国际肿瘤学杂志 ›› 2022, Vol. 49 ›› Issue (1): 51-55.doi: 10.3760/cma.j.cn371439-20210408-00007
收稿日期:
2021-04-08
修回日期:
2021-09-17
出版日期:
2022-01-08
发布日期:
2022-01-17
通讯作者:
黄伟
E-mail:alvinbird@163.com
基金资助:
Zhang Hongjiao1, Jiang Jie2, Huang Wei1()
Received:
2021-04-08
Revised:
2021-09-17
Online:
2022-01-08
Published:
2022-01-17
Contact:
Huang Wei
E-mail:alvinbird@163.com
Supported by:
摘要:
放疗是肺癌特别是局部晚期肺癌患者的主要治疗方式之一。局部晚期肺癌常伴有肺不张,给放疗靶区的勾画带来较大的困难和不确定性,进而影响患者疗效。最新研究表明功能影像具备独特的成像原理,可真实反映肿瘤的增殖、代谢等生物学信息,在区分肺癌与肺不张靶区勾画方面具有应用前景。
张红娇, 姜杰, 黄伟. 功能影像辅助伴肺不张肺癌放疗靶区勾画的研究进展[J]. 国际肿瘤学杂志, 2022, 49(1): 51-55.
Zhang Hongjiao, Jiang Jie, Huang Wei. Research progress of functional imaging-assisted radiotherapy target delineation of lung cancer with atelectasis[J]. Journal of International Oncology, 2022, 49(1): 51-55.
[1] |
Karki K, Saraiya S, Hugo GD, et al. Variabilities of magnetic resonance imaging-, computed tomography-, and positron emission tomography-computed tomography-based tumor and lymph node delineations for lung cancer radiation therapy planning[J]. Int J Radiat Oncol Biol Phys, 2017, 99(1):80-89. DOI: 10.1016/j.ijrobp.2017.05.002.
doi: 10.1016/j.ijrobp.2017.05.002 |
[2] |
Prathipati A, Manthri RG, Subramanian BV, et al. A prospective study comparing functional imaging (18F-FDG PET) versus anatomical imaging (contrast enhanced CT) in dosimetric planning for non-small cell lung cancer[J]. Asia Ocean J Nucl Med Biol, 2017, 5(2):75-84. DOI: 10.22038/aojnmb.2017.8706.
doi: 10.22038/aojnmb.2017.8706 pmid: 28660217 |
[3] |
Sibille L, Seifert R, Avramovic N, et al. 18F-FDG PET/CT uptake classification in lymphoma and lung cancer by using deep convolutional neural networks[J]. Radiology, 2020, 294(2):445-452. DOI: 10.1148/radiol.2019191114.
doi: 10.1148/radiol.2019191114 |
[4] |
Konert T, Vogel WV, Paez D, et al. Introducing FDG PET/CT-guided chemoradiotherapy for stage Ⅲ NSCLC in low- and middle-income countries: preliminary results from the IAEA PERTAIN trial[J]. Eur J Nucl Med Mol Imaging, 2019, 46(11):2235-2243. DOI: 10.1007/s00259-019-04421-5.
doi: 10.1007/s00259-019-04421-5 |
[5] |
Schaefer A, Kim YJ, Kremp S, et al. PET-based delineation of tumour volumes in lung cancer: comparison with pathological findings[J]. Eur J Nucl Med Mol Imaging, 2013, 40(8):1233-44. DOI: 10.1007/s00259-013-2407-x.
doi: 10.1007/s00259-013-2407-x |
[6] |
Kajáry K, Lengyel Z, Tokés AM, et al. Dynamic FDG-PET/CT in the initial staging of primary breast cancer: clinicopathological correlations[J]. Pathol Oncol Res, 2020, 26(2):997-1006. DOI: 10.1007/s12253-019-00641-0.
doi: 10.1007/s12253-019-00641-0 |
[7] |
Bianconi F, Palumbo I, Fravolini ML, et al. Texture analysis on [18F]FDG PET/CT in non-small-cell lung cancer: correlations between PET features, CT features, and histological types[J]. Mol Imaging Biol, 2019, 21(6):1200-1209. DOI: 10.1007/s11307-019-01336-3.
doi: 10.1007/s11307-019-01336-3 |
[8] |
Yin LJ, Yu XB, Ren YG, et al. Utilization of PET-CT in target volume delineation for three-dimensional conformal radiotherapy in patients with non-small cell lung cancer and atelectasis[J]. Multidiscip Respir Med, 2013, 8(1):21. DOI: 10.1186/2049-6958-8-21.
doi: 10.1186/2049-6958-8-21 |
[9] |
Fonti R, Conson M, Del Vecchio S. PET/CT in radiation oncology[J]. Semin Oncol, 2019, 46(3):202-209. DOI: 10.1053/j.seminoncol.2019.07.001.
doi: S0093-7754(19)30079-X pmid: 31378377 |
[10] |
Flechsig P, Rastgoo R, Kratochwil C, et al. Impact of computer-aided CT and PET analysis on non-invasive T staging in patients with lung cancer and atelectasis[J]. Mol Imaging Biol, 2018, 20(6):1044-1052. DOI: 10.1007/s11307-018-1196-9.
doi: 10.1007/s11307-018-1196-9 pmid: 29679299 |
[11] |
Fiset S, Welch ML, Weiss J, et al. Repeatability and reproducibility of MRI-based radiomic features in cervical cancer[J]. Radiother Oncol, 2019, 135:107-114. DOI: 10.1016/j.radonc.2019.03.001.
doi: 10.1016/j.radonc.2019.03.001 |
[12] |
Pavic M, Bogowicz M, Würms X, et al. Influence of inter-observer delineation variability on radiomics stability in different tumor sites[J]. Acta Oncol, 2018, 57(8):1070-1074. DOI: 10.1080/0284186X.2018.1445283.
doi: 10.1080/0284186X.2018.1445283 |
[13] |
刘陈路, 马长升, 陈进琥, 等. PET-CT SUV阈值对非小细胞肺癌靶区勾画体积及对影像组学指标的影响[J]. 中华肿瘤防治杂志, 2020, 27(22):1815-1820. DOI: 10.16073/j.cnki.cjcpt.2020.22.07.
doi: 10.16073/j.cnki.cjcpt.2020.22.07 |
[14] |
Yousaf T, Dervenoulas G, Politis M. Advances in MRI methodology[J]. Int Rev Neurobiol, 2018, 141:31-76. DOI: 10.1016/bs.irn.2018.08.008.
doi: 10.1016/bs.irn.2018.08.008 |
[15] |
Khalil A, Majlath M, Gounant V, et al. Contribution of magnetic resonance imaging in lung cancer imaging[J]. Diagn Interv Imaging, 2016, 97(10):991-1002. DOI: 10.1016/j.diii.2016.08.015.
doi: 10.1016/j.diii.2016.08.015 |
[16] |
Zhang X, Fu Z, Gong G, et al. Implementation of diffusion-weighted magnetic resonance imaging in target delineation of central lung cancer accompanied with atelectasis in precision radiotherapy[J]. Oncol Lett, 2017, 14(3):2677-2682. DOI: 10.3892/ol.2017.6479.
doi: 10.3892/ol.2017.6479 |
[17] |
谢青, 任彤, 孙梅, 等. DWI-MRI对区分中央型肺癌与阻塞性肺不张的价值[J]. 中国临床研究, 2020, 33(8):1097-1100. DOI: 10.13429/j.cnki.cjcr.2020.08.023.
doi: 10.13429/j.cnki.cjcr.2020.08.023 |
[18] |
Eun NL, Kang D, Son EJ, et al. Texture analysis with 3.0-T MRI for association of response to neoadjuvant chemotherapy in breast cancer[J]. Radiology, 2020, 294(1):31-41. DOI: 10.1148/radiol.2019182718.
doi: 10.1148/radiol.2019182718 |
[19] |
Sarioglu FC, Sarioglu O, Guleryuz H, et al. MRI-based texture analysis for differentiating pediatric craniofacial rhabdomyosarcoma from infantile hemangioma[J]. Eur Radiol, 2020, 30(10):5227-5236. DOI: 10.1007/s00330-020-06908-4.
doi: 10.1007/s00330-020-06908-4 pmid: 32382846 |
[20] |
Sarioglu O, Sarioglu FC, Akdogan AI, et al. MRI-based texture analysis to differentiate the most common parotid tumours[J]. Clin Radiol, 2020, 75(11): 877.e15-877.e23. DOI: 10.1016/j.crad.2020.06.018.
doi: 10.1016/j.crad.2020.06.018 |
[21] |
Mao J, Fang J, Duan X, et al. Predictive value of pretreatment MRI texture analysis in patients with primary nasopharyngeal carcinoma[J]. Eur Radiol, 2019, 29(8):4105-4113. DOI: 10.1007/s00330-018-5961-6.
doi: 10.1007/s00330-018-5961-6 |
[22] |
Ortiz-Ramón R, Ruiz-Espana S, Mollá-Olmos E, et al. Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach[J]. Phys Med, 2020, 76:44-54. DOI: 10.1016/j.ejmp.2020.06.016.
doi: S1120-1797(20)30149-6 pmid: 32593138 |
[23] |
Xu J, Cui X, Wang B, et al. Texture analysis of early cerebral tissue damage in magnetic resonance imaging of patients with lung cancer[J]. Oncol Lett, 2020, 19(4):3089-3100. DOI: 10.3892/ol.2020.11426.
doi: 10.3892/ol.2020.11426 |
[24] |
Mahon RN, Hugo GD, Weiss E. Repeatability of texture features derived from magnetic resonance and computed tomography imaging and use in predictive models for non-small cell lung cancer outcome[J]. Phys Med Biol, 2019, 64(14):145007. DOI: 10.1088/1361-6560/ab18d3.
doi: 10.1088/1361-6560/ab18d3 |
[25] |
Wang C, Tyagi N, Rimner A, et al. Segmenting lung tumors on longitudinal imaging studies via a patient-specific adaptive convolutional neural network[J]. Radiother Oncol, 2019, 131:101-107. DOI: 10.1016/j.radonc.2018.10.037.
doi: 10.1016/j.radonc.2018.10.037 |
[26] |
Hearn N, Bugg W, Chan A, et al. Manual and semi-automated delineation of locally advanced rectal cancer subvolumes with diffusion-weighted MRI[J]. Br J Radiol, 2020, 93(1114):20200543. DOI: 10.1259/bjr.20200543.
doi: 10.1259/bjr.20200543 |
[27] |
邹茂扬, 杨昊, 潘光晖, 等. 深度学习在医学图像配准上的研究进展与挑战[J]. 生物医学工程学杂志, 2019, 36(4):677-683. DOI: 10.7507/1001-5515.201810004.
doi: 10.7507/1001-5515.201810004 |
[28] |
Hu R, Wang H, Ristaniemi T, et al. Lung CT image registration through landmark-constrained learning with convolutional neural network[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020:1368-1371. DOI: 10.1109/EMBC44109.2020.9176363.
doi: 10.1109/EMBC44109.2020.9176363 |
[29] |
Han Q, Liang H, Cheng P, et al. Comparison of different registration landmarks for MRI-CT fusion in radiotherapy for lung cancer with post-obstructive lobar collapse[J]. J Appl Clin Med Phys, 2019, 20(1):50-54. DOI: 10.1002/acm2.12495.
doi: 10.1002/acm2.12495 |
[30] |
Higgins J, Bezjak A, Franks K, et al. Comparison of spine, carina, and tumor as registration landmarks for volumetric image-guided lung radiotherapy[J]. Int J Radiat Oncol Biol Phys, 2009, 73(5):1404-1413. DOI: 10.1016/j.ijrobp.2008.06.1926.
doi: 10.1016/j.ijrobp.2008.06.1926 |
[31] |
de Vos BD, Berendsen FF, Viergever MA, et al. A deep learning framework for unsupervised affine and deformable image registration[J]. Med Image Anal, 2019, 52:128-143. DOI: 10.1016/j.media.2018.11.010.
doi: 10.1016/j.media.2018.11.010 |
[32] |
Aldosary G, Szanto J, Holmes O, et al. Geometric inaccuracy and co-registration errors for CT, DynaCT and MRI images used in robotic stereotactic radiosurgery treatment planning[J]. Phys Med, 2020, 69:212-222. DOI: 10.1016/j.ejmp.2019.12.002.
doi: S1120-1797(19)30523-X pmid: 31918373 |
[33] |
刘士远, 肖湘生, 李成州, 等. MRI区分肺癌肿块与阻塞性肺炎或肺不张: 与CT对照[J]. 中国医学计算机成像杂志, 1997, (3):172-174. DOI: 10.19627/j.cnki.cn31-1700/th.1997.03.008.
doi: 10.19627/j.cnki.cn31-1700/th.1997.03.008 |
[34] |
Li L, Lu W, Tan S. Variational PET/CT tumor co-segmentation integrated with PET restoration[J]. IEEE Trans Radiat Plasma Med Sci, 2020, 4(1):37-49. DOI: 10.1109/trpms.2019.2911597.
doi: 10.1109/trpms.2019.2911597 |
[35] |
Sindoni A, Minutoli F, Pontoriero A, et al. Usefulness of four dimensional (4D) PET/CT imaging in the evaluation of thoracic lesions and in radiotherapy planning: review of the literature[J]. Lung Cancer, 2016, 96:78-86. DOI: 10.1016/j.lungcan.2016.03.019.
doi: 10.1016/j.lungcan.2016.03.019 |
[36] |
Reinartz G, Haverkamp U, Wullenkord R, et al. 4D-Listmode-PET-CT and 4D-CT for optimizing PTV margins in gastric lymphoma: determination of intra- and interfractional gastric motion[J]. Strahlenther Onkol, 2016, 192(5):322-332. DOI: 10.1007/s00066-016-0949-0.
doi: 10.1007/s00066-016-0949-0 |
[37] |
Scarsbrook A, Ward G, Murray P, et al. Respiratory-gated (4D) contrast-enhanced FDG PET-CT for radiotherapy planning of lower oesophageal carcinoma: feasibility and impact on planning target volume[J]. BMC Cancer, 2017, 17(1):671. DOI: 10.1186/s12885-017-3659-9.
doi: 10.1186/s12885-017-3659-9 pmid: 28978306 |
[38] |
Hapdey S, Dubray B, Chastan M, et al. Respiratory gated multistatic PET reconstructions to delineate radiotherapy target volume in patients with mobile lung tumours[J]. Q J Nucl Med Mol Imaging, 2020. DOI: 10.23736/S1824-4785.19.03183-2.
doi: 10.23736/S1824-4785.19.03183-2 |
[39] |
Navest RJM, Mandija S, Bruijnen T, et al. The noise navigator: a surrogate for respiratory-correlated 4D-MRI for motion characterization in radiotherapy[J]. Phys Med Biol, 2020, 65(1): 01NT02. DOI: 10.1088/1361-6560/ab5c62.
doi: 10.1088/1361-6560/ab5c62 |
[40] |
Wang FL, Tan YY, Gu XM, et al. Comparison of positron emission tomography using 2-[18F]-fluoro-2-deoxy-D-glucose and 3-deoxy-3-[18F]-fluorothymidine in lung cancer imaging[J]. Chin Med J (Engl), 2016, 129(24):2926-2935. DOI: 10.4103/0366-6999.195468.
doi: 10.4103/0366-6999.195468 |
[41] |
Shimizu K, Kaira K, Higuchi T, et al. Relationship between tumor immune markers and fluorine-18-α-methyltyrosine ([18F]FAMT) uptake in patients with lung cancer[J]. Mol Imaging Biol, 2020, 22(4):1078-1086. DOI: 10.1007/s11307-019-01456-w.
doi: 10.1007/s11307-019-01456-w |
[42] |
Cheng G. Non-small-cell lung cancer PET imaging beyond F18 fluorodeoxyglucose[J]. PET Clin, 2018, 13(1):73-81. DOI: 10.1016/j.cpet.2017.09.006.
doi: S1556-8598(17)30103-7 pmid: 29157387 |
[43] |
Windisch P, Zwahlen DR, Koerber SA, et al. Clinical results of fibroblast activation protein (FAP) specific PET and implications for radiotherapy planning: systematic review[J]. Cancers (Basel), 2020, 12(9):2629. DOI: 10.3390/cancers12092629.
doi: 10.3390/cancers12092629 |
[44] |
Prosch H. Pulmonary carcinoid tumors[J]. Radiologe, 2017, 57(5):397-406. DOI: 10.1007/s00117-017-0243-x.
doi: 10.1007/s00117-017-0243-x pmid: 28405692 |
[1] | 刘娜, 寇介丽, 杨枫, 刘桃桃, 李丹萍, 韩君蕊, 杨立洲. 血清miR-106b-5p、miR-760联合低剂量螺旋CT诊断早期肺癌的临床价值[J]. 国际肿瘤学杂志, 2024, 51(6): 321-325. |
[2] | 高凡, 王萍, 杜超, 褚衍六. 肠道菌群与结直肠癌非手术治疗的相关研究进展[J]. 国际肿瘤学杂志, 2024, 51(6): 376-381. |
[3] | 王丽, 刘志华, 杨伟洪, 蒋凤莲, 李全泳, 宋浩杰, 鞠文东. ROS1突变肺腺鳞癌合并脑梗死为主要表现的Trousseau综合征1例[J]. 国际肿瘤学杂志, 2024, 51(6): 382-384. |
[4] | 李书月, 马辰莺, 周菊英, 徐晓婷, 秦颂兵. 寡转移非小细胞肺癌的放疗进展[J]. 国际肿瘤学杂志, 2024, 51(3): 170-174. |
[5] | 李济时, 陆钊群, 刘俊茹, 吕建勋, 陈霜, 沈琳, 徐志渊, 吴平安. 新辅助放疗联合部分喉切除术治疗喉滑膜肉瘤1例并文献复习[J]. 国际肿瘤学杂志, 2024, 51(2): 123-125. |
[6] | 中国医师协会放射肿瘤治疗医师分会, 中华医学会放射肿瘤治疗学分会, 中国抗癌协会肿瘤放射治疗专业委员会. 中国食管癌放射治疗指南(2023年版)[J]. 国际肿瘤学杂志, 2024, 51(1): 1-20. |
[7] | 贺嘉慧, 胡钦勇. 基于GBD数据的中国和美国肺癌发病和死亡趋势及危险因素对比分析[J]. 国际肿瘤学杂志, 2024, 51(1): 29-36. |
[8] | 高新雨, 李振江, 孙洪福, 韩丹, 赵倩, 刘成新, 黄伟. 基于MR加速器的MR引导放疗在食管癌患者中的临床应用[J]. 国际肿瘤学杂志, 2024, 51(1): 37-42. |
[9] | 崔腾璐, 吕璐, 孙鹏飞. 放疗联合免疫治疗在头颈部鳞状细胞癌治疗中的应用[J]. 国际肿瘤学杂志, 2023, 50(9): 548-552. |
[10] | 李青珊, 谢鑫, 张楠, 刘帅. 放疗联合系统治疗在乳腺癌中的应用进展[J]. 国际肿瘤学杂志, 2023, 50(6): 362-367. |
[11] | 许萌, 姜伟, 朱海涛, 曹雄锋. 癌相关成纤维细胞在肿瘤放疗抵抗中的研究进展[J]. 国际肿瘤学杂志, 2023, 50(4): 227-230. |
[12] | 石小琪, 汪红艳. 肠道菌群与放射性肠炎的相互作用及研究进展[J]. 国际肿瘤学杂志, 2023, 50(4): 244-247. |
[13] | 李雄安, 颜艳艳. 丙戊酸镁用于治疗继发癫痫的晚期肺癌脑转移患者1例报道[J]. 国际肿瘤学杂志, 2023, 50(3): 191-192. |
[14] | 左小平, 刘晓川, 吴西强, 李周, 夏天, 刘国凤. 老年早期肺癌患者经胸腔镜肺切除术后心律失常发生的危险因素及预测模型构建[J]. 国际肿瘤学杂志, 2023, 50(12): 711-716. |
[15] | 李进芝, 赵彪, 文晓博, 张明, 袁美芳, 孙梦真, 蒲琴, 杨毅. Monaco系统计算网格尺寸对T4期鼻咽癌的剂量学影响[J]. 国际肿瘤学杂志, 2023, 50(11): 641-649. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||