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

doi:10.3969/j.issn.1006-5725.2024.02.005

Abstract

Abstract:

Objective To explore the feasibility of automatic segmentation of clinical target volume （CTV） and organs at risk （OARs） for cervical cancer using AccuLearning （AL） based on geometric and dosimetric indices. Methods Seventy-five CT localization images with manual contouring data of postoperative cervical cancer were enrolled in this study. Sixty cases were randomly selected to trained to generate automatic segmentation model by AL， and the CTV and OARs of the remaining 15 cases were automatically contoured. Radiotherapy plans on the automatic segmentation contours were imported on the CT images of manual contours. The efficiency， Dice similarity coefficient （DSC）， Hausdorff distance （HD） and dosimetric parameters were compared between the two methods. Results The time of automatic segmentation was significantly shorter than that of the manual contour（P < 0.05）. The DSC of all structures were ≥ 0.87. The HD of bowel bag and rectum were about 10 mm， and that of the rest of OARs were less than 5 mm. CTV （D98， V90%， V95%， Dmean， HI）， bowel bag （V50） and bladder （V50） had significant differences in dosimetric comparison（P < 0.05）. Conclusion The automatic segmentation model based on AL can improve the efficiency of radiotherapy. Automatic segmentation of OARs has the potential of clinical application， while that of CTV still needs to be further modified.

Key words: automatic segmentation, cervical cancer, clinical target volume, organs at risk, dosimetry

CLC Number:

R811.1

Fei CHEN,Xiaoqin GONG,Yunpeng YU,Tao YOU,Xu WANG,Chunhua DAI,Jing HU. Feasibility of automatic segmentation of CTV and OARs in postoperative radiotherapy for cervical cancer using AccuLearning[J]. The Journal of Practical Medicine, 2024, 40(2): 153-157.

Figures/Tables 6

Tab.1

Fig.1

Tab.2

Tab.3

Fig.2

Tab.4

References 26

1	CHARGARI C， PEIGNAUX K， ESCANDE A， et al. Radiotherapy of cervical cancer［J］. Cancer Radiother， 2022， 26（1/2）： 298-308. doi:10.1016/j.canrad.2021.11.009 doi: 10.1016/j.canrad.2021.11.009
2	POLGAR C， MAJOR T， VARGA S. Radiotherapy and radio-chemotherapy of cervical cancer［J］.Magy Onkol， 2022， 66（4）： 307-314.
3	GUO Q， WANG R， JIN D， et al. Comparison of adjuvant chemoradiotherapy versus radiotherapy in early-stage cervical cancer patients with intermediate-risk factors： A systematic review and meta-analysis［J］. Taiwan J Obstet Gyne， 2022， 61（1）： 15-23. doi:10.1016/j.tjog.2021.11.006 doi: 10.1016/j.tjog.2021.11.006
4	FAYE M D， ALFIERI J. Advances in Radiation Oncology for the Treatment of Cervical Cancer［J］. Curr Oncol， 2022， 29（2）： 928-944. doi:10.3390/curroncol29020079 doi: 10.3390/curroncol29020079
5	陈默，黄荣，蒋军. 调强放射治疗条件下根治性宫颈癌的长期生存报道［J］. 实用医学杂志， 2020， 36（20）： 2810-2814. doi:10.3969/j.issn.1006-5725.2020.20.013 doi: 10.3969/j.issn.1006-5725.2020.20.013
6	CHOPRA S， GUPTA S， KANNAN S， et al. Late Toxicity After Adjuvant Conventional Radiation Versus Image-Guided Intensity-Modulated Radiotherapy for Cervical Cancer （PARCER）： A Randomized Controlled Trial［J］.J Clin Oncol， 2021， 39（33）： 3682-3692. doi:10.1200/jco.20.02530 doi: 10.1200/jco.20.02530
7	RIOS I， VASQUEZ I， CUERVO E， et al. Problems and solutions in IGRT for cervical cancer［J］. Rep Pract Oncol Radiother， 2018， 23（6）： 517-527. doi:10.1016/j.rpor.2018.05.002 doi: 10.1016/j.rpor.2018.05.002
8	WANG J， CHEN Z， YANG C， et al. Evaluation Exploration of Atlas-Based and Deep Learning-Based Automatic Contouring for Nasopharyngeal Carcinoma［J］. Front Oncol， 2022， 12： 833816. doi:10.3389/fonc.2022.833816 doi: 10.3389/fonc.2022.833816
9	KANO Y， IKUSHIMA H， SASAKI M， et al. Automatic contour segmentation of cervical cancer using artificial intelligence［J］.J Radiat Res， 2021， 62（5）： 934-944. doi:10.1093/jrr/rrab070 doi: 10.1093/jrr/rrab070
10	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
11	JU Z， WU Q， YANG W， et al. Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples［J］. Acta Oncol， 2020， 59（8）： 933-939. doi:10.1080/0284186x.2020.1775290 doi: 10.1080/0284186x.2020.1775290
12	LIU Z， LIU X， GUAN H， et al. Development and validation of a deep learning algorithm for autodelineation of clinical target volume and organs at risk in cervical cancer radiotherapy［J］. Radiother Oncol， 2020， 153： 172-179. doi:10.1016/j.radonc.2020.09.060 doi: 10.1016/j.radonc.2020.09.060
13	NIE S， WEI Y， ZHAO F， et al. A dual deep neural network for auto-delineation in cervical cancer radiotherapy with clinical validation［J］. Radiat Oncol， 2022， 17（1）： 182. doi:10.1186/s13014-022-02157-5 doi: 10.1186/s13014-022-02157-5
14	XIAO C， JIN J， YI J， et al. RefineNet-based 2D and 3D automatic segmentations for clinical target volume and organs at risks for patients with cervical cancer in postoperative radiotherapy［J］. J Appl Clin Med Phys， 2022， 23（7）： e13631. doi:10.1002/acm2.13631 doi: 10.1002/acm2.13631
15	DING Y， CHEN Z， WANG Z， et al. Three-dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images［J］. J Appl Clin Med Phys， 2022， 23（4）： e13566. doi:10.1002/acm2.13566 doi: 10.1002/acm2.13566
16	钱涵，汪红艳，王凡. 呼吸触发前瞻门控在早期非小细胞肺癌立体定向放射治疗中剂量学优势［J］. 实用医学杂志， 2023， 39（1）： 86-91.
17	TAN MBBS MRCP FRCR MD L T， TANDERUP PHD K， KIRISITS PHD C， et al. Image-guided Adaptive Radiotherapy in Cervical Cancer［J］. Semin Radiat Oncol， 2019， 29（3）： 284-298. doi:10.1016/j.semradonc.2019.02.010 doi: 10.1016/j.semradonc.2019.02.010
18	SHELLEY C E， BOLT M A， HOLLINGDALE R， et al. Implementing cone-beam computed tomography-guided online adaptive radiotherapy in cervical cancer［J］.Clin Transl Radiat Oncol， 2023， 40： 100596. doi:10.1016/j.ctro.2023.100596 doi: 10.1016/j.ctro.2023.100596
19	BRETO A L， SPIELER B， ZAVALA-ROMERO O， et al. Deep Learning for Per-Fraction Automatic Segmentation of Gross Tumor Volume （GTV） and Organs at Risk （OARs） in Adaptive Radiotherapy of Cervical Cancer［J］. Front Oncol， 2022， 12： 854349. doi:10.3389/fonc.2022.854349 doi: 10.3389/fonc.2022.854349
20	CHOI M S， CHOI B S， CHUNG S Y， et al. Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer［J］. Radiother Oncol， 2020， 153： 139-145. doi:10.1016/j.radonc.2020.09.045 doi: 10.1016/j.radonc.2020.09.045
21	URAGO Y， OKAMOTO H， KANEDA T， et al. Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models［J］. Radiat Oncol， 2021， 16（1）： 175. doi:10.1186/s13014-021-01896-1 doi: 10.1186/s13014-021-01896-1
22	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
23	ZHU J， CHEN X， YANG B， et al. Evaluation of Automatic Segmentation Model With Dosimetric Metrics for Radiotherapy of Esophageal Cancer［J］. Front Oncol， 2020， 10： 564737. doi:10.3389/fonc.2020.564737 doi: 10.3389/fonc.2020.564737
24	KAWULA M， PURICE D， LI M， et al. Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer［J］. Radiat Oncol， 2022， 17（1）： 21. doi:10.1186/s13014-022-01985-9 doi: 10.1186/s13014-022-01985-9
25	FUNG N T C， HUNG W M， SZE C K， et al. Automatic segmentation for adaptive planning in nasopharyngeal carcinoma IMRT： Time， geometrical， and dosimetric analysis［J］. Med Dosim， 2020， 45（1）： 60-65. doi:10.1016/j.meddos.2019.06.002 doi: 10.1016/j.meddos.2019.06.002
26	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

参数	手动勾画（s）	自动勾画（s）	时间节省百分比（%）	t值	P值
CTV	3 158.00 ± 405.78	34.93 ± 3.77	98.89	29.873	< 0.001
肠袋	1 382.00 ± 145.71	33.80 ± 2.78	97.55	35.829	< 0.001
直肠	174.20 ± 13.76	34.00 ± 2.90	80.48	39.405	< 0.001
膀胱	212.80 ± 12.66	34.27 ± 2.74	83.90	50.548	< 0.001
骨髓	1 582.87 ± 51.81	34.00 ± 2.98	97.85	115.394	< 0.001
右侧股骨头	138.60 ± 7.50	34.00 ± 2.80	75.47	49.138	< 0.001
左侧股骨头	137.40 ± 8.93	34.40 ± 2.61	74.64	46.195	< 0.001

参数	CTV	肠袋	直肠	膀胱	骨髓	右侧股骨头	左侧股骨头
DSC	0.90 ± 0.02	0.90 ± 0.03	0.87 ± 0.07	0.91 ± 0.08	0.92 ± 0.01	0.94 ± 0.03	0.94 ± 0.02
HD（mm）	4.23 ± 0.92	10.58 ± 3.43	9.48 ± 7.82	4.48 ± 3.97	1.56 ± 0.33	1.79 ± 0.83	2.55 ± 0.97

研究	CTV	肠袋	直肠	膀胱	骨髓	右侧股骨头	左侧股骨头
JU等^［11］	-	0.87（小肠）	0.87	0.95	-	0.92	0.92
LIU等^［12］	0.86	0.85	0.82	0.91	0.85	0.90	0.90
NIE等^［13］	0.85	0.89	0.84	0.93	-	0.93	0.92
XIAO等^［14］	0.82	0.90（小肠）	0.88	0.95		0.97	0.97
DING等^［15］	0.85	0.82（小肠）	0.85	0.94	0.92	0.81	0.82
本研究	0.90	0.90	0.87	0.91	0.92	0.94	0.94

参数	手动勾画	自动勾画	t值	P值
CTV
D98（Gy）	47.54 ± 1.00	49.70 ± 0.05	-8.212	< 0.001
D2（Gy）	51.52 ± 0.12	51.52 ± 0.13	-0.435	0.670
V90（%）	99.25 ± 0.49	100.00 ± 0.00	-5.856	< 0.001
V95（%）	98.09 ± 0.85	100.00 ± 0.00	-8.675	< 0.001
Dmean（Gy）	50.64 ± 0.09	50.82 ± 0.05	-7.315	< 0.001
HI	1.05 ± 0.01	1.03 ± 0.00	7.005	< 0.001
肠袋
V30（%）	36.38 ± 4.52	34.92 ± 1.32	1.472	0.163
V40（%）	17.39 ± 4.04	15.76 ± 2.52	2.049	0.060
V50（%）	4.16 ± 2.07	2.95 ± 1.01	2.579	0.022
Dmean（Gy）	26.25 ± 1.88	26.01 ± 1.06	0.558	0.586
直肠
V30（%）	84.88 ± 3.90	85.94 ± 3.56	-1.036	0.318
V40（%）	43.35 ± 6.44	44.51 ± 5.37	-0.927	0.370
V50（%）	2.59 ± 1.68	2.31 ± 1.63	0.622	0.544
Dmean（Gy）	37.19 ± 0.68	37.40 ± 0.74	-0.922	0.372
膀胱
V50（%）	19.95 ± 7.50	17.01 ± 5.29	4.063	0.001
Dmean（Gy）	43.31 ± 2.32	42.64 ± 2.00	1.927	0.074
骨髓Dmean（Gy）	25.70 ± 0.84	25.75 ± 0.88	-0.996	0.336
右侧股骨头Dmean（Gy）	11.41 ± 1.33	11.32 ± 1.41	0.636	0.535
左侧侧股骨头Dmean（Gy）	11.20 ± 1.33	11.43±1.55	-1.954	0.071

[1]	Jing HU,Xu WANG,Xiaoqin GONG,Rui LING,Tao YOU,Chunhua DAI,Ye TIAN,Fei. CHEN. Analysis of dosimetric parameters of acute radiation enteritis in cervical cancer patients treated with concurrent chemoradiotherapy [J]. The Journal of Practical Medicine, 2024, 40(5): 672-676.
[2]	Baoni JIANG,Meiqin ZHANG,Lu YANG,Zhiyuan CHEN,Na′na HAN. Application and prospects of ctDNA detection in HPV associated cervical cancer [J]. The Journal of Practical Medicine, 2024, 40(2): 129-132.
[3]	Ping LI,Tuo TANG,Aixue ZHENG,Luping ZHANG,Tao WANG,Xian HONG,Zhihui DENG. Generation of MCM2 gene inducible knockout cervical cancer HeLa cells and its effect on DNA replication [J]. The Journal of Practical Medicine, 2024, 40(2): 133-139.
[4]	Shenglong YUAN,Huanhuan HU,Zhen GONG. Efficacy of the ACS NSQIP surgical risk calculator in open radical hysterectomy [J]. The Journal of Practical Medicine, 2024, 40(2): 140-145.
[5]	Min WANG,Dan MU,Dejun KONG,Li YANG,Lu YE,Dan HE. HPV16 E6 regulates miR-23a expression and promotes invasion and migration of cervical cancer cells [J]. The Journal of Practical Medicine, 2024, 40(2): 146-152.
[6]	Yongzhen ZHANG,Shanshan WANG,Hailing ZHAO,Liwei. XU. Levels and clinical significance of serum miR-651 and miR-630 in patients with cervical cancer [J]. The Journal of Practical Medicine, 2024, 40(2): 158-162.
[7]	Pixi WEI,Yu DENG,Cailing ZHAO,Liu XU,Min ZHANG. The expression of WDR5 in cervical cancer tissue and its relationship with clinical and pathological characteristics of patients [J]. The Journal of Practical Medicine, 2024, 40(2): 169-173.
[8]	Qingwei ZHANG,Rutie. YIN. Hotspots and advances on diagnosis and treatment for cervical cancer [J]. The Journal of Practical Medicine, 2024, 40(17): 2357-2362.
[9]	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.
[10]	Kai LIAO,Yunhong TIAN,Ronghui ZHENG,Caixian HE,Jiyong PENG,Huijun LI. Reduction of head and neck lymphedema by placing dose limiting rings in the anterior and posterior regions of the neck for treating early nasopharyngeal carcinoma using intensity-modulated radiotherapy： A dosimetric perspective [J]. The Journal of Practical Medicine, 2024, 40(12): 1659-1664.
[11]	ZHONG Hua, ZENG Meiqing, ZHOU Yuting, HE Shunqing, LU Binghui.. Transfection of human cervical cancer cell HeLa by ultrasound microbubble contrast agent carrying pcD⁃ NA⁃STC1 [J]. The Journal of Practical Medicine, 2023, 39(8): 936-943.
[12]	LU Yanyue, ZHAO Xinxin, LIU Liyan, WANG Kai, YE Jingwen, LIU Lingyun, WANG Baojin.. Effect and mechanism of endogenous aromatic ligand ITE on the biological behavior of cervical cancer cells [J]. The Journal of Practical Medicine, 2023, 39(6): 679-687.
[13]	Huaming ZHANG,Wanshan HE,Yun HAN,Guanqiao CHEN,Bin CHEN,Zhifu WEI,Hengying WU,Bin. WEN. Screening of gene differential expression of adenosine deaminase RNA specific 1 in cervical cancer cells based on transcriptome sequencing technology [J]. The Journal of Practical Medicine, 2023, 39(24): 3169-3174.
[14]	Guang YANG,Danfeng ZHANG,Xiaona FENG,Yan. ZHANG. Expression of programmed cell death protein 5 in cervical cancer and its relationship with lymph node metastasis [J]. The Journal of Practical Medicine, 2023, 39(24): 3210-3213.
[15]	Ping YIN,Hanzi XU,Chenjing. ZHU. Application of ctDNA in gynecological malignant cancer [J]. The Journal of Practical Medicine, 2023, 39(17): 2153-2158.

Feasibility of automatic segmentation of CTV and OARs in postoperative radiotherapy for cervical cancer using AccuLearning

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 26

Related Articles 15

Recommended Articles

Metrics

Comments