Jiayuan Peng Zhen Zhang Weigang Hu The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer Radiation Oncology Treatment planning Dosimetric Deep learning Auto-segmentation |
| title | The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer |
| title_full | The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer |
| title_fullStr | The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer |
| title_full_unstemmed | The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer |
| title_short | The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer |
| title_sort | dosimetric impact of deep learning based auto segmentation of organs at risk on nasopharyngeal and rectal cancer |
| topic | Treatment planning Dosimetric Deep learning Auto-segmentation |
| url | https://doi.org/10.1186/s13014-021-01837-y |
| work_keys_str_mv | AT hongboguo thedosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT jiazhouwang thedosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT xiangxia thedosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT yangzhong thedosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT jiayuanpeng thedosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT zhenzhang thedosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT weiganghu thedosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT hongboguo dosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT jiazhouwang dosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT xiangxia dosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT yangzhong dosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT jiayuanpeng dosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT zhenzhang dosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer AT weiganghu dosimetricimpactofdeeplearningbasedautosegmentationoforgansatriskonnasopharyngealandrectalcancer |