Journal of International Oncology ›› 2022, Vol. 49 ›› Issue (1): 51-55.doi: 10.3760/cma.j.cn371439-20210408-00007
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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:
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 |
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