Watanabe S, Asamura H. Lymph node dissection for lung cancer significance, strategy, and technique. J Thorac Oncol. 2009;4(5):652–7. https://doi.org/10.1097/JTO.0b013e31819cce50.
Duma N, Santana-Davila R, Molina JR. Non-small cell lung cancer: epidemiology, screening, diagnosis, and treatment. Mayo Clin Proc. 2019;94(8):1623–40. https://doi.org/10.1016/j.mayocp.2019.01.013.
Article CAS PubMed Google Scholar
Darling GE, Allen MS, Decker PA, Ballman K, Malthaner RA, Inculet RI, Jones DR, McKenna RJ, Landreneau RJ, Rusch VW, et al. Randomized trial of mediastinal lymph node sampling versus complete lymphadenectomy during pulmonary resection in the patient with N0 or N1 (less than hilar) non-small cell carcinoma: results of the American College of Surgery Oncology Group Z0030 Trial. J Thorac Cardiovasc Surg. 2011;141(3):662–70. https://doi.org/10.1016/j.jtcvs.2010.11.008.
Article PubMed PubMed Central Google Scholar
Ishiguro F, Matsuo K, Fukui T, Mori S, Hatooka S, Mitsudomi T. Effect of selective lymph node dissection based on patterns of lobe-specific lymph node metastases on patient outcome in patients with resectable non-small cell lung cancer: a large-scale retrospective cohort study applying a propensity score. J Thorac Cardiovasc Surg. 2010;139(4):1001–6. https://doi.org/10.1016/j.jtcvs.2009.07.024.
Ray MA, Smeltzer MP, Faris NR, Osarogiagbon RU. Survival after mediastinal node dissection, systematic sampling, or neither for early stage NSCLC. J Thorac Oncol. 2020;15(10):1670–81. https://doi.org/10.1016/j.jtho.2020.06.009.
Article PubMed PubMed Central Google Scholar
Zhang Y, Deng C, Zheng Q, Qian B, Ma J, Zhang C, Jin Y, Shen X, Zang Y, Guo Y, et al. Selective mediastinal lymph node dissection strategy for clinical T1N0 invasive lung cancer: a prospective, multicenter, clinical trial. J Thorac Oncol. 2023. https://doi.org/10.1016/j.jtho.2023.02.010.
Tournoy KG, De Ryck F, Vanwalleghem L, Praet M, Vermassen F, Van Maele G, van Meerbeeck JP. The yield of endoscopic ultrasound in lung cancer staging: does lymph node size matter? J Thorac Oncol. 2008;3(3):245–9. https://doi.org/10.1097/JTO.0b013e3181653cbb.
de Margerie-Mellon C, de Bazelaire C, de Kerviler E. Image-guided biopsy in primary lung cancer: why, when and how. Diagn Interv Imaging. 2016;97(10):965–72. https://doi.org/10.1016/j.diii.2016.06.016.
Osarogiagbon RU, Van Schil P, Giroux DJ, Lim E, Putora PM, Lievens Y, Cardillo G, Kim HK, Rocco G, Bille A, et al. The International Association for the Study of Lung Cancer Lung Cancer Staging Project: overview of challenges and opportunities in revising the nodal classification of lung cancer. J Thorac Oncol. 2023;18(4):410–8. https://doi.org/10.1016/j.jtho.2022.12.009.
De Leyn P, Dooms C, Kuzdzal J, Lardinois D, Passlick B, Rami-Porta R, Turna A, Van Schil P, Venuta F, Waller D, et al. Revised ESTS guidelines for preoperative mediastinal lymph node staging for non-small-cell lung cancer. Eur J Cardiothorac Surg. 2014;45(5):787–98. https://doi.org/10.1093/ejcts/ezu028.
Gould MK, Kuschner WG, Rydzak CE, Maclean CC, Demas AN, Shigemitsu H, Chan JK, Owens DK. Test performance of positron emission tomography and computed tomography for mediastinal staging in patients with non-small-cell lung cancer—a meta-analysis. Ann Intern Med. 2003;139(11):879–92. https://doi.org/10.7326/0003-4819-139-11-200311180-00013.
Al-Sarraf N, Gately K, Lucey J, Wilson L, McGovern E, Young V. Lymph node staging by means of positron emission tomography is less accurate in non-small cell lung cancer patients with enlarged lymph nodes: analysis of 1145 lymph nodes. Lung Cancer. 2008;60(1):62–8. https://doi.org/10.1016/j.lungcan.2007.08.036.
Zheng K, Wang XR, Jiang CZ, Tang YX, Fang ZH, Hou JL, Zhu ZH, Hu S. Pre-operative prediction of mediastinal node metastasis using radiomics model based on F-18-FDG PET/CT of the primary tumor in non-small cell lung cancer patients. Front Med. 2021. https://doi.org/10.3389/fmed.2021.673876.
Dai M, Wang N, Zhao XM, Zhang JY, Zhang ZQ, Zhang JM, Wang JF, Hu YJ, Liu YN, Zhao XJ, et al. Value of presurgical F-18-FDG PET/CT radiomics for predicting mediastinal lymph node metastasis in patients with lung adenocarcinoma. Cancer Biotherapy Radiopharm. 2022. https://doi.org/10.1089/cbr.2022.0038.
Laros SSA, Dickerscheid DBM, Blazis SP, van der Heide JA. Machine learning classification of mediastinal lymph node metastasis in NSCLC: a multicentre study in a Western European patient population. Ejnmmi Physics. 2022. https://doi.org/10.1186/s40658-022-00494-8.
Article PubMed PubMed Central Google Scholar
Ouyang ML, Wang YR, Deng QS, Zhu YF, Zhao ZH, Wang L, Wang LX, Tang K. Development and validation of a F-18-FDG PET-based radiomic model for evaluating hypermetabolic mediastinal-hilar lymph nodes in non-small-cell lung cancer. Front Oncol. 2021. https://doi.org/10.3389/fonc.2021.710909.
Article PubMed PubMed Central Google Scholar
Ren C, Zhang J, Qi M, Zhang J, Zhang Y, Song S, Sun Y, Cheng J. Machine learning based on clinico-biological features integrated (18)F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung. Eur J Nucl Med Mol Imaging. 2021;48(5):1538–49. https://doi.org/10.1007/s00259-020-05065-6.
Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen SA, El Naqa I. Machine and deep learning methods for radiomics. Med Phys. 2020;47(5):e185–202. https://doi.org/10.1002/mp.13678.
Schmidt-Hansen M, Baldwin DR, Hasler E, Zamora J, Abraira V, Roqué IFM. PET-CT for assessing mediastinal lymph node involvement in patients with suspected resectable non-small cell lung cancer. Cochrane Database Syst Rev. 2014;2014(11): Cd009519. https://doi.org/10.1002/14651858.CD009519.pub2.
Article PubMed PubMed Central Google Scholar
Rohren EM, Turkington TG, Coleman RE. Clinical applications of PET in oncology. Radiology. 2004;231(2):305–32. https://doi.org/10.1148/radiol.2312021185.
Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, Chirieac LR, Dacic S, Duhig E, Flieder DB, et al. The 2015 World Health Organization Classification of lung tumors impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol. 2015;10(9):1243–60. https://doi.org/10.1097/JTO.0000000000000630.
Detterbeck FC, Nishimura KK, Cilento VJ, Giuliani M, Marino M, Osarogiagbon RU, Rami-Porta R, Rusch VW, Asamura H, Boards A. The International Association for the Study of Lung Cancer Staging Project: methods and guiding principles for the development of the ninth edition TNM classification. J Thorac Oncol. 2022;17(6):806–15. https://doi.org/10.1016/j.jtho.2022.02.008.
Boellaard R, Delgado-Bolton R, Oyen WJG, Giammarile F, Tatsch K, Eschner W, Verzijlbergen FJ, Barrington SF, Pike LC, Weber WA, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42(2):328–54. https://doi.org/10.1007/s00259-014-2961-x.
Article CAS PubMed Google Scholar
Werner-Wasik M, Nelson AD, Choi W, Arai Y, Faulhaber PF, Kang P, Almeida FD, Xiao Y, Ohri N, Brockway KD, et al. What is the best way to contour lung tumors on PET scans? Multiobserver validation of a gradient-based method using a NSCLC digital PET phantom. Int J Radiat Oncol Biol Phys. 2012;82(3):1164–71. https://doi.org/10.1016/j.ijrobp.2010.12.055.
Sridhar P, Mercier G, Tan J, Truong MT, Daly B, Subramaniam RM. FDG PET metabolic tumor volume segmentation and pathologic volume of primary human solid tumors. AJR Am J Roentgenol. 2014;202(5):1114–9. https://doi.org/10.2214/ajr.13.11456.
Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30(9):1323–41. https://doi.org/10.1016/j.mri.2012.05.001.
Article PubMed PubMed Central Google Scholar
Zwanenburg A, Vallières M, Abdalah MA, Aerts H, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328–38. https://doi.org/10.1148/radiol.2020191145.
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue R, Even AJG, Jochems A, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62. https://doi.org/10.1038/nrclinonc.2017.141.
Abdurixiti M, Nijiati M, Shen RF, Ya Q, Abuduxiku N, Nijiati M. Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review. Br J Radiol. 2021. https://doi.org/10.1259/bjr.20201272.
Article PubMed PubMed Central Google Scholar
Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15(2):81–94. https://doi.org/10.1038/nrclinonc.2017.166.
Article CAS PubMed Google Scholar
Lv WB, Yuan QY, Wang QS, Ma JH, Feng QJ, Chen WF, Rahmim A, Lu LJ. Radiomics analysis of PET and CT components of PET/CT imaging integrated with clinical parameters: application to prognosis for nasopharyngeal carcinoma. Mol Imaging Biol. 2019;21(5):954–64. https://doi.org/10.1007/s11307-018-01304-3.
Article CAS PubMed Google Scholar
Zwanenburg A. Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging. 2019;46(13):2638–55. https://doi.org/10.1007/s00259-019-04391-8.
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