Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Assembling High-quality Lymph Node Clinical Target Volumes for Cervical Cancer Radiotherapy using a Deep Learning-based Approach

Author(s): Xiaoxuan Jiang, Shengyuan Zhang, Yuchuan Fu*, Hang Yu, Huanan Tang and Xiangyang Wu

Volume 20, 2024

Published on: 18 October, 2023

Article ID: e150923221162 Pages: 9

DOI: 10.2174/1573405620666230915125606

open_access

Abstract

Aim: The study aimed to explore an approach for accurately assembling high-quality lymph node clinical target volumes (CTV) on CT images in cervical cancer radiotherapy with the encoder-decoder 3D network.

Methods: 216 cases of CT images treated at our center between 2017 and 2020 were included as a sample, which were divided into two cohorts, including 152 cases and 64 cases, respectively. Para-aortic lymph node, common iliac, external iliac, internal iliac, obturator, presacral, and groin nodal regions were delineated as sub-CTV manually in the cohort including 152 cases. Then, the 152 cases were randomly divided into training (96 cases), validation (36 cases), and test (20 cases) groups for the training process. Each structure was individually trained and optimized through a deep learning model. An additional 64 cases with 6 different clinical conditions were taken as examples to verify the feasibility of CTV generation based on our model. Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics were both used for quantitative evaluation.

Results: Comparing auto-segmentation results to ground truth, the mean DSC value/HD was 0.838/7.7mm, 0.853/4.7mm, 0.855/4.7mm, 0.844/4.7mm, 0.784/5.2mm, 0.826/4.8mm and 0.874/4.8mm for CTV_PAN, CTV_common iliac, CTV_internal iliac, CTV_external iliac, CTV_obturator, CTV_presacral, and CTV_groin, respectively. The similarity comparison results of six different clinical situations were 0.877/4.4mm, 0.879/4.6mm, 0.881/4.2mm, 0.882/4.3mm, 0.872/6.0mm, and 0.875/4.9mm for DSC value/HD, respectively.

Conclusion: We have developed a deep learning-based approach to segmenting lymph node sub-regions automatically and assembling high-quality CTVs according to clinical needs in cervical cancer radiotherapy. This work can increase the efficiency of the process of cervical cancer detection and treatment.

Keywords: Auto-segmentation, Clinical target volume, Lymph nodes, Deep leaning, Cervical cancer, Radiotherapy.

[1]
Arbyn M, Weiderpass E, Bruni L, et al. Estimates of incidence and mortality of cervical cancer in 2018: A worldwide analysis. Lancet Glob Health 2020; 8(2): e191-203.
[http://dx.doi.org/10.1016/S2214-109X(19)30482-6] [PMID: 31812369]
[2]
Petrelli F, De Stefani A, Raspagliesi F, Lorusso D, Barni S. Radiotherapy with concurrent cisplatin-based doublet or weekly cisplatin for cervical cancer: A systematic review and meta-analysis. Gynecol Oncol 2014; 134(1): 166-71.
[http://dx.doi.org/10.1016/j.ygyno.2014.04.049] [PMID: 24793000]
[3]
Koh WJ, Abu-Rustum NR, Bean S, et al. Cervical cancer, version 3.2019, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 2019; 17(1): 64-84.
[http://dx.doi.org/10.6004/jnccn.2019.0001] [PMID: 30659131]
[4]
Teoh M, Clark CH, Wood K, Whitaker S, Nisbet A. Volumetric modulated arc therapy: A review of current literature and clinical use in practice. Br J Radiol 2011; 84(1007): 967-96.
[http://dx.doi.org/10.1259/bjr/22373346] [PMID: 22011829]
[5]
Kim N, Chang JS, Kim YB, Kim JS. Atlas-based auto-segmentation for postoperative radiotherapy planning in endometrial and cervical cancers. Radiat Oncol 2020; 15(1): 106.
[http://dx.doi.org/10.1186/s13014-020-01562-y] [PMID: 32404123]
[6]
Harari PM, Song S, Tomé WA. Emphasizing conformal avoidance versus target definition for IMRT planning in head-and-neck cancer. Int J Radiat Oncol Biol Phys 2010; 77(3): 950-8.
[http://dx.doi.org/10.1016/j.ijrobp.2009.09.062] [PMID: 20378266]
[7]
Brouwer CL, Steenbakkers RJHM, van den Heuvel E, et al. 3D Variation in delineation of head and neck organs at risk. Radiat Oncol 2012; 7(1): 32.
[http://dx.doi.org/10.1186/1748-717X-7-32] [PMID: 22414264]
[8]
Nelms BE, Tomé WA, Robinson G, Wheeler J. Variations in the contouring of organs at risk: test case from a patient with oropharyngeal cancer. Int J Radiat Oncol Biol Phys 2012; 82(1): 368-78.
[http://dx.doi.org/10.1016/j.ijrobp.2010.10.019] [PMID: 21123004]
[9]
Small W Jr, Mell LK, Anderson P, et al. Consensus guidelines for delineation of clinical target volume for intensity-modulated pelvic radiotherapy in postoperative treatment of endometrial and cervical cancer. Int J Radiat Oncol Biol Phys 2008; 71(2): 428-34.
[http://dx.doi.org/10.1016/j.ijrobp.2007.09.042] [PMID: 18037584]
[10]
Toita T, Ohno T, Kaneyasu Y, et al. A consensus-based guideline defining the clinical target volume for pelvic lymph nodes in external beam radiotherapy for uterine cervical cancer. Jpn J Clin Oncol 2010; 40(5): 456-63.
[http://dx.doi.org/10.1093/jjco/hyp191] [PMID: 20133334]
[11]
Small W Jr, Bosch WR, Harkenrider MM, et al. NRG oncology/RTOG consensus guidelines for delineation of clinical target volume for intensity modulated pelvic radiation therapy in postoperative treatment of endometrial and cervical cancer: an update. Int J Radiat Oncol Biol Phys 2021; 109(2): 413-24.
[http://dx.doi.org/10.1016/j.ijrobp.2020.08.061] [PMID: 32905846]
[12]
Toita T, Ohno T, Kaneyasu Y, et al. A consensus-based guideline defining clinical target volume for primary disease in external beam radiotherapy for intact uterine cervical cancer. Jpn J Clin Oncol 2011; 41(9): 1119-26.
[http://dx.doi.org/10.1093/jjco/hyr096] [PMID: 21875938]
[13]
Lim K, Small W Jr, Portelance L, et al. Consensus guidelines for delineation of clinical target volume for intensity-modulated pelvic radiotherapy for the definitive treatment of cervix cancer. Int J Radiat Oncol Biol Phys 2011; 79(2): 348-55.
[http://dx.doi.org/10.1016/j.ijrobp.2009.10.075] [PMID: 20472347]
[14]
Kosmin M, Ledsam J, Romera-Paredes B, et al. Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer. Radiother Oncol 2019; 135: 130-40.
[http://dx.doi.org/10.1016/j.radonc.2019.03.004] [PMID: 31015159]
[15]
Hoang Duc AK, Eminowicz G, Mendes R, et al. Validation of clinical acceptability of an atlas-based segmentation algorithm for the delineation of organs at risk in head and neck cancer. Med Phys 2015; 42(9): 5027-34.
[http://dx.doi.org/10.1118/1.4927567] [PMID: 26328953]
[16]
Delpon G, Escande A, Ruef T, et al. Comparison of automated atlas-based segmentation software for postoperative prostate cancer radiotherapy. Front Oncol 2016; 6: 178.
[http://dx.doi.org/10.3389/fonc.2016.00178] [PMID: 27536556]
[17]
Pejavar S, Yom SS, Hwang A, et al. Computer-assisted, atlas-based segmentation for target volume delineation in whole pelvic IMRT for prostate cancer. Technol Cancer Res Treat 2013; 12(3): 199-206.
[http://dx.doi.org/10.7785/tcrt.2012.500313] [PMID: 23289478]
[18]
Rohlfing T, Brandt R, Menzel R, Russakoff DB, Maurer CR. Quo vadis, atlas-based segmentation? Handbook of biomedical image analysis. Springer 2005; pp. 435-86.
[http://dx.doi.org/10.1007/0-306-48608-3_11]
[19]
Sartor H, Minarik D, Enqvist O, et al. Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth. Clin Transl Radiat Oncol 2020; 25: 37-45.
[http://dx.doi.org/10.1016/j.ctro.2020.09.004] [PMID: 33005756]
[20]
Liu Z, Liu X, Xiao B, et al. Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network. Phys Med 2020; 69: 184-91.
[http://dx.doi.org/10.1016/j.ejmp.2019.12.008] [PMID: 31918371]
[21]
Wang Z, Chang Y, Peng Z, et al. Evaluation of deep learning‐based auto‐segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients. J Appl Clin Med Phys 2020; 21(12): 272-9.
[http://dx.doi.org/10.1002/acm2.13097] [PMID: 33238060]
[22]
Rhee DJ, Jhingran A, Rigaud B, et al. Automatic contouring system for cervical cancer using convolutional neural networks. Med Phys 2020; 47(11): 5648-58.
[http://dx.doi.org/10.1002/mp.14467] [PMID: 32964477]
[23]
Cardenas CE, Beadle BM, Garden AS, et al. Generating high-quality lymph node clinical target volumes for head and neck cancer radiation therapy using a fully automated deep learning-based approach. Int J Radiat Oncol Biol Phys 2021; 109(3): 801-12.
[http://dx.doi.org/10.1016/j.ijrobp.2020.10.005] [PMID: 33068690]
[24]
Otsu N, man C. A threshold selection method from gray-level histograms IEEE T SYST MAN CY-S 1979; 9(1): 62-6.
[25]
He K, Zhang X, Ren S, Sun J, Eds. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
[26]
Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Eds. Residual attention network for image classification. Proceedings of the IEEE conference on computer vision and pattern recognition. 21-26 July, USA, 2017, pp. 6450-6458.
[27]
Gu Z, Cheng J, Fu H, et al. Ce-net: Context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging 2019; 38(10): 2281-92.
[http://dx.doi.org/10.1109/TMI.2019.2903562] [PMID: 30843824]
[28]
Jiang J, Luo Y, Wang F, Fu Y, Yu H, He Y. Evaluation on auto-segmentation of the clinical target volume (CTV) for Graves’ Ophthalmopathy (GO) with a fully convolutional network (FCN) on ct images. Curr Med Imaging Rev 2021; 17(3): 404-9.
[http://dx.doi.org/10.2174/1573405616666200910141323] [PMID: 32914716]
[29]
Lee H, Lee E, Kim N, et al. Clinical evaluation of commercial atlas-based auto-segmentation in the head and neck region. Front Oncol 2019; 9: 239.
[http://dx.doi.org/10.3389/fonc.2019.00239] [PMID: 31024843]
[30]
Mundt AJ, Lujan AE, Rotmensch J, et al. Intensity-modulated whole pelvic radiotherapy in women with gynecologic malignancies. Int J Radiat Oncol Biol Phys 2002; 52(5): 1330-7.
[http://dx.doi.org/10.1016/S0360-3016(01)02785-7] [PMID: 11955746]
[31]
He S, Gill BS, Heron DE, et al. Long-term outcomes using adjuvant pelvic intensity modulated radiation therapy (IMRT) for endometrial carcinoma. Pract Radiat Oncol 2017; 7(1): 19-25.
[http://dx.doi.org/10.1016/j.prro.2016.06.005] [PMID: 27527897]
[32]
Liang Y, Bydder M, Yashar CM, et al. Prospective study of functional bone marrow-sparing intensity modulated radiation therapy with concurrent chemotherapy for pelvic malignancies. Int J Radiat Oncol Biol Phys 2013; 85(2): 406-14.
[http://dx.doi.org/10.1016/j.ijrobp.2012.04.044] [PMID: 22687195]
[33]
Young AV, Wortham A, Wernick I, Evans A, Ennis RD. Atlas-based segmentation improves consistency and decreases time required for contouring postoperative endometrial cancer nodal volumes. Int J Radiat Oncol Biol Phys 2011; 79(3): 943-7.
[http://dx.doi.org/10.1016/j.ijrobp.2010.04.063] [PMID: 21281897]
[34]
Taylor A, Rockall AG, Powell MEB. An atlas of the pelvic lymph node regions to aid radiotherapy target volume definition. Clin Oncol (R Coll Radiol) 2007; 19(7): 542-50.
[http://dx.doi.org/10.1016/j.clon.2007.05.002] [PMID: 17624745]
[35]
Eifel PJ, Winter K, Morris M, et al. Pelvic irradiation with concurrent chemotherapy versus pelvic and para-aortic irradiation for high-risk cervical cancer: An update of radiation therapy oncology group trial (RTOG) 90-01. J Clin Oncol 2004; 22(5): 872-80.
[http://dx.doi.org/10.1200/JCO.2004.07.197] [PMID: 14990643]
[36]
Isik A, Soran A, Grasi A, Barry N, Sezgin E. Lymphedema after sentinel lymph node biopsy: Who is at risk? Lymphat Res Biol 2022; 20(2): 160-3.
[http://dx.doi.org/10.1089/lrb.2020.0093] [PMID: 34191608]

Rights & Permissions Print Cite
Article Metrics
20
2
Wayfinder Image
© 2024 Bentham Science Publishers | Privacy Policy