Journal of International Oncology ›› 2022, Vol. 49 ›› Issue (3): 168-172.doi: 10.3760/cma.j.cn371439-20220104-00028
• Reviews • Previous Articles Next Articles
Yan Danfang1, Wang Lihong1, Ye Hongxing2, Yan Senxiang1()
Received:
2022-01-04
Revised:
2022-02-13
Online:
2022-03-08
Published:
2022-03-22
Contact:
Yan Senxiang
E-mail:yansenxiang@zju.edu.cn
Supported by:
Yan Danfang, Wang Lihong, Ye Hongxing, Yan Senxiang. Application of artificial intelligence in the target delineation of radiotherapy[J]. Journal of International Oncology, 2022, 49(3): 168-172.
[1] |
Sheng K. Artificial intelligence in radiotherapy: a technological review[J]. Front Med, 2020, 14(4):431-449. DOI: 10.1007/s11684-020-0761-1.
doi: 10.1007/s11684-020-0761-1 |
[2] |
van Dijk LV, Van den Bosch L, Aljabar P, et al. Improving automa-tic delineation for head and neck organs at risk by deep learning contouring[J]. Radiother Oncol, 2020, 142:115-123. DOI: 10.1016/j.radonc.2019.09.022.
doi: 10.1016/j.radonc.2019.09.022 |
[3] |
Ahn SH, Kim E, Kim C, et al. Deep learning method for prediction of patient-specific dose distribution in breast cancer[J]. Radiat Oncol, 2021, 16(1):154. DOI: 10.1186/s13014-021-01864-9.
doi: 10.1186/s13014-021-01864-9 |
[4] |
Osman AFI, Maalej NM. Applications of machine and deep learning to patient-specific IMRT/VMAT quality assurance[J]. J Appl Clin Med Phys, 2021, 22(9):20-36. DOI: 10.1002/acm2.13375.
doi: 10.1002/acm2.13375 |
[5] |
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. DOI: 10.3969/j.issn.1009-0460.2020.07.015.
doi: 10.3969/j.issn.1009-0460.2020.07.015 |
[6] |
Teng L, Li H, Karim S. DMCNN: a deep multiscale convolutional neural network model for medical image segmentation[J]. J Healthc Eng, 2019, 2019:8597606. DOI: 10.1155/2019/8597606.
doi: 10.1155/2019/8597606 pmid: 31949890 |
[7] |
LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436-444. DOI: 10.1038/nature14539.
doi: 10.1038/nature14539 |
[8] |
邓金城, 彭应林, 刘常春, 等. 深度卷积神经网络在放射治疗计划图像分割中的应用[J]. 中国医学物理学杂志, 2018, 35(6):621-627. DOI: 10.3969/j.issn.1005-202X.2018.06.001.
doi: 10.3969/j.issn.1005-202X.2018.06.001 |
[9] |
Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks[J]. Med Phys, 2017, 44(2):547-557. DOI: 10.1002/mp.12045.
doi: 10.1002/mp.12045 pmid: 28205307 |
[10] |
Tong N, Gou S, Yang S, et al. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks[J]. Med Phys, 2018, 45(10):4558-4567. DOI: 10.1002/mp.13147.
doi: 10.1002/mp.13147 pmid: 30136285 |
[11] |
Tong N, Gou S, Yang S, et al. Shape constrained fully convolutio-nal DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images[J]. Med Phys, 2019, 46(6):2669-2682. DOI: 10.1002/mp.13553.
doi: 10.1002/mp.13553 pmid: 31002188 |
[12] |
Ye Y, Cai Z, Huang B, et al. Fully-automated segmentation of nasopharyngeal carcinoma on dual-sequence MRI using convolutional neural networks[J]. Front Oncol, 2020, 10:166. DOI: 10.3389/fonc.2020.00166.
doi: 10.3389/fonc.2020.00166 |
[13] |
曹祺炜, 王峰, 牛锦. 基于3D卷积神经网络的脑肿瘤医学图像分割优化[J]. 现代电子技术, 2020, 43(3):74-77. DOI: 10.16652/j.issn.1004-373x.2020.03.018.
doi: 10.16652/j.issn.1004-373x.2020.03.018 |
[14] |
王沛沛, 李金凯, 李彩虹, 等. 基于人工智能技术的危及器官自动勾画在胸部肿瘤中的应用[J]. 中国医学物理学杂志, 2019, 36(11):1346-1349. DOI: 10.3969/j.issn.1005-202X.2019.11.019.
doi: 10.3969/j.issn.1005-202X.2019.11.019 |
[15] |
Lee H, Lee E, Kim N, et al. Clinical evaluation of commercial atlas-based auto-segmentation in the head and neck region[J]. Front Oncol, 2019, 9:239. DOI: 10.3389/fonc.2019.00239.
doi: 10.3389/fonc.2019.00239 |
[16] |
Fritscher KD, Peroni M, Zaffino P, et al. Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours[J]. Med Phys, 2014, 41(5):051910. DOI: 10.1118/1.4871623.
doi: 10.1118/1.4871623 |
[17] |
Wang X, Miralbell R, Fargier-Bochaton O, et al. Atlas sampling for prone breast automatic segmentation of organs at risk: the importance of patients' body mass index and breast cup size for an optimized contouring of the heart and the coronary vessels[J]. Technol Cancer Res Treat, 2020, 19:1533033820920624. DOI: 10.1177/1533033820920624.
doi: 10.1177/1533033820920624 |
[18] |
Vrtovec T, Močnik D, Strojan P, et al. Auto-segmentation of organs at risk for head and neck radiotherapy planning: from atlas-based to deep learning methods[J]. Med Phys, 2020, 47(9):e929-e950. DOI: 10.1002/mp.14320.
doi: 10.1002/mp.14320 |
[19] |
Zabel WJ, Conway JL, Gladwish A, et al. Clinical evaluation of deep learning and atlas-based auto-contouring of bladder and rectum for prostate radiation therapy[J]. Pract Radiat Oncol, 2021, 11(1):e80-e89. DOI: 10.1016/j.prro.2020.05.013.
doi: 10.1016/j.prro.2020.05.013 |
[20] |
Zhu W, Huang Y, Zeng L, et al. AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy[J]. Med Phys, 2019, 46(2):576-589. DOI: 10.1002/mp.13300.
doi: 10.1002/mp.13300 |
[21] |
Dolz J, Betrouni N, Quidet M, et al. Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: a clinical study[J]. Comput Med Imaging Graph, 2016, 52:8-18. DOI: 10.1016/j.compmedimag.2016.03.003.
doi: 10.1016/j.compmedimag.2016.03.003 |
[22] |
Močnik D, Ibragimov B, Xing L, et al. Segmentation of parotid glands from registered CT and MR images[J]. Phys Med, 2018, 52:33-41. DOI: 10.1016/j.ejmp.2018.06.012.
doi: S1120-1797(18)30494-0 pmid: 30139607 |
[23] |
Feng CH, Cornell M, Moore KL, et al. Automated contouring and planning pipeline for hippocampal-avoidant whole-brain radiotherapy[J]. Radiat Oncol, 2020, 15(1):251. DOI: 10.1186/s13014-020-01689-y.
doi: 10.1186/s13014-020-01689-y |
[24] |
Chung SY, Chang JS, Choi MS, et al. Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery[J]. Radiat Oncol, 2021, 16(1):44. DOI: 10.1186/s13014-021-01771-z.
doi: 10.1186/s13014-021-01771-z |
[25] |
Speight R, Karakaya E, Prestwich R, et al. Evaluation of atlas based auto-segmentation for head and neck target volume delineation in adaptive/replan IMRT[J]. J Phys Conf Ser, 2014, 489(1):012060. DOI: 10.1088/1742-6596/489/1/012060.
doi: 10.1088/1742-6596/489/1/012060 |
[26] |
Bell LR, Dowling JA, Pogson EM, et al. Atlas-based segmentation technique incorporating inter-observer delineation uncertainty for whole breast[J]. J Phys Conf Ser, 2016, 777(1):012002. DOI: 10.1088/1742-6596/777/1/012002.
doi: 10.1088/1742-6596/777/1/012002 |
[27] |
Wong Yuzhen N, Barrett S. A review of automatic lung tumour segmentation in the era of 4DCT[J]. Rep Pract Oncol Radiother, 2019, 24(2):208-220. DOI: 10.1016/j.rpor.2019.01.003.
doi: 10.1016/j.rpor.2019.01.003 |
[28] |
Song Y, Hu J, Wu Q, et al. Automatic delineation of the clinical target volume and organs at risk by deep learning for rectal cancer postoperative radiotherapy[J]. Radiother Oncol, 2020, 145:186-192. DOI: 10.1016/j.radonc.2020.01.020.
doi: 10.1016/j.radonc.2020.01.020 |
[29] |
Men K, Dai J, 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[J]. Med Phys, 2017, 44(12):6377-6389. DOI: 10.1002/mp.12602.
doi: 10.1002/mp.12602 |
[30] |
Men K, Chen X, Zhang Y, et al. Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images[J]. Front Oncol, 2017, 7:315. DOI: 10.3389/fonc.2017.00315.
doi: 10.3389/fonc.2017.00315 |
[31] |
Lin L, Dou Q, Jin YM, et al. Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma[J]. Radiology, 2019, 291(3):677-686. DOI: 10.1148/radiol.2019182012.
doi: 10.1148/radiol.2019182012 pmid: 30912722 |
[32] |
Guidi G, Maffei N, Vecchi C, et al. A support vector machine tool for adaptive tomotherapy treatments: prediction of head and neck patients criticalities[J]. Phys Med, 2015, 31(5):442-451. DOI: 10.1016/j.ejmp.2015.04.009.
doi: 10.1016/j.ejmp.2015.04.009 |
[33] |
Bridge P, Bridge R. Artificial intelligence in radiotherapy: a philosophical perspective[J]. J Med Imaging Radiat Sci, 2019, 50(4 Suppl 2):S27-S31. DOI: 10.1016/j.jmir.2019.09.003.
doi: 10.1016/j.jmir.2019.09.003 |
[1] | Qian Xiaotao, Shi Ziyi, Hu Ge, Wu Xiaowei. Efficacy of consolidation chemotherapy after radical radiotherapy and chemotherapy for stage Ⅲ-ⅣA esophageal squamous cell carcinoma: a real-world clinical study [J]. Journal of International Oncology, 2024, 51(6): 326-331. |
[2] | Yang Mi, Bie Jun, Zhang Jiayong, Deng Jiaxiu, Tang Zuge, Lu Jun. Analysis of the efficacy and prognosis of neoadjuvant therapy for locally advanced resectable esophageal cancer [J]. Journal of International Oncology, 2024, 51(6): 332-337. |
[3] | Gao Fan, Wang Ping, Du Chao, Chu Yanliu. Research progress on intestinal flora and non-surgical treatment of the colorectal cancer [J]. Journal of International Oncology, 2024, 51(6): 376-381. |
[4] | Gu Fangmeng, Xu Chenyang, Lei Dapeng. Research progress on artificial intelligence-assisted electronic laryngoscopy in the diagnosis and treatment of laryngeal cancer and laryngeal precancerous lesions [J]. Journal of International Oncology, 2024, 51(5): 303-307. |
[5] | Qian Xiaotao, Shi Ziyi, Hu Ge. A real-world clinical study of immunocheckpoint inhibitor maintenance therapy after radical radiotherapy and chemotherapy in stage Ⅲ-ⅣA esophageal squamous cell carcinoma [J]. Journal of International Oncology, 2024, 51(3): 151-156. |
[6] | Li Shuyue, Ma Chenying, Zhou Juying, Xu Xiaoting, Qin Songbing. Progress of radiotherapy in oligometastatic non-small cell lung cancer [J]. Journal of International Oncology, 2024, 51(3): 170-174. |
[7] | Gao Xinyu, Li Zhenjiang, Sun Hongfu, Han Dan, Zhao Qian, Liu Chengxin, Huang Wei. Clinical application of MR-guided radiotherapy based on MR-linac in esophageal cancer patients [J]. Journal of International Oncology, 2024, 51(1): 37-42. |
[8] | Cui Tenglu, Lyu lu, Sun Pengfei. Application of radiotherapy combined with immunotherapy in the treatment of head and neck squamous cell carcinoma [J]. Journal of International Oncology, 2023, 50(9): 548-552. |
[9] | Li Qingshan, Xie Xin, Zhang Nan, Liu Shuai. Research progress on the application of combining radiotherapy and systemic therapy in breast cancer [J]. Journal of International Oncology, 2023, 50(6): 362-367. |
[10] | Lyu Lu, Sun Pengfei. Gut flora and cervical cancer [J]. Journal of International Oncology, 2023, 50(6): 373-376. |
[11] | Radiation Oncology Treatment Physician Branch, Chinese Medical Doctor Association, Radiation Oncology Therapy Branch, Chinese Medical Association, Chinese Association of Radiation Therapy, China Anti-Cancer Association. Chinese experts' consensus on the application of pegylated recombinant human granulocyte colony-stimulating factor during concurrent chemoradiotherapy (2023 version) [J]. Journal of International Oncology, 2023, 50(4): 193-201. |
[12] | Xu Meng, Jiang Wei, Zhu Haitao, Cao Xiongfeng. Research progress of cancer-associated fibroblasts in tumor radiotherapy resistance [J]. Journal of International Oncology, 2023, 50(4): 227-230. |
[13] | Shi Xaioqi, Wang Hongyan. Research progress on the interaction between gut microbiota and radiation enteritis [J]. Journal of International Oncology, 2023, 50(4): 244-247. |
[14] | Zhao Yongrui, Gao Ying, Chen Yidong, Xu Jiankun. Efficacy and safety of linear accelerator-based fractionated stereotactic radiotherapy for small volume brain metastases [J]. Journal of International Oncology, 2023, 50(3): 138-143. |
[15] | Huang Huayu, Gong Hongyun, Song Qibin. Influencing factors of pneumonitis in the period of thoracic radiotherapy combined with immunotherapy [J]. Journal of International Oncology, 2023, 50(2): 102-106. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||