Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery

Chung, Sung Pil; Chang, Jee Suk; Choi, Min Jeong; Chang, Yongjin; Choi, Byong Su; Chun, Jaehee; Keum, Ki Chang; Kim, Jin Sung; Kim, Yong Bae

doi:10.1186/s13014-021-01771-z

“…consistent DC underperformance can be identified and targeted with additional training to further improve DC model accuracy. Additional OAR and CTV DC models, such as those applicable to breast, thoracic, and gynecological RT treatment planning, are currently being developed and tested in workflow by a variety of groups, including at our own institutions [14][15][16][17].…”

Section: Supplementary Informationmentioning

confidence: 99%

Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers

Wong¹,

Huang²,

Wells³

et al. 2021

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Purpose We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemented DC models in the clinical radiotherapy (RT) planning workflow and report on user experience. Methods and materials DC models were implemented at two cancer centers and used to generate OAR and CTVs for all patients undergoing RT for a central nervous system (CNS), head and neck (H&N), or prostate cancer. Radiation Therapists/Dosimetrists and Radiation Oncologists completed post-contouring surveys rating the degree of edits required for DCs (1 = minimal, 5 = significant) and overall DC satisfaction (1 = poor, 5 = high). Unedited DCs were compared to the edited treatment approved contours using Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD). Results Between September 19, 2019 and March 6, 2020, DCs were generated on approximately 551 eligible cases. 203 surveys were collected on 27 CNS, 54 H&N, and 93 prostate RT plans, resulting in an overall survey compliance rate of 32%. The majority of OAR DCs required minimal edits subjectively (mean editing score ≤ 2) and objectively (mean DSC and 95% HD was ≥ 0.90 and ≤ 2.0 mm). Mean OAR satisfaction score was 4.1 for CNS, 4.4 for H&N, and 4.6 for prostate structures. Overall CTV satisfaction score (n = 25), which encompassed the prostate, seminal vesicles, and neck lymph node volumes, was 4.1. Conclusions Previously validated OAR DC models for CNS, H&N, and prostate RT planning required minimal subjective and objective edits and resulted in a positive user experience, although low survey compliance was a concern. CTV DC model evaluation was even more limited, but high user satisfaction suggests that they may have served as appropriate starting points for patient specific edits.

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“…Previously, auto-segmentation methods such as (multi)atlas based and hybrid techniques have been considered state-of-the-art [ 9 ]. Over time, methods based on convolutional neural networks (CNN) [ 10 ] gained more attention [ 11 , 12 ]. Milletari et al [ 13 ] proposed a 3D fully convolutional neural network architecture trained end-to-end on magnetic resonance (MR) prostate images, referred to as V-Net, and introduced a novel objective function based on the Dice similarity coefficient (DSC).…”

Section: Introductionmentioning

confidence: 99%

Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer

Kawula

¹

,

Purice

²

,

Li

³

et al. 2022

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Background The evaluation of automatic segmentation algorithms is commonly performed using geometric metrics. An analysis based on dosimetric parameters might be more relevant in clinical practice but is often lacking in the literature. The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in radiation therapy (RT) for prostate cancer patients. Methods A database of 69 computed tomography images with prostate, bladder, and rectum delineations was used for single-label 3D U-Net training with dice similarity coefficient (DSC)-based loss. Volumetric modulated arc therapy (VMAT) plans have been generated for both manual and automatic segmentations with the same optimization settings. These were chosen to give consistent plans when applying perturbations to the manual segmentations. Contours were evaluated in terms of DSC, average and 95% Hausdorff distance (HD). Dose distributions were evaluated with the manual segmentation as reference using dose volume histogram (DVH) parameters and a 3%/3 mm gamma-criterion with 10% dose cut-off. A Pearson correlation coefficient between DSC and dosimetric metrics, i.e. gamma index and DVH parameters, has been calculated. Results 3D U-Net-based segmentation achieved a DSC of 0.87 (0.03) for prostate, 0.97 (0.01) for bladder and 0.89 (0.04) for rectum. The mean and 95% HD were below 1.6 (0.4) and below 5 (4) mm, respectively. The DVH parameters, V$$_{60/65/70\,{\mathrm{Gy}}}$$ 60 / 65 / 70 Gy for the bladder and V$$_{50/65/70\,{\mathrm{Gy}}}$$ 50 / 65 / 70 Gy for the rectum, showed agreement between dose distributions within $$\pm \, 5\%$$ ± 5 % and $$\pm \,2\%$$ ± 2 % , respectively. The D$$_{98/2\%}$$ 98 / 2 % and V$$_{95\%}$$ 95 % , for prostate and its 3 mm expansion (surrogate clinical target volume) showed agreement with the reference dose distribution within 2% and 3 Gy with the exception of one case. The average gamma pass-rate was 85%. The comparison between geometric and dosimetric metrics showed no strong statistically significant correlation. Conclusions The 3D U-Net developed for this work achieved state-of-the-art geometrical performance. Analysis based on clinically relevant DVH parameters of VMAT plans demonstrated neither excessive dose increase to OARs nor substantial under/over-dosage of the target in all but one case. Yet the gamma analysis indicated several cases with low pass rates. The study highlighted the importance of adding dosimetric analysis to the standard geometric evaluation.

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“…Even if they segmented the GTV and OARs according to the same guidelines, inconsistencies may still exist in the segmentation for both inter-and intra-observers. On the other hand, the automatic segmentation technique has the potential to provide efficient and accurate results (16,17). It can not only shorten the time needed to exploit the anatomy but also allow experts to devote time to optimize RT treatment planning so that the OARs could be less irradiated.…”

Section: Introductionmentioning

confidence: 99%

Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy

Liu

¹

,

Li

²

,

Yang

³

et al. 2021

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Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed dose to tumor targets, it is essential to spare the tissues near the targets—the so-called organs-at-risk (OARs). An optimal RT planning benefits from the accurate segmentation of the gross tumor volume and surrounding OARs. Manual segmentation is a time-consuming and tedious task for radiation oncologists. Therefore, it is crucial to develop automatic image segmentation to relieve radiation oncologists of the tedious contouring work. Currently, the atlas-based automatic segmentation technique is commonly used in clinical routines. However, this technique depends heavily on the similarity between the atlas and the image segmented. With significant advances made in computer vision, deep learning as a part of artificial intelligence attracts increasing attention in medical image automatic segmentation. In this article, we reviewed deep learning based automatic segmentation techniques related to lung cancer and compared them with the atlas-based automatic segmentation technique. At present, the auto-segmentation of OARs with relatively large volume such as lung and heart etc. outperforms the organs with small volume such as esophagus. The average Dice similarity coefficient (DSC) of lung, heart and liver are over 0.9, and the best DSC of spinal cord reaches 0.9. However, the DSC of esophagus ranges between 0.71 and 0.87 with a ragged performance. In terms of the gross tumor volume, the average DSC is below 0.8. Although deep learning based automatic segmentation techniques indicate significant superiority in many aspects compared to manual segmentation, various issues still need to be solved. We discussed the potential issues in deep learning based automatic segmentation including low contrast, dataset size, consensus guidelines, and network design. Clinical limitations and future research directions of deep learning based automatic segmentation were discussed as well.

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Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery

Cited by 45 publications

References 36 publications

Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers

Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers

Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer

Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy

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