[1] SIEGEL R L, MILLER K D, FUCHS H E, et al. Cancer statistics, 2021[J]. CA Cancer J Clin, 2021, 71(1): 7-33. DOI: 10.3322/caac.21654. [2] 袁蕙芸,蒋宇飞,谭玉婷,等.全球癌症发病与死亡流行现状和变化趋势[J].肿瘤防治研究, 2021, 48(6): 642-646. DOI: 10.3971/j.issn.1000-8578.2021.20.1533. [3] POPAT S, WELSH L. Brain metastases in solid tumours: new guidelines for a new era[J]. Ann Oncol, 2021, 32(11): 1322-1324. DOI: 10.1016/j.annonc.2021.08.1992. [4] 中国医师协会肿瘤医师分会,中国医疗保健国际交流促进会肿瘤内科分会. Ⅳ期原发性肺癌中国治疗指南(2021年版)[J].中华肿瘤杂志, 2021, 43(1): 39-59. DOI: 10.3760/cma.j.cn112152-20201009-00884. [5] FECCI P E, CHAMPION C D, HOJ J, et al. The evolving modern management of brain metastasis[J]. Clin Cancer Res, 2019, 25(22): 6570-6580. DOI: 10.1158/1078-0432.CCR-18-1624. [6] BEKAERT L, EMERY E, LEVALLET G, et al. Histopathologic diagnosis of brain metastases: current trends in management and future considerations[J]. Brain Tumor Pathol, 2017, 34(1): 8-19. DOI: 10.1007/s10014-016-0275-3. [7] GILLIES R J, KINAHAN P E, HRICAK H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169. [8] LAMBIN P, LEIJENAAR R T H, DEIST T M, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141. [9] QIAN Z H, LI Y M, WANG Y Z, et al. Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers[J]. Cancer Lett, 2019, 451: 128-135. DOI: 10.1016/j.canlet.2019.02.054. [10] 吕建波,齐欣,陈志庚,等.基于MRI影像组学鉴别高级别胶质瘤和单发脑转移瘤的研究进展[J].磁共振成像, 2021, 12(6): 108-110. DOI: 10.12015/issn.1674-8034.2021.06.022. [11] ORTIZ-RAMÓN R, LARROZA A, RUIZ-ESPAÑA S, et al. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study[J]. Eur Radiol, 2018, 28(11): 4514-4523. DOI: 10.1007/s00330-018-5463-6. [12] KNIEP H C, MADESTA F, SCHNEIDER T, et al. Radiomics of brain MRI: utility in prediction of metastatic tumor type[J]. Radiology, 2019, 290(2): 479-487. DOI: 10.1148/radiol.2018180946. [13] LARROZA A, MORATAL D, PAREDES-SÁNCHEZ A, et al. Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI[J]. J Magn Reson Imaging, 2015, 42(5): 1362-1368. DOI: 10.1002/jmri.24913. [14] WANG H S, XUE J Y, QU T X, et al. Predicting local failure of brain metastases after stereotactic radiosurgery with radiomics on planning MR images and dose maps[J]. Med Phys, 2021, 48(9): 5522-5530. DOI: 10.1002/mp.15110. [15] 熊飞,王叶,王翅鹏,等.ADC直方图鉴别脑转移瘤病理性质的价值[J].中国临床神经外科杂志, 2018, 23(7): 452-454. DOI: 10.13798/j.issn.1009-153X.2018.07.002. [16] LI Z J, MAO Y, LI H S, et al. Differentiating brain metastases from different pathological types of lung cancers using texture analysis of T1 postcontrast MR[J]. Magn Reson Med, 2016, 76(5): 1410-1419. DOI: 10.1002/mrm.26029. [17] 李瑞,葛亚琼,张明珠,等.基于全肿瘤区域MRI纹理分析鉴别肺癌脑转移瘤病理类型的研究[J].放射学实践, 2021, 36(2): 176-180. DOI: 10.13609/j.cnki.1000-0313.2021.02.006. [18] ZHANG J, JIN J B, AI Y, et al. Differentiating the pathological subtypes of primary lung cancer for patients with brain metastases based on radiomics features from brain CT images[J]. Eur Radiol, 2021, 31(2): 1022-1028. DOI: 10.1007/s00330-020-07183-z. [19] KARAMI E, SOLIMAN H, RUSCHIN M, et al. Quantitative MRI biomarkers of stereotactic radiotherapy outcome in brain metastasis[J]. Sci Rep, 2019, 9(1): 19830. DOI: 10.1038/s41598-019-56185-5. [20] HUANG C Y, LEE C C, YANG H C, et al. Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery[J]. J Neurooncol, 2020, 146(3): 439-449. DOI: 10.1007/s11060-019-03343-4. [21] BODALAL Z, TREBESCHI S, NGUYEN-KIM T D L, et al. Radiogenomics: bridging imaging and genomics[J]. Abdom Radiol(NY), 2019, 44(6): 1960-1984. DOI: 10.1007/s00261-019-02028-w. [22] ABROL S, KOTROTSOU A, SALEM A, et al. Radiomic phenotyping in brain cancer to unravel hidden information in medical images[J]. Top Magn Reson Imaging, 2017, 26(1): 43-53. DOI: 10.1097/RMR.0000000000000117. [23] JAIN R, POISSON L M, GUTMAN D, et al. Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor[J]. Radiology, 2014, 272(2): 484-493. DOI: 10.1148/radiol.14131691. [24] MACEACHERN S J, FORKERT N D. Machine learning for precision medicine[J]. Genome, 2021, 64(4): 416-425. DOI: 10.1139/gen-2020-0131. [25] MILLER J J, SHIH H A, ANDRONESI O C, et al. Isocitrate dehydrogenase-mutant glioma: evolving clinical and therapeutic implications[J]. Cancer, 2017, 123(23): 4535-4546. DOI: 10.1002/cncr.31039. [26] FARES J, KANOJIA D, RASHIDI A, et al. Genes that mediate metastasis across the blood-brain barrier[J]. Trends Cancer, 2020, 6(8): 660-676. DOI: 10.1016/j.trecan.2020.04.007. [27] GROSSMANN P, STRINGFIELD O, EL-HACHEM N, et al. Defining the biological basis of radiomic phenotypes in lung cancer[J]. eLife, 2017, 6: e23421. DOI: 10.7554/eLife.23421. [28] LIU Y, KIM J, BALAGURUNATHAN Y, et al. Radiomic features are associated with EGFR mutation status in lung adenocarcinomas[J]. Clin Lung Cancer, 2016, 17(5): 441-448. DOI: 10.1016/j.cllc.2016.02.001. [29] 冯颖慧,陈晓东,罗泽斌,等.影像组学在非小细胞肺癌基因突变中的应用进展[J].海南医学, 2021, 32(3):372-376. DOI: 10.3969/j.issn.1003-6350.2021.03.027. [30] GE M X, ZHUANG Y J, ZHOU X L, et al. High probability and frequency of EGFR mutations in non-small cell lung cancer with brain metastases[J]. J Neurooncol, 2017, 135(2): 413-418. DOI: 10.1007/s11060-017-2590-x. [31] QIU M T, WANG J, XU Y T, et al. Circulating tumor DNA is effective for the detection of EGFR mutation in non-small cell lung cancer: a meta-analysis[J]. Cancer Epidemiol Biomarkers Prev, 2015, 24(1): 206-212. DOI: 10.1158/1055-9965.EPI-14-0895. [32] MONACO S E, NIKIFOROVA M N, CIEPLY K, et al. A comparison of EGFR and KRAS status in primary lung carcinoma and matched metastases[J]. Hum Pathol, 2010, 41(1): 94-102. DOI: 10.1016/j.humpath.2009.06.019. [33] LUO J, SHEN L, ZHENG D. Diagnostic value of circulating free DNA for the detection of EGFR mutation status in NSCLC: a systematic review and meta-analysis[J]. Sci Rep, 2014, 4: 6269. DOI: 10.1038/srep06269. [34] NAIR J K R, SAEED U A, MCDOUGALL C C, et al. Radiogenomic models using machine learning techniques to predict EGFR mutations in non-small cell lung cancer[J]. Can Assoc Radiol J, 2021, 72(1): 109-119. DOI: 10.1177/0846537119899526. [35] JUNG W S, PARK C H, HONG C K, et al. Diffusion-weighted imaging of brain metastasis from lung cancer: correlation of MRI parameters with the histologic type and gene mutation status[J]. AJNR Am J Neuroradiol, 2018, 39(2): 273-279. DOI: 10.3174/ajnr.A5516. [36] AHN S J, KWON H, YANG J J, et al. Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary lung cancer[J]. Sci Rep, 2020, 10(1): 8905. DOI: 10.1038/s41598-020-65470-7. [37] WANG G Y, WANG B M, WANG Z, et al. Radiomics signature of brain metastasis: prediction of EGFR mutation status[J]. Eur Radiol, 2021, 31(7): 4538-4547. DOI: 10.1007/s00330-020-07614-x. [38] KARACHALIOU N, MAYO C, COSTA C, et al. KRAS mutations in lung cancer[J]. Clin Lung Cancer, 2013, 14(3): 205-214. DOI: 10.1016/j.cllc.2012.09.007. [39] WEISS G J, GANESHAN B, MILES K A, et al. Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic[J]. PLoS One, 2014, 9(7): e100244. DOI: 10.1371/journal.pone.0100244. [40] LE N Q K, KHA Q H, NGUYEN V H, et al. Machine learning-based radiomics signatures for EGFR and KRAS mutations prediction in non-small-cell lung cancer[J]. Int J Mol Sci, 2021, 22(17): 9254. DOI: 10.3390/ijms22179254. [41] SHIRI I, MALEKI H, HAJIANFAR G, et al. Next-generation radiogenomics sequencing for prediction of EGFR and KRAS mutation status in NSCLC patients using multimodal imaging and machine learning algorithms[J]. Mol Imaging Biol, 2020, 22(4): 1132-1148. DOI: 10.1007/s11307-020-01487-8. [42] XING P Y, WANG S Z, WANG Q, et al. Efficacy of crizotinib for advanced ALK-rearranged non-small-cell lung cancer patients with brain metastasis: a multicenter, retrospective study in China[J]. Target Oncol, 2019, 14(3): 325-333. DOI: 10.1007/s11523-019-00637-5. [43] SONG L, ZHU Z C, MAO L, et al. Clinical, conventional CT and radiomic feature-based machine learning models for predicting ALK rearrangement status in lung adenocarcinoma patients[J]. Front Oncol, 2020, 10: 369. DOI: 10.3389/fonc.2020.00369. [44] XU X Y, HUANG L, CHEN J Y, et al. Application of radiomics signature captured from pretreatment thoracic CT to predict brain metastases in stage III/IV ALK-positive non-small cell lung cancer patients[J]. J Thorac Dis, 2019, 11(11): 4516-4528. DOI: 10.21037/jtd.2019.11.01. [45] ZHAO S J, HOU D H, ZHENG X M, et al. MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer[J]. Transl Lung Cancer Res, 2021, 10(1): 368-380. DOI: 10.21037/tlcr-20-361. [46] RIZZO S, PETRELLA F, BUSCARINO V, et al. CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer[J]. Eur Radiol, 2016, 26(1): 32-42. DOI: 10.1007/s00330-015-3814-0. [47] ZHANG T N, XU Z H, LIU G X, et al. Simultaneous identification of EGFR, KRAS, ERBB2, and TP53 mutations in patients with non-small cell lung cancer by machine learning-derived three-dimensional radiomics[J]. Cancers, 2021, 13(8): 1814. DOI: 10.3390/cancers13081814. [48] CHEN B T, JIN T H, YE N R, et al. Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases[J]. Magn Reson Imaging, 2020, 69: 49-56. DOI: 10.1016/j.mri.2020.03.002. [49] CHEN B T, JIN T H, YE N R, et al. Predicting survival duration with MRI radiomics of brain metastases from non-small cell lung cancer[J]. Front Oncol, 2021, 11: 621088. DOI: 10.3389/fonc.2021.621088. |