Clinical evaluation of the convolutional neural network‑based automatic delineation tool in determining the clinical target volume and organs at risk in rectal cancer radiotherapy

Huang,Yangyang; Song,Rui; Qin,Tingting; Yang,Menglin; Liu,Zongwen

doi:10.3892/ol.2024.14672

Clinical evaluation of the convolutional neural network‑based automatic delineation tool in determining the clinical target volume and organs at risk in rectal cancer radiotherapy

Authors:
- Yangyang Huang
- Rui Song
- Tingting Qin
- Menglin Yang
- Zongwen Liu
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Affiliations: Department of Radiation Oncology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450014, P.R. China
Published online on: September 6, 2024 https://doi.org/10.3892/ol.2024.14672
Article Number: 539
Copyright: © Huang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Delineating the clinical target volume (CTV) and organs at risk (OARs) is crucial in rectal cancer radiotherapy. However, the accuracy of manual delineation (MD) is variable and the process is time consuming. Automatic delineation (AD) may be a solution to produce quicker and more accurate contours. In the present study, a convolutional neural network (CNN)‑based AD tool was clinically evaluated to analyze its accuracy and efficiency in rectal cancer. CT images were collected from 148 supine patients in whom tumor stage and type of surgery were not differentiated. The rectal cancer contours consisted of CTV and OARs, where the OARs included the bladder, left and right femoral head, left and right kidney, spinal cord and bowel bag. The MD contours reviewed and modified together by a senior radiation oncologist committee were set as the reference values. The Dice similarity coefficient (DSC), Jaccard coefficient (JAC) and Hausdorff distance (HD) were used to evaluate the AD accuracy. The correlation between CT slice number and AD accuracy was analyzed, and the AD accuracy for different contour numbers was compared. The time recorded in the present study included the MD time, AD time for different CT slice and contour numbers and the editing time for AD contours. The Pearson correlation coefficient, paired‑sample t‑test and unpaired‑sample t‑test were used for statistical analyses. The results of the present study indicated that the DSC, JAC and HD for CTV using AD were 0.80±0.06, 0.67±0.08 and 6.96±2.45 mm, respectively. Among the OARs, the highest DSC and JAC using AD were found for the right and left kidney, with 0.91±0.06 and 0.93±0.04, and 0.84±0.09 and 0.88±0.07, respectively, and HD was lowest for the spinal cord with 2.26±0.82 mm. The lowest accuracy was found for the bowel bag. The more CT slice numbers, the higher the accuracy of the spinal cord analysis. However, the contour number had no effect on AD accuracy. To obtain qualified contours, the AD time plus editing time was 662.97±195.57 sec, while the MD time was 3294.29±824.70 sec. In conclusion, the results of the present study indicate that AD can significantly improve efficiency and a higher number of CT slices and contours can reduce AD efficiency. The AD tool provides acceptable CTV and OARs for rectal cancer and improves efficiency for delineation.

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1	Siegel RL, Miller KD, Fuchs HE and Jemal A: Cancer statistics, 2022. CA Cancer J Clin. 72:7–33. 2022. View Article : Google Scholar : PubMed/NCBI
2	Cao W, Chen HD, Yu YW, Li N and Chen WQ: Changing profiles of cancer burden worldwide and in China: A secondary analysis of the global cancer statistics 2020. Chin Med J (Engl). 134:783–791. 2021. View Article : Google Scholar : PubMed/NCBI
3	Hanna CR, Slevin F, Appelt A, Beavon M, Adams R, Arthur C, Beasley M, Duffton A, Gilbert A, Gollins S, et al: Intensity-modulated radiotherapy for rectal cancer in the UK in 2020. Clin Oncol (R Coll Radiol). 33:214–223. 2021. View Article : Google Scholar : PubMed/NCBI
4	Chen AM, Chin R, Beron P, Yoshizaki T, Mikaeilian AG and Cao M: Inadequate target volume delineation and local-regional recurrence after intensity-modulated radiotherapy for human papillomavirus-positive oropharynx cancer. Radiother Oncol. 123:412–418. 2017. View Article : Google Scholar : PubMed/NCBI
5	Walker GV, Awan M, Tao R, Koay EJ, Boehling NS, Grant JD, Sittig DF, Gunn GB, Garden AS, Phan J, et al: Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer. Radiother Oncol. 112:321–325. 2014. View Article : Google Scholar : PubMed/NCBI
6	Rezaeijo SM, Jafarpoor Nesheli S, Fatan Serj M and Tahmasebi Birgani MJ: Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model. Quant Imaging Med Surg. 12:4786–4804. 2022. View Article : Google Scholar : PubMed/NCBI
7	Voet PWJ, Dirkx MLP, Teguh DN, Hoogeman MS, Levendag PC and Heijmen BJM: Does atlas-based autosegmentation of neck levels require subsequent manual contour editing to avoid risk of severe target underdosage? A dosimetric analysis. Radiother Oncol. 98:373–377. 2011. View Article : Google Scholar : PubMed/NCBI
8	Daisne JF and Blumhofer A: Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: A clinical validation. Radiat Oncol. 8:1542013. View Article : Google Scholar : PubMed/NCBI
9	Ma CY, Zhou JY, Xu XT, Guo J, Han MF, Gao YZ, Du H, Stahl JN and Maltz JS: Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer. J Appl Clin Med Phys. 23:e134702022. View Article : Google Scholar : PubMed/NCBI
10	Marin T, Zhuo Y, Lahoud RM, Tian F, Ma X, Xing F, Moteabbed M, Liu X, Grogg K, Shusharina N, et al: Deep learning-based GTV contouring modeling inter- and intra-observer variability in sarcomas. Radiother Oncol. 167:269–276. 2022. View Article : Google Scholar : PubMed/NCBI
11	Casati M, Piffer S, Calusi S, Marrazzo L, Simontacchi G, Di Cataldo V, Greto D, Desideri I, Vernaleone M, Francolini G, et al: Clinical validation of an automatic atlas-based segmentation tool for male pelvis CT images. J Appl Clin Med Phys. 23:e135072022. View Article : Google Scholar : PubMed/NCBI
12	Vinod SK, Min M, Jameson MG and Holloway LC: A review of interventions to reduce inter-observer variability in volume delineation in radiation oncology. J Med Imaging Radiat Oncol. 60:393–406. 2016. View Article : Google Scholar : PubMed/NCBI
13	Wang Z, Chang Y, Peng Z, Lv Y, Shi W, Wang F, Pei X and Xu XG: 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. 21:272–279. 2020. View Article : Google Scholar : PubMed/NCBI
14	Young AV, Wortham A, Wernick I, Evans A and Ennis RD: Atlas-based segmentation improves consistency and decreases time required for contouring postoperative endometrial cancer nodal volumes. Int J Radiat Oncol Biol Phys. 79:943–947. 2011. View Article : Google Scholar : PubMed/NCBI
15	Song Y, Hu J, Wu Q, Xu F, Nie S, Zhao Y, Bai S and Yi Z: Automatic delineation of the clinical target volume and organs at risk by deep learning for rectal cancer postoperative radiotherapy. Radiother Oncol. 145:186–192. 2020. View Article : Google Scholar : PubMed/NCBI
16	Chen W, Wang C, Zhan W, Jia Y, Ruan F, Qiu L, Yang S and Li Y: A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer. Sci Rep. 11:230022021. View Article : Google Scholar : PubMed/NCBI
17	Piqeur F, Hupkens BJP, Nordkamp S, Witte MG, Meijnen P, Ceha HM, Berbee M, Dieters M, Heyman S, Valdman A, et al: Development of a consensus-based delineation guideline for locally recurrent rectal cancer. Radiother Oncol. 177:214–221. 2022. View Article : Google Scholar : PubMed/NCBI
18	Mackay K, Bernstein D, Glocker B, Kamnitsas K and Taylor A: A review of the metrics used to assess auto-contouring systems in radiotherapy. Clin Oncol (R Coll Radiol). 35:354–369. 2023. View Article : Google Scholar : PubMed/NCBI
19	Li Y, Wu W, Sun Y, Yu D, Zhang Y, Wang L, Wang Y, Zhang X and Lu Y: The clinical evaluation of atlas-based auto-segmentation for automatic contouring during cervical cancer radiotherapy. Front Oncol. 12:9450532022. View Article : Google Scholar : PubMed/NCBI
20	Men K, Dai J and Li Y: Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys. 44:6377–6389. 2017. View Article : Google Scholar : PubMed/NCBI
21	Ronneberger O, Fischer P and Brox T: U-net: Convolutional networks for biomedical image segmentation: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings Part III. Springer International Publishing; Cham: pp. 234–241. 2015
22	Shen G, Jin X, Sun C and Li Q: Artificial intelligence radiotherapy planning: Automatic segmentation of human organs in CT images based on a modified convolutional neural network. Front Public Health. 10:8131352022. View Article : Google Scholar : PubMed/NCBI
23	Luan S, Xue X, Ding Y, Wei W and Zhu B: Adaptive attention convolutional neural network for liver tumor segmentation. Front Oncol. 11:6808072021. View Article : Google Scholar : PubMed/NCBI
24	Wu Y, Kang K, Han C, Wang S, Chen Q, Chen Y, Zhang F and Liu Z: A blind randomized validated convolutional neural network for auto-segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy. Cancer Med. 11:166–175. 2022. View Article : Google Scholar : PubMed/NCBI
25	Liu C, Gardner SJ, Wen N, Elshaikh MA, Siddiqui F, Movsas B and Chetty IJ: Automatic segmentation of the prostate on CT images using deep neural networks (DNN). Int J Radiat Oncol Biol Phys. 104:924–932. 2019. View Article : Google Scholar : PubMed/NCBI
26	Liu Z, Liu X, Xiao B, Wang S, Miao Z, Sun Y and Zhang F: Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network. Phys Med. 69:184–191. 2020. View Article : Google Scholar : PubMed/NCBI
27	To MNN, Vu DQ, Turkbey B, Choyke PL and Kwak JT: Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging. Int J Comput Assist Radiol Surg. 13:1687–1696. 2018. View Article : Google Scholar : PubMed/NCBI
28	Breto A, Zavala-Romero O, Asher D, Baikovitz J, Ford J, Stoyanova R and Portelance L: A deep learning pipeline for per-fraction automatic segmentation of GTV and OAR in cervical cancer. Int J Radiat Oncol Biol Phys. 105 (Suppl 1):S2022019. View Article : Google Scholar
29	Bi N, Wang J, Zhang T, Chen X, Xia W, Miao J, Xu K, Wu L, Fan Q, Wang L, et al: Deep learning improved clinical target volume contouring quality and efficiency for postoperative radiation therapy in non-small cell lung cancer. Front Oncol. 9:11922019. View Article : Google Scholar : PubMed/NCBI
30	Sha X, Wang H, Sha H, Xie L, Zhou Q, Zhang W and Yin Y: Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network. Front Oncol. 13:11724242023. View Article : Google Scholar : PubMed/NCBI
31	Li J, Song Y, Wu Y, Liang L, Li G and Bai S: Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy. Front Oncol. 13:11583152023. View Article : Google Scholar : PubMed/NCBI
32	Chen Y, Li J, Xiao H, Jin X, Yan S and Feng J: Dual path networks. Adv Neural Inf Process Syst. 30:2017.PubMed/NCBI
33	Liu Z, Liu X, Guan H, Zhen H, Sun Y, Chen Q, Chen Y, Wang S and Qiu J: Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy. Radiother Oncol. 153:172–179. 2020. View Article : Google Scholar : PubMed/NCBI
34	Liu Z, Liu F, Chen W, Liu X, Hou X, Shen J, Guan H, Zhen H, Wang S, Chen Q, et al: Automatic segmentation of clinical target volumes for post-modified radical mastectomy radiotherapy using convolutional neural networks. Front Oncol. 10:5813472021. View Article : Google Scholar : PubMed/NCBI
35	Valentini V, Gambacorta MA, Barbaro B, Chiloiro G, Coco C, Das P, Fanfani F, Joye I, Kachnic L, Maingon P, et al: International consensus guidelines on clinical target volume delineation in rectal cancer. Radiother Oncol. 120:195–201. 2016. View Article : Google Scholar : PubMed/NCBI
36	Gay HA, Barthold HJ, O'Meara E, Bosch WR, El Naqa I, Al-Lozi R, Rosenthal SA, Lawton C, Lee WR, Sandler H, et al: Pelvic normal tissue contouring guidelines for radiation therapy: A radiation therapy oncology group consensus panel atlas. Int J Radiat Oncol Biol Phys. 83:e353–e362. 2012. View Article : Google Scholar : PubMed/NCBI
37	Yeghiazaryan V and Voiculescu I: Family of boundary overlap metrics for the evaluation of medical image segmentation. J Med Imaging (Bellingham). 5:0150062018.PubMed/NCBI
38	Geets X, Daisne JF, Arcangeli S, Coche E, De Poel M, Duprez T, Nardella G and Grégoire V: Inter-observer variability in the delineation of pharyngo-laryngeal tumor and parotid glands and cervical spinal cord: Comparison between CT-scan and MRI. Radiother Oncol. 77:25–31. 2005. View Article : Google Scholar : PubMed/NCBI
39	Brouwer CL, Steenbakkers RJHM, Bourhis J, Budach W, Grau C, Grégoire V, Van Herk M, Lee A, Maingon P, Nutting C, et al: CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG oncology and TROG consensus guidelines. Radiother Oncol. 117:83–90. 2015. View Article : Google Scholar : PubMed/NCBI
40	Rhee DJ, Jhingran A, Rigaud B, Netherton T, Cardenas CE, Zhang L, Vedam S, Kry S, Brock KK, Shaw W, et al: Automatic contouring system for cervical cancer using convolutional neural networks. Med Phys. 47:5648–5658. 2020. View Article : Google Scholar : PubMed/NCBI
41	Zabihollahy F, Viswanathan AN, Schmidt EJ and Lee J: Fully automated segmentation of clinical target volume in cervical cancer from magnetic resonance imaging with convolutional neural network. J Appl Clin Med Phys. 23:e137252022. View Article : Google Scholar : PubMed/NCBI
42	Mohammadi R, Shokatian I, Salehi M, Arabi H, Shiri I and Zaidi H: Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer. Radiother Oncol. 159:231–240. 2021. View Article : Google Scholar : PubMed/NCBI
43	Ju Z, Guo W, Gu S, Zhou J, Yang W, Cong X, Dai X, Quan H, Liu J, Qu B and Liu G: CT based automatic clinical target volume delineation using a dense-fully connected convolution network for cervical Cancer radiation therapy. BMC Cancer. 21:2432021. View Article : Google Scholar : PubMed/NCBI
44	Chan JW, Kearney V, Haaf S, Wu S, Bogdanov M, Reddick M, Dixit N, Sudhyadhom A, Chen J, Yom SS and Solberg TD: A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning. Med Phys. 46:2204–2213. 2019. View Article : Google Scholar : PubMed/NCBI
45	Ginn JS, Gay HA, Hilliard J, Shah J, Mistry N, Möhler C, Hugo GD and Hao Y: A clinical and time savings evaluation of a deep learning automatic contouring algorithm. Med Dosim. 48:55–60. 2023. View Article : Google Scholar : PubMed/NCBI
46	Åström LM, Behrens CP, Calmels L, Sjöström D, Geertsen P, Mouritsen LS, Serup-Hansen E, Lindberg H and Sibolt P: Online adaptive radiotherapy of urinary bladder cancer with full re-optimization to the anatomy of the day: Initial experience and dosimetric benefits. Radiother Oncol. 171:37–42. 2022. View Article : Google Scholar : PubMed/NCBI
47	D'Aviero A, Re A, Catucci F, Piccari D, Votta C, Piro D, Piras A, Di Dio C, Iezzi M, Preziosi F, et al: Clinical validation of a deep-learning segmentation software in head neck: An early analysis in a developing radiation oncology center. Int J Environ Res Public Health. 19:90572022. View Article : Google Scholar : PubMed/NCBI
48	Lustberg T, van Soest J, Gooding M, Peressutti D, Aljabar P, van der Stoep J, van Elmpt W and Dekker A: Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol. 126:312–317. 2018. View Article : Google Scholar : PubMed/NCBI
49	Hu Y, Nguyen H, Smith C, Chen T, Byrne M, Archibald-Heeren B, Rijken J and Aland T: Clinical assessment of a novel machine-learning automated contouring tool for radiotherapy planning. J Appl Clin Med Phys. 24:e139492023. View Article : Google Scholar : PubMed/NCBI