The Journal of Practical Medicine >

2024 , Vol. 40 >Issue 17: 2406 - 2411

DOI: https://doi.org/10.3969/j.issn.1006-5725.2024.17.009

Clinical Research

Clinical Application of automatic delineation in whole breast radiotherapy with simultaneous integrated boost to the medial tumor beds

  • Che CHEN ,
  • Dehong LUO ,
  • Huangfei YU ,
  • Qin ZHANG ,
  • Xiaochi HU ,
  • Shenghua YU ,
  • Yajun. LI
Expand
  • Radiation Therapy Center，the First People′s Hospital of Zunyi，the Third Affiliated Hospital of Zunyi Medical University，Zunyi 563000，China

Received date: 2023-12-06

  Online published: 2024-09-13

Fold

Abstract

Objective To assess the viability and efficacy of employing automated segmentation for whole breast radiotherapy with simultaneous integrated boost to the medial tumor beds， a comparative analysis was conducted on the disparities in geometry， dosimetry， and working time between the auto-segmentation （AS） and manual segmentation （MS） groups. Methods A total of 30 patients with early breast cancer， who had undergone conserving surgery and received hypofractionated radiotherapy with a boost to the medial tumor bed， were enrolled from the First People's Hospital of Zunyi. AccuContour software was used in the AS group to obtain the whole breast planning target volume and cardiopulmonary structure. Geometric differences between AS and MS groups were assessed using Dice similarity coefficient （DSC） and 95% Hausdorff distance （95HD）. Subsequently， a comparison was made between the two groups regarding target and cardiopulmonary dosimetry for PlanA and PlanM. Additionally， the time spent by each group was also compared. Results The DSC of PGTV， PTV， lung， and heart were 0.94（0.91，0.96）， 0.88（0.86，0.91）， 0.98（0.97，0.98） and 0.94（0.93，0.95）， respectively. And the 95 HD（cm） were 0.25（0.20，0.33）， 0.99（0.56，1.20）， 0.29（0.25，0.35） and 0.50（0.50，0.59） respectively. The dosimetric results showed that the V95， D95， and Dmean of PGTV and PTV in the AS group were significantly lower than those in the MS group （P < 0.05）； while the V20 and MLD of the left lung were significantly higher （P < 0.05）. No significant difference was observed in cardiac dose between the two groups. The mean absolute differences of PGTV and cardiopulmonary dose parameters between the two groups were less than 1 Gy/1%， respectively. In terms of work efficiency， the AS approach substantially reduced contouring and planning time with over 70% of cases approved within two days. Conclusions The differences in geometric and dosimetric parameters between the auto-segmentation and manual segmentation groups were found to be negligible for whole breast radiotherapy with medial tumor bed boost patients. It is recommended that the PTV be manually modified prior to plan optimization， leading to a significant improvement in work efficiency.

Key words： breast cancer; auto-segmentation; dosimetry; medial tumor bed

Cite this article

Che CHEN , Dehong LUO , Huangfei YU , Qin ZHANG , Xiaochi HU , Shenghua YU , Yajun. LI . Clinical Application of automatic delineation in whole breast radiotherapy with simultaneous integrated boost to the medial tumor beds[J]. The Journal of Practical Medicine, 2024 , 40(17) : 2406 -2411 . DOI: 10.3969/j.issn.1006-5725.2024.17.009

References

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
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
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
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
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
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
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
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 .
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Options
Outlines

/

〈 〉