1 |
SUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021,71(3):209-249. doi:10.3322/caac.21660
doi: 10.3322/caac.21660
|
2 |
ANDRIANARISON V A, LAOUITI M, FARGIER-BOCHATON O, et al. Contouring workload in adjuvant breast cancer radiotherapy[J]. Cancer Radiother, 2018,22(8):747-753. doi:10.1016/j.canrad.2018.01.008
doi: 10.1016/j.canrad.2018.01.008
|
3 |
LI X A, ARTHUR D W, BUCHHOLZ T A, et al. Variability of target and normal structure delineation for breast-cancer radiotherapy: A RTOG multi-institutional and multi-observer study[J]. Int J Radiat Oncol Biol Phys, 2009,73(3):944-951. doi:10.1016/j.ijrobp.2008.10.034
doi: 10.1016/j.ijrobp.2008.10.034
|
4 |
陈飞, 龚筱钦, 余云鹏, 等. AccuLearning自动勾画临床靶区和危及器官用于宫颈癌术后放疗的可行性研究[J]. 实用医学杂志, 2024,40(2):153-157.
|
5 |
RADICI L, FERRARIO S, BORCA V C, et al. Implementation of a commercial deep learning-based auto segmentation software in radiotherapy: Evaluation of effectiveness and impact on workflow[J]. Life (Basel), 2022,12(12):2088. doi:10.3390/life12122088
doi: 10.3390/life12122088
|
6 |
邢碧媛, 盛宇涵, 赵迎超, 等. 人工智能在恶性肿瘤放射治疗领域的相关应用及进展[J]. 临床肿瘤学杂志, 2020,25(7):656-663.
|
7 |
VINOD S K, JAMESON M G, MIN M, et al. Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies[J]. Radiother Oncol, 2016,121(2):169-179. doi:10.1016/j.radonc.2016.09.009
doi: 10.1016/j.radonc.2016.09.009
|
8 |
何奕松, 蒋家良, 余行, 等. 影像分割中Dice系数和Hausdorff距离的比较[J]. 中国医学物理学杂志, 2019,36(11):1307-1311. doi:10.3969/j.issn.1005-202X.2019.11.012
doi: 10.3969/j.issn.1005-202X.2019.11.012
|
9 |
AHN S H, YEO A U, KIM K H, et al. Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer[J]. Radiat Oncol, 2019,14(1):213. doi:10.1186/s13014-019-1392-z
doi: 10.1186/s13014-019-1392-z
|
10 |
ANDREWS S, HAMARNEH G. Multi-Region Probabilistic Dice Similarity Coefficient using the Aitchison Distance and Bipartite Graph Matching[J]. Computer Science, 2015. DOI:10.48550/arXiv.1509.07244 .
doi: 10.48550/arXiv.1509.07244
|
11 |
BAROUDI H, BROCK K K, CAO W, et al. Automated contouring and planning in radiation therapy: What is 'clinically acceptable'?[J]. Diagnostics (Basel), 2023,13(4):667. doi:10.3390/diagnostics13040667
doi: 10.3390/diagnostics13040667
|
12 |
MACCHIA G, CILLA S, BUWENGE M, et al. Intensity-modulated radiotherapy with concomitant boost after breast conserving surgery: A Phase I-II trial[J]. Breast Cancer (Dove Med Press), 2020,12:243-249. doi:10.2147/bctt.s261587
doi: 10.2147/bctt.s261587
|
13 |
ANDRADE T R M, FONSECA M C M, SEGRETO H R C, et al. Meta-analysis of long-term efficacy and safety of hypofractionated radiotherapy in the treatment of early breast cancer[J]. Breast, 2019,48:24-31. doi:10.1016/j.breast.2019.08.001
doi: 10.1016/j.breast.2019.08.001
|
14 |
陈车, 陈睿, 陆治江, 等. 多叶准直器角度改变对左侧全乳大分割放疗瘤床推量的剂量学影响[J]. 辐射研究与辐射工艺学报, 2023,41(4):53-59.
|
15 |
VANDEWINCKELE L, CLAESSENS M, DINKLA A, et al. Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance[J]. Radiother Oncol, 2020,153:55-66. doi:10.1016/j.radonc.2020.09.008
doi: 10.1016/j.radonc.2020.09.008
|
16 |
LOI G, FUSELLA M, LANZI E, et al. Performance of commercially available deformable image registration platforms for contour propagation using patient‐based computational phantoms: A multi‐institutional study[J]. Med Phys, 2018,45(2):748-757. doi:10.1002/mp.12737
doi: 10.1002/mp.12737
|
17 |
ZHOU H, LI Y, GU Y, et al. A deep learning based automatic segmentation approach for anatomical structures in intensity modulation radiotherapy[J]. Math Biosci Eng, 2021,18(6):7506-7524. doi:10.3934/mbe.2021371
doi: 10.3934/mbe.2021371
|
18 |
GUO B, SHAH C, XIA P. Automated planning of whole breast irradiation using hybrid IMRT improves efficiency and quality[J]. J Appl Clin Med Phys, 2019,20(12):87-96. doi:10.1002/acm2.12767
doi: 10.1002/acm2.12767
|
19 |
VOET P W J, DIRKX M L P, TEGUH D N, et al. Does atlas-based autosegmentation of neck levels require subsequent manual contour editing to avoid risk of severe target underdosage? A dosimetric analysis[J]. Radiother Oncol, 2011,98(3):373-377. doi:10.1016/j.radonc.2010.11.017
doi: 10.1016/j.radonc.2010.11.017
|
20 |
GUO H, WANG J, XIA X, et al. The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer[J]. Radiat Oncol, 2021,16(1):113. doi:10.1186/s13014-021-01837-y
doi: 10.1186/s13014-021-01837-y
|
21 |
YAN C, GUO B, TENDULKAR R, et al. Contour similarity and its implication on inverse prostate SBRT treatment planning[J]. J Appl Clin Med Phys, 2023,24(2):e13809. doi:10.1002/acm2.13809
doi: 10.1002/acm2.13809
|
22 |
POEL R, RÜFENACHT E, HERMANN E, et al. The predictive value of segmentation metrics on dosimetry in organs at risk of the brain[J]. Med Image Anal, 2021,73:102161. doi:10.1016/j.media.2021.102161
doi: 10.1016/j.media.2021.102161
|
23 |
SHARP G, FRITSCHER K D, PEKAR V, et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy[J]. Med Phys, 2014,41(5):50902. doi:10.1118/1.4871620
doi: 10.1118/1.4871620
|
24 |
DAI Z, ZHANG Y, ZHU L, et al. Geometric and dosimetric evaluation of deep learning-based automatic delineation on CBCT-Synthesized CT and planning CT for breast cancer adaptive radiotherapy: A multi-institutional study[J]. Front Oncol, 2021,11:725507. doi:10.3389/fonc.2021.725507
doi: 10.3389/fonc.2021.725507
|
25 |
CAO M, STIEHL B, YU V Y, et al. Analysis of geometric performance and dosimetric impact of using automatic contour segmentation for radiotherapy planning[J]. Front Oncol, 2020,10:1762. doi:10.3389/fonc.2020.01762
doi: 10.3389/fonc.2020.01762
|
26 |
BAROUDI H, MINH NGUYEN C I HUY, MAROONGROGE S, et al. Automated contouring and statistical process control for plan quality in a breast clinical trial[J]. Phys Imaging Radiat Oncol, 2023,28:100486. doi:10.1016/j.phro.2023.100486
doi: 10.1016/j.phro.2023.100486
|
27 |
ZHONG Y, GUO Y, FANG Y, et al. Geometric and dosimetric evaluation of deep learning based auto-segmentation for clinical target volume on breast cancer[J]. J Appl Clin Med Phys, 2023,24(7):e13951. doi:10.1002/acm2.13951
doi: 10.1002/acm2.13951
|