浏览全部资源
扫码关注微信
Received：09 June 2021，
Revised：2021-11-3，
Accepted：10 November 2021，
Published：16 November 2022
移动端阅览
研究表明在明确驱动基因后进行特异性靶向治疗，肺癌患者的中位生存期显著延长。而除高通量测序技术和荧光原位杂交等分子生物学技术外，影像基因组学的出现，也为肺腺癌分子分型预测提供了一种无创的新方法。本文对肺腺癌计算机断层扫描（computed tomography，CT）影像分子分型的研究进展进行综述。首先，介绍肺腺癌分子分型的研究背景及肺腺癌主要的基因突变类型；然后，重点介绍两种主要的研究方法，即CT语义特征与肺腺癌分子亚型的相关性分析和基于机器学习的肺腺癌分子分型预测模型；最后，总结了该领域现阶段面临的主要问题，并对未来的研究方向做出展望。肺腺癌CT影像分子分型研究已经取得了一定成果，但仍存在很多问题。相关性分析与基于影像组学的预测模型研究由于样本各异且受过多人为干预，导致研究结果差异大，甚至有部分文献得到的结论截然相反。而基于深度学习的预测模型研究采用端到端的神经网络模型，人为参与极少，降低了研究难度，但尚处于起步阶段，构建的模型大多相对简单，远不能达到临床应用标准。今后的研究应聚焦于结合多种医学图像构建肺腺癌分子分型的大样本深度学习预测模型，同时结合临床信息、语义特征及影像组学特征，实现肺腺癌分子分型的无创、精准预测。
The lung adenocarcinoma oriented median survival intervals can be significantly extended through specific targeted therapy based on the identified gene-driven following. Current biopsy is regarded as the "gold standard" for gene-driven tumor detection in clinical practice. Such invasive examination has a certain probability of misdiagnosis and missed diagnosis due to the tumor heterogeneity. Moreover
some of the molecular biology detection technologies are time-consuming and costly
such as the next generation of sequencing and fluorescence in situ hybridization. Therefore
radiogenomics has emerged and provided a new non-invasive method for the prediction of tumor molecular typing. As the most commonly-used way to monitor the lung cancer-related curative effect
computed tomography (CT) has its potential of short-term scanning
high resolution and relatively low-cost
which can carry out the tumor evaluation overall. It makes up the deficiency of biopsy to a certain extent. Thanks to the development of molecular targeted drugs in the context of lung adenocarcinoma treatment
most of researchers have been committed to using medical images to predict the molecular typing of lung adenocarcinoma. We carry out the critical review to harness CT images based molecular typing of lung adenocarcinoma. 1) Current situation of lung adenocarcinoma-oriented molecular typing and the key gene mutation types are introduced. 2) Existing CT images-related methods are divided into two categories: the correlation analysis of CT semantic features and the molecular subtype of lung adenocarcinoma
and the prediction model of molecular typing based on machine learning (ML). Among them
the ML-based prediction model is mainly introduced
which includes radiomics model and deep learning neural network model. 3) Some challenging problems are summarized in this field
and the future research direction is predicted. The correlation between semantic features and molecular typing of lung adenocarcinoma is derived of naked eyes visible tumor features. But
the predicted accuracy is still relatively low. Furthermore
the prediction model is demonstrated based on extracted features of radiomics from the segmented tumor images
and the selected radiomics features are input into the machine learning classifier to obtain the final prediction results. This method is still subject to human subjective influence to some extent
such as the stage of tumor segmentation and pre-setting features. To extract higher-level features for higher prediction accuracy
the convolutional neural network based (CNN-based) deep learning technology can beneficial for low-level features learning in tumor images. The deep learning model has less human-derived intervention
but it needs to be trained and verified through a large amount of data
and the expected effect cannot be achieved temporarily via a small sample. A challenging issue is to be tackled for the complex status of genetic mutations in lung adenocarcinoma and a complete and standardized database for generalization ability. With the database development of gradual standardization and expansion
future research direction can be focused on the construction of large-sample deep learning prediction model based on the integration of multiple medical images. To achieve noninvasive and accurate prediction of molecular typing of lung adenocarcinoma
the model optimization should combine clinical information
CT semantic features and radiomics features further.
Chen L F, Chen X Y, Lin J, Yu X B and Jin L. 2019. The characteristics of lung adenocarcinoma driver gene variants detected by high-throughput sequencing and quantitative fluorescence PCR. Chinese Journal of Pathology, 48(10): 772-778
陈灵锋, 陈小岩, 林洁, 俞训彬, 晋龙. 2019. 应用高通量测序技术与荧光PCR技术检测肺腺癌驱动基因变异分析. 中华病理学杂志, 48(10): 772-778 [DOI: 10.3760/cma.j.issn.0529-5807.2019.10.005]
Chen Y Q, Yang Y, Ma L B, Zhu H Y, Feng T N, Jiang S, Wei Y Y, Wang T T and Sun X W. 2019. Prediction of EGFR mutations by conventional CT-features in advanced pulmonary adenocarcinoma. European Journal of Radiology, 112: 44-51 [DOI: 10.1016/j.ejrad.2019.01.005]
Colen R, Foster I, Gatenby R, Giger M E, Gillies R, Gutman D, Heller M, Jain R, Madabhushi A, Madhavan S, Napel S, Rao A, Saltz J, Tatum J, Verhaak R and Whitman G.2014. NCI Workshop Report: clinical and computational requirements for correlating imaging phenotypes with genomics signatures. Translational Oncology, 7(5): 556-569 [DOI: 10.1016/j.tranon.2014.07.007]
Digumarthy S R, Mendoza D P, Lin J J, Chen T Q, Rooney M M, Chin E, Sequist L V, Lennerz J K, Gainor J F and Shaw A T. 2020a. Computed tomography imaging features and distribution of metastases in ROS1-rearranged non-small-cell lung cancer. Clinical Lung Cancer, 21(2): 153-159. e3[DOI: 10.1016/j.cllc.2019.10.006]
Digumarthy S R, Mendoza D P, Lin J J, Rooney M, Do A, Chin E, Yeap B Y, Shaw A T and Gainor J F. 2020b. Imaging features and patterns of metastasis in non-small cell lung cancer with RET rearrangements. Cancers, 12(3): #693 [DOI: 10.3390/cancers12030693]
Ettinger D S, Aisner D L, Wood D E, Akerley W, Bauman J, Chang J Y, Chirieac L R, D′Amico T A, Dilling T J, Dobelbower M, Govindan R, Gubens M A, Hennon M, Horn L, Lackner R P, Lanuti M, Leal T A, Lilenbaum R, Lin J, Loo B W Jr, Martins R, Otterson G A, Patel S P, Reckamp K, Riely G J, Schild S E, Shapiro T A, Stevenson J, Swanson S J, Tauer K, Yang S C, Gregory K and Hughes M. 2018. NCCN guidelines insights: non-small cell lung cancer, Version 5.2018. Journal of the National Comprehensive Cancer Network, 16(7): 807-821 [DOI: 10.6004/jnccn.2018.0062]
Gandhi L and Jänne P A. 2012. Crizotinib for ALK-rearranged non-small cell lung cancer: a new targeted therapy for a new target. Clinical Cancer Research, 18(14): 3737-3742 [DOI: 10.1158/1078-0432.CCR-11-2393]
Garinet S, Laurent-Puig P, Blons H and Oudart J B. 2018. Current and future molecular testing in NSCLC, what can we expect from new sequencing technologies? Journal of Clinical Medicine, 7(6): #144 [DOI: 10.3390/jcm7060144]
Halpenny D F, Plodkowski A, Riely G, Zheng J T, Litvak A, Moscowitz C and Ginsberg M S. 2017. Radiogenomic evaluation of lung cancer—Are there imaging characteristics associated with lung adenocarcinomas harboring BRAF mutations? Clinical Imaging, 42: 147-151 [DOI: 10.1016/j.clinimag.2016.11.015]
Han X Y, Fan J, Gu J, Li Y M, Yang M, Liu T, Li N, Zeng W J and Shi H S. 2020. CT features associated with EGFR mutations and ALK positivity in patients with multiple primary lung adenocarcinomas. Cancer Imaging, 20(1): #51 [DOI: 10.1186/s40644-020-00330-1]
Han Z P, Tang X, Yang T Y, Chang N, Zhang Y, Zhang X X, Xu Y Q and Zhang J. 2019. The application and research progress of radiomics in lung cancer. International Journal of Respiration, 39(9): 692-695
韩志萍, 唐兴, 杨庭义, 常宁, 张勇, 张信信, 徐永强, 张艰. 2019. 人工智能时代下的影像组学在肺癌中的应用研究进展. 国际呼吸杂志, 39(9): 692-695 [DOI: 10.3760/cma.j.issn.1673-436X.2019.09.010]
Hong D, Xu K, Zhang L N, Wan X T and Guo Y. 2020. Radiomics signature as a predictive factor for EGFR mutations in advanced lung adenocarcinoma. Frontiers in Oncology, 10: #28 [DOI: 10.3389/fonc.2020.00028]
Jia T Y, Xiong J F, Li X Y, Yu W, Xu Z Y, Cai X W, Ma J C, Ren Y C, Larsson R, Zhang J, Zhao J and Fu X L. 2019. Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling. European Radiology, 29(9): 4742-4750 [DOI: 10.1007/s00330-019-06024-y]
Kim T H, Woo S, Yoon S H, Halpenny D F, Han S and Suh C H. 2019. CT characteristics of non-small cell lung cancer with anaplastic lymphoma kinase rearrangement: a systematic review and meta-analysis. American Journal of Roentgenology, 213(5): 1059-1072 [DOI: 10.2214/AJR.19.21485]
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout R G P M, Granton P, Zegers C M L, Gillies R, Boellard R, Dekker A and Aerts H J W L. 2012. Radiomics: extracting more information from medical images using advanced feature analysis. European Journal of Cancer, 48(4): 441-446 [DOI: 10.1016/j.ejca.2011.11.036]
Li S, Ding C W, Zhang H, Song J D and Wu L. 2019. Radiomics for the prediction of EGFR mutation subtypes in non-small cell lung cancer. Medical Physics, 46(10): 4545-4552 [DOI: 10.1002/mp.13747]
Li X Y, Xiong J F, Jia T Y, Shen T L, Hou R P, Zhao J and Fu X L. 2018a. Detection of epithelial growth factor receptor (EGFR) mutations on CT images of patients with lung adenocarcinoma using radiomics and/or multi-level residual convolutionary neural networks. Journal of Thoracic Disease, 10(12): 6624-6635 [DOI: 10.21037/jtd.2018.11.03]
Li Y J, Lu L, Xiao M J, Dercle L, Huang Y, Zhang Z S, Schwartz L H, Li D Q and Zhao B S. 2018b. CT slice thickness and convolution kernel affect performance of a radiomic model for predicting EGFR status in non-small cell lung cancer: a preliminary study. Scientific Reports, 8(1): #17913 [DOI: 10.1038/s41598-018-36421-0]
Liu G X, Xu Z H, Ge Y Q, Jiang B B, Groen H, Vliegenthart R and Xie X Q. 2020a. 3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma. Translational Lung Cancer Research, 9(4): 1212-1224 [DOI: 10.21037/tlcr-20-122]
Liu Q F, Sun D Z, Li N, Kim J, Feng D G, Huang G, Wang L S and Song S L. 2020b. Predicting EGFR mutation subtypes in lung adenocarcinoma using 18 F-FDG PET/CT radiomic features. Translational Lung Cancer Research, 9(3): 549-562 [DOI: 10.21037/tlcr.2020.04.17]
Lu X Q, Li M Y, Zhang H M, Hua S C, Meng F Y, Yang H L, Li X Y and Cao D B. 2020. A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma. Physics in Medicine and Biology, 65(5): #0655012 [DOI: 10.1088/1361-6560/ab6f98]
Ma D N, Gao X Y, Dan Y B, Zhang A N, Wang W J, Yang G and Zhu H Z. 2020. Evaluating solid lung adenocarcinoma anaplastic lymphoma kinase gene rearrangement using noninvasive radiomics biomarkers. OncoTargets and Therapy, 13: 6927-6935 [DOI: 10.2147/OTT.S257798]
Mahajan A, Kania V, Patil V M, Noronha V, Joshi A, Menon N S, Kumar R, Rane S, Chougule A, Vaidya S and Prabhash K. 2020. Deep learning-based predictive imaging biomarker model for EGFR mutation status in non-small cell lung cancer from CT imaging. Journal of Clinical Oncology, 38(S15): #3106 [DOI: 10.1200/JCO.2020.38.15_suppl.3106]
Mei D D, Luo Y, Wang Y and Gong J S. 2018. CT texture analysis of lung adenocarcinoma: can radiomic features be surrogate biomarkers for EGFR mutation statuses. Cancer Imaging, 18(1): #52 [DOI: 10.1186/s40644-018-0184-2]
Mendoza D P, Lin J J, Rooney M M, Chen T Q, Sequist L V, Shaw A T and Digumarthy S R. 2020. Imaging features and metastatic patterns of advanced ALK -rearranged non-small cell lung cancer. American Journal of Roentgenology, 214(4): 766-774 [DOI: 10.2214/AJR.19.21982]
Nie Y Q, Liu H J, Tan X, Wang H, Li F Z, Li C Y, Han P, Lyu X, Xu X Y and Guo M. 2019. Correlation between high-resolution computed tomography lung nodule characteristics and EGFR mutation in lung adenocarcinomas. OncoTargets and Therapy, 12: 519-526 [DOI: 10.2147/OTT.S184217]
Padole A, Singh R, Zhang E W, Mendoza D P, Dagogo-Jack I, Kalra M K and Digumarthy S R. 2020. Radiomic features of primary tumor by lung cancer stage: analysis in BRAF mutated non-small cell lung cancer. Translational Lung Cancer Research, 9(4): 1441-1451 [DOI: 10.21037/tlcr-20-347]
Pinheiro G, Pereira T, Dias C, Freitas C, Hespanhol V, Costa J L, Cunha A and Oliveira H P. 2020. Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS. Scientific Reports, 10(1): #3625 [DOI: 10.1038/s41598-020-60202-3]
Qin R X, Wang Z Z, Qiao K, Hai J J, Jiang L Y, Chen J, Pei X L, Shi D P and Yan B. 2020. Multi-type interdependent feature analysis based on hybrid neural networks for computer-aided diagnosis of epidermal growth factor receptor mutations. IEEE Access, 8: 38517-38527 [DOI: 10.1109/ACCESS.2020.2971281]
Qiu X W, Yuan H and Sima B. 2019. Relationship between EGFR mutation and computed tomography characteristics of the lung in patients with lung adenocarcinoma. Thoracic Cancer, 10(2): 170-174 [DOI: 10.1111/1759-7714.12928]
Ren X T, Zhao J J, Qiang Y, Rauf S A and Liu J H. 2020. Lung cancer subtype recognition with unsupervised learning combining paired learning and image clustering. Computer Science, 47(10): 200-206
任雪婷, 赵涓涓, 强彦, Rauf S A, 刘继华. 2020. 联合成对学习和图像聚类的无监督肺癌亚型识别. 计算机科学, 47(10): 200-206 [DOI: 10.11896/jsjkx.190900073]
Rizzo S, Raimondi S, de Jong E E C, van Elmpt W, De Piano F, Petrella F, Bagnardi V, Jochems A, Bellomi M, Dingemans A M and Lambin P. 2019. Genomics of non-small cell lung cancer (NSCLC): association between CT-based imaging features and EGFR and K-RAS mutations in 122 patients—An external validation. European Journal of Radiology, 110: 148-155 [DOI: 10.1016/j.ejrad.2018.11.032]
Sawan P, Plodkowski A J, Li A E, Li B T, Drilon A, Capanu M and Ginsberg M S. 2018. CT features of HER2-mutant lung adenocarcinomas. Clinical Imaging, 51: 279-283 [DOI: 10.1016/j.clinimag.2018.05.028]
Shen T X, Liu L, Li W H, Fu P, Xu K, Jiang Y Q, Pan F, Guo Y and Zhang M C. 2019. CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma. Cancer Imaging, 19(1): #34 [DOI: 10.1186/s40644-019-0221-9]
Shi Z, Zheng X, Shi R F, Song C G, Yang R H, Zhang Q W, Wang X R, Lu J P, Yu Y W, Liu Q and Jiang T. 2017. Radiological and clinical features associated with epidermal growth factor receptor mutation status of exon 19 and 21 in lung adenocarcinoma. Scientific Reports, 7(1): #364 [DOI: 10.1038/s41598-017-00511-2]
Song L, Zhu Z C, Mao L, Li X L, Han W, Du H Y, Wu H W, Song W and Jin Z Y. 2020. Clinical, conventional CT and radiomic feature-based machine learning models for predicting ALK rearrangement status in lung adenocarcinoma patients. Frontiers in Oncology, 10: #369 [DOI: 10.3389/fonc.2020.00369]
Song L, Zhu Z C, Wu H W, Han W, Cheng X, Li J, Du H Y, Lei J, Sui X, Song W and Jin Z Y. 2021a. Individualized nomogram for predicting ALK rearrangement status in lung adenocarcinoma patients. European Radiology, 31(4): 2034-2047 [DOI: 10.1007/s00330-020-07331-5]
Song Z B, Liu T C, Shi L, Yu Z Y, Shen Q, Xu M D, Huang Z Z, Cai Z J, Wang W X, Xu C W, Sun J J and Chen M. 2021b. The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients. European Journal of Nuclear Medicine and Molecular Imaging, 48(2): 361-371 [DOI: 10.1007/s00259-020-04986-6]
Suh Y J, Lee H J, Kim Y J, Kim K G, Kim H, Jeon Y K and Kim Y T. 2018. Computed tomography characteristics of lung adenocarcinomas with epidermal growth factor receptor mutation: a propensity score matching study. Lung Cancer, 123: 52-59 [DOI: 10.1016/j.lungcan.2018.06.030]
Sun Y W, Xu J, Zhou J and Liu W J. 2018. Targeted drugs for systemic therapy of lung cancer with brain metastases. Oncotarget, 9(4): 5459-5472 [DOI: 10.18632/oncotarget.23616]
Tu W T, Sun G Y, Fan L, Wang Y, Xia Y, Guan Y, Li Q, Zhang D, Liu S Y and Li Z B. 2019. Radiomics signature: a potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology. Lung Cancer, 132: 28-35 [DOI: 10.1016/j.lungcan.2019.03.025]
Velazquez E R, Parmar C, Liu Y, Coroller T P, Cruz G, Stringfield O, Ye Z X, Makrigiorgos M, Fennessy F, Mak R H, Gillies R, Quackenbush J and Aerts H J W L. 2017. Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Research, 77(14): 3922-3930 [DOI: 10.1158/0008-5472.CAN-17-0122]
Wang S, Shi J Y, Ye Z X, Dong D, Yu D D, Zhou M, Liu Y, Gevaert O, Wang K, Zhu Y B, Zhou H Y, Liu Z Y and Tian J. 2019. Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. European Respiratory Journal, 53(3): #1800986 [DOI: 10.1183/13993003.00986-2018]
Weiss G J, Ganeshan B, Miles K A, Campbell D H, Cheung P Y, Frank S and Korn R L. 2014. Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic. PLoS One, 9(7): #100244 [DOI: 10.1371/journal.pone.0100244]
Wild C P, Weiderpass E and Stewart B W. 2020. World Cancer Report: Cancer Research for Cancer Prevention. Lyon: International Agency for Research on Cancer
Wu S S, Shen G Q, Mao J J and Gao B. 2020. CT radiomics in predicting EGFR mutation in non-small cell lung cancer: a single institutional study. Frontiers in Oncology, 10: #542957 [DOI: 10.3389/fonc.2020.542957]
Xiong J F, Li X Y, Lu L, Schwartz L H, Fu X L, Zhao J and Zhao B S. 2019. Implementation strategy of a CNN model affects the performance of CT assessment of EGFR mutation status in lung cancer patients. IEEE Access, 7: 64583-64591 [DOI: 10.1109/ACCESS.2019.2916557]
Yang B, Ji H S, Zhou C S, Dong H, Ma L, Ge Y Q, Zhu C H, Tian J H, Zhang L J, Zhu H and Lu G M. 2020. 18 F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma. Translational Lung Cancer Research, 9(3): 563-574 [DOI: 10.21037/tlcr-19-592]
Yang X G, Dong X, Wang J, Li W W, Gu Z R, Gao D S, Zhong N S and Guan Y B. 2019. Computed tomography-based radiomics signature: a potential indicator of epidermal growth factor receptor mutation in pulmonary adenocarcinoma appearing as a subsolid nodule. The Oncologist, 24(11): 1156-1164 [DOI: 10.1634/theoncologist.2018-0706]
Yu D D, Zhou M, Yang F, Dong D, Gevaert O, Liu Z Y, Shi J Y and Tian J. 2017. Convolutional neural networks for predicting molecular profiles of non-small cell lung cancer//Proceedings of the 14th IEEE International Symposium on Biomedical Imaging. Melbourne, Australia: IEEE: 569-572 [ DOI:10.1109/ISBI.2017.7950585 http://dx.doi.org/10.1109/ISBI.2017.7950585 ]
Zeng H M, Chen W Q, Zheng R S, Zhang S W, Ji J S, Zou X N, Xia C F, Sun K X, Yang Z X, Li H, Wang N, Han R Q, Liu S Z, Li H Z, Mu H J, He Y T, Xu Y J, Fu ZT, Zhou Y, Jiang J, Yang Y L, Chen J G, Wei K R, Fan D M, Wang J, Fu F X, Zhao D L, Song G H, Chen J S, Jiang C X, Zhou X, Gu X P, Jin F, Li Q L, Li Y H, Wu T H, Yan C H, Dong J M, Hua Z L, Baade P, Bray F, Jemal A, Yu X Q and He J. 2018. Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries. Lancet Glob Health, 6(5): 555-567 [DOI: 10.1016/s2214-109x(18)30127-x]
Zhang B H, Qi S L, Pan X H, Li C, Yao Y D, Qian W and Guan Y B. 2021. Deep CNN model using CT radiomics feature mapping recognizes EGFR gene mutation status of lung adenocarcinoma. Frontiers in Oncology, 10: #3389 [DOI: 10.3389/fonc.2020.598721]
Zhang H F, Cai W G, Wang Y F, Liao M Y and Tian S F. 2019. CT and clinical characteristics that predict risk of EGFR mutation in non-small cell lung cancer: a systematic review and meta-analysis. International Journal of Clinical Oncology, 24(6): 649-659 [DOI: 10.1007/s10147-019-01403-3]
Zhang J Y, Zhao X M, Zhao Y, Zhang J M, Zhang Z Q, Wang J F, Wang Y C, Dai M and Han J Y. 2020. Value of pre-therapy 18 F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer. European Journal of Nuclear Medicine and Molecular Imaging, 47(5): 1137-1146 [DOI: 10.1007/s00259-019-04592-1]
Zhang L W, Chen B J, Liu X, Song J D, Fang M J, Hu C E, Dong D, Li W M and Tian J. 2018. Quantitative biomarkers for prediction of epidermal growth factor receptor mutation in non-small cell lung cancer. Translational Oncology, 11(1): 94-101 [DOI: 10.1016/j.tranon.2017.10.012]
Zhao W, Yang J C, Ni B B, Bi D X, Sun Y L, Xu M D, Zhu X X, Li C, Jin L, Gao P, Wang P J, Hua Y Q and Li M. 2019. Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning. Cancer Medicine, 8(6): 3532-3543 [DOI: 10.1002/cam4.2233]
Zou J W, Lv T F, Zhu S H, Lu Z F, Shen Q, Xia L L, Wu J, Song Y and Liu H B. 2017. Computed tomography and clinical features associated with epidermal growth factor receptor mutation status in stage I/II lung adenocarcinoma. Thoracic Cancer, 8(3): 260-270 [DOI: 10.1111/1759-7714.12436]
相关文章
相关作者
相关机构
京公网安备11010802024621
