小细胞肺癌影像诊断研究进展

蒋旭; 苗雷; 杨琳; 孙旭杰; 胡思洁; 张丽; 李蒙

doi:10.16766/j.cnki.issn.1674-4152.003388

小细胞肺癌影像诊断研究进展

doi: 10.16766/j.cnki.issn.1674-4152.003388

蒋旭¹,
苗雷¹,
杨琳²,
孙旭杰²,
胡思洁¹,
张丽¹,
李蒙^1, ,

1.
国家癌症中心/国家肿瘤临床医学研究中心/中国医学科学院北京协和医学院肿瘤医院影像诊断科，北京 100021
2.
国家癌症中心/国家肿瘤临床医学研究中心/中国医学科学院北京协和医学院肿瘤医院病理科

基金项目:

北京市科技计划项目 Z201100005620002

详细信息

通讯作者:
李蒙，E-mail：lmcams@163.com

中图分类号: R734.2 R730.4
计量
- 文章访问数: 52
- HTML全文浏览量: 31
- PDF下载量: 6
- 被引次数: 0
出版历程
- 收稿日期: 2023-02-05
- 网络出版日期: 2024-03-27

Advances in diagnostic imaging of small cell lung cancer

JIANG Xu¹,
MIAO Lei¹,
YANG Lin²,
SUN Xujie²,
HU Sijie¹,
ZHANG Li¹,
LI Meng^{1
, ,}

1.
Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

摘要

摘要: 肺癌是中国乃至全球致死率最高的恶性肿瘤，严重威胁人类生命健康。小细胞肺癌(SCLC)约占所有肺癌的15%，是肺癌中最具侵袭性的亚型。相较于非小细胞肺癌，SCLC具有肿瘤倍增时间短、转移早、预后差的特点。影像学检查具有简单、无创的独特优势，在临床应用广泛，是肺部疾病最基本的检查方式。如何通过影像学检查实现SCLC的早发现、早诊断，从而及时干预，已成为SCLC诊疗策略中值得关注并亟待解决的问题之一。近年来，影像学检查技术飞速发展，除了传统的计算机断层成像(CT)、磁共振成像(MRI)、正电子发射断层显像检查(PET/CT)，还出现了能谱CT、功能磁共振成像(fMRI)等新技术；与此同时，影像组学与深度学习等研究方法的广泛应用，均为SCLC的准确诊断提供了强有力的帮助。因此，本文就近年来SCLC在影像学诊断方面的研究进行综述，以更好地了解SCLC的常规影像学诊断以及新技术的发展。
- 小细胞肺癌 /
- 计算机断层成像 /
- 正电子发射断层显像检查 /
- 磁共振成像 /
- 影像组学 /
- 深度学习
Abstract: Lung cancer is the malignant tumor with the highest fatality rate in China and even the world, threatening human life and health. Small cell lung cancer (SCLC) represents about 15% of all lung cancers and is the most aggressive subtype of lung cancer. Compared with non-small cell lung cancer, SCLC is marked by an exceptionally high proliferative rate, a strong predilection for early metastasis, and a poor prognosis. Imaging has the unique advantages of simplicity and non-invasiveness and is widely used in clinical practice as the most basic examination method for lung diseases. How to detect and diagnose SCLC early through imaging examination to intervene on time has become one of the most significant and urgent problems in SCLC diagnosis and treatment strategy. In recent years, imaging examination technology has developed rapidly, in addition to traditional computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography and computed tomography (PET/CT), but also the emergence of energy spectrum CT and functional magnetic resonance imaging (fMRI). At the same time, the wide application of research methods has also provided substantial help for SCLC ' s accurate image diagnosis, such as radiomics and deep learning. Therefore, this paper reviews recent research on the imaging diagnosis of SCLC, aiming to help us better understand the routine imaging diagnosis of SCLC.
- Small cell lung cancer /
- Computed tomography /
- Positron emission tomography and computed tomography /
- Magnetic resonance imaging /
- Radiomics /
- Deep learning

HTML全文

参考文献(47)

[1]	SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. doi: 10.3322/caac.21660
[2]	SIEGEL R L, MILLER K D, FUCHS H E, et al. Cancer statistics, 2022[J]. CA Cancer J Clin, 2022, 72(1): 7-33. doi: 10.3322/caac.21708
[3]	ZHENG R S, ZHANG S W, ZENG H M, et al. Cancer incidence and mortality in China, 2016[J]. J Nat Cancer Cent, 2022, 2(1): 1-9. doi: 10.1016/j.jncc.2022.02.002
[4]	THAI A A, SOLOMON B J, SEQUIST L V, et al. Lung cancer[J]. Lancet, 2021, 398(10299): 535-554. doi: 10.1016/S0140-6736(21)00312-3
[5]	肖永鑫, 于红, 刘士远, 等. 小细胞肺癌影像表现及治疗进展[J]. 国际医学放射学杂志, 2017, 40(2): 152-156. https://www.cnki.com.cn/Article/CJFDTOTAL-GWLC201702009.htm XIAO Y X, YU H, LIU S Y, et al. Imaging findings and progress in treatment of small cell lung cancer[J]. International Journal of Medical Radiology, 2017, 40(2): 152-156. https://www.cnki.com.cn/Article/CJFDTOTAL-GWLC201702009.htm
[6]	RUDIN C M, BRAMBILLA E, FAIVRE-FINN C, et al. Small-cell lung cancer[J]. Nat Rev Dis Primers, 2021, 7(1): 3. doi: 10.1038/s41572-020-00235-0
[7]	DINGEMANS A C, FRVH M, ARDIZZONI A, et al. Small-cell lung cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up[J]. Ann Oncol, 2021, 32(7): 839-853. doi: 10.1016/j.annonc.2021.03.207
[8]	GANTI A K P, LOO B W, BASSETTI M, et al. Small cell lung cancer, version 2. 2022, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2021, 19(12): 1441-1464. doi: 10.6004/jnccn.2021.0058
[9]	李燕, 董兰兰. 中央型小细胞肺癌与肺鳞癌CT征象分析[J]. 医学影像学杂志, 2021, 31(4): 586-589. https://www.cnki.com.cn/Article/CJFDTOTAL-XYXZ202104013.htm LI Y, DONG L L. Analysis of CT signs of central small cell lung cancer and lung squamous carcinoma[J]. Journal of Medical Imaging, 2021, 31(4): 586-589. https://www.cnki.com.cn/Article/CJFDTOTAL-XYXZ202104013.htm
[10]	项国靓, 龙安军, 张依云. 中央型小细胞肺癌与鳞癌患者多层螺旋CT影像特点研究[J]. 实用医学影像杂志, 2019, 20(3): 316-317. https://www.cnki.com.cn/Article/CJFDTOTAL-SYXY201903051.htm XIANG G L, LONG A J, ZHANG Y Y. To study the multi-slice spiral CT imaging features of central small cell lung cancer and squamous cell carcinoma[J]. Journal of Practical Medical Imaging, 2019, 20(3): 316-317. https://www.cnki.com.cn/Article/CJFDTOTAL-SYXY201903051.htm
[11]	KOBAYASHI T, TANAKA N, MATSUMOTO T, et al. HRCT findings of small cell lung cancer measuring 30 mm or less located in the peripheral lung[J]. Jpn J Radiol, 2015, 33(2): 67-75. doi: 10.1007/s11604-014-0381-2
[12]	MIYAUCHI E, MOTOI N, ONO H, et al. Distinct characteristics of small cell lung cancer correlate with central or peripheral origin: subtyping based on location and expression of transcription factor TTF-1[J]. Medicine (Baltimore), 2015, 94(51): e2324. DOI: 10.1097/MD.0000000000002324.
[13]	NOBASHI T, KOYASU S, NAKAMOTO Y, et al. Prognostic value of fluorine-18 fludeoxyglucose positron emission tomography parameters differs according to primary tumour location in small-cell lung cancer[J]. Br J Radiol, 2016, 89(1059): 20150618. DOI: 10.1259/bjr.20150618.
[14]	KANAJI N, SAKAI K, UEDA Y, et al. Peripheral-type small cell lung cancer is associated with better survival and higher frequency of interstitial lung disease[J]. Lung Cancer, 2017, 108: 126-133. doi: 10.1016/j.lungcan.2017.03.013
[15]	XU X, SUI X, ZHONG W, et al. Clinical utility of quantitative dual-energy CT iodine maps and CT morphological features in distinguishing small-cell from non-small-cell lung cancer[J]. Clin Radiol, 2019, 74(4): 268-277. doi: 10.1016/j.crad.2018.10.012
[16]	COZZI D, BICCI E, CAVIGLI E, et al. Radiomics in pulmonary neuroendocrine tumours (NETs)[J]. Radiol Med, 2022, 127(6): 609-615. doi: 10.1007/s11547-022-01494-5
[17]	李宇婷, 张慧芬, 郑祥武, 等. 小细胞肺癌的多层螺旋CT征象分析[J]. 医学研究杂志, 2017, 46(1): 39-42. https://www.cnki.com.cn/Article/CJFDTOTAL-YXYZ201701013.htm LI Y T, ZHANG H F, ZHENG X W, et al. Multidetector spiral CT manifestions of small cell lung cancer[J]. Journal of Medical Research, 2017, 46(1): 39-42. https://www.cnki.com.cn/Article/CJFDTOTAL-YXYZ201701013.htm
[18]	黄先敏, 方莉莉, 林修径. 小细胞肺癌及肺鳞癌多层螺旋CT影像特征及鉴别诊断分析[J]. 实用医学影像杂志, 2020, 21(6): 680-682. https://www.cnki.com.cn/Article/CJFDTOTAL-SYXY202006052.htm HUANG X M, FANG L L, LIN X J. To analyze the multi-slice spiral CT imaging features and differential diagnosis of small cell lung cancer and lung squamous cell carcinoma[J]. Journal of Practical Medical Imaging, 2020, 21(6): 680-682. https://www.cnki.com.cn/Article/CJFDTOTAL-SYXY202006052.htm
[19]	KAZAWA N, KITAICHI M, HIRAOKA M, et al. Small cell lung carcinoma: eight types of extension and spread on computed tomography[J]. J Comput Assist Tomogr, 2006, 30(4): 653-661. doi: 10.1097/00004728-200607000-00017
[20]	LEE D J, RHO J Y, KANG S, et al. CT findings of small cell lung carcinoma: can recognizable features be found?[J]. Medicine (Baltimore), 2016, 95(47): e5426. DOI: 10.1097/MD.0000000000005426.
[21]	MATSUDA I, AKAHANE M, SATO J, et al. Precision of the measurement of CT numbers: comparison of dual-energy CT spectral imaging with fast kVp switching and conventional CT with phantoms[J]. Jpn J Radiol, 2012, 30(1): 34-39. doi: 10.1007/s11604-011-0004-0
[22]	LI M, ZHANG L, TANG W, et al. Quantitative features of dual-energy spectral computed tomography for solid lung adenocarcinoma with EGFR and KRAS mutations, and ALK rearrangement: a preliminary study[J]. Transl Lung Cancer Res, 2019, 8(4): 401-412. doi: 10.21037/tlcr.2019.08.13
[23]	MU R H, MENG Z N, ZHANG X D, et al. Parameters of dual-layer spectral detector CT could be used to differentiate non-small cell lung cancer from small cell lung cancer[J]. Curr Med Imaging, 2022, 18(10): 1070-1078. doi: 10.2174/1573405618666220308105359
[24]	王珂, 吴红霞, 罗民新, 等. CT诊断中心型肺癌的准确性及MRI补充诊断的意义[J]. 中国CT和MRI杂志, 2013, 11(3): 61-63. https://www.cnki.com.cn/Article/CJFDTOTAL-CTMR201303024.htm WANG K, WU H X, LUO M X, et al. Veracity of using CT to diagnose centraltype lung cancer and the value of adopting MRI as additional diagnosis[J]. Chinese Journal of CT and MRI, 2013, 11(3): 61-63. https://www.cnki.com.cn/Article/CJFDTOTAL-CTMR201303024.htm
[25]	LI G Z, HUANG R J, ZHU M, et al. Native T₁-mapping and diffusion-weighted imaging (DWI) can be used to identify lung cancer pathological types and their correlation with Ki-67 expression[J]. J Thorac Dis, 2022, 14(2): 443-454. doi: 10.21037/jtd-22-77
[26]	SHEN G H, JIA Z Y, DENG H F. Apparent diffusion coefficient values of diffusion-weighted imaging for distinguishing focal pulmonary lesions and characterizing the subtype of lung cancer: a meta-analysis[J]. Eur Radiol, 2016, 26(2): 556-566. doi: 10.1007/s00330-015-3840-y
[27]	库雷志, 马明平, 俞顺, 等. DCE-MRI在肺癌不同病理类型的诊断价值[J]. 中国医学影像学杂志, 2016, 24(2): 100-105. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYYZ201602008.htm KU L Z, MA M P, YU S, et al. Dynamic contrast-enhanced MRI in diagnosis of different histopathological types of lung carcinoma[J]. Chinese Journal of Medical Imaging, 2016, 24(2): 100-105. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYYZ201602008.htm
[28]	党珊, 郭炎兵, 马光明, 等. 体素内不相干运动扩散加权成像对小细胞肺癌与非小细胞肺癌的鉴别诊断[J]. 中国医学影像学杂志, 2020, 28(5): 357-361. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYYZ202005010.htm DANG S, GUO Y B, MA G M, et al. Differentiation of small cell lung carcinoma and non-small cell lung carcinoma by intravoxel incoherent motion diffusion weighted imaging[J]. Chinese Journal of Medical Imaging, 2020, 28(5): 357-361. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYYZ202005010.htm
[29]	汪玉, 赵鹏, 贾阳, 等. 小视野IVIM-DWI在非小细胞肺癌与小细胞肺癌鉴别诊断中的应用[J]. 医学影像学杂志, 2022, 32(4): 614-618. https://www.cnki.com.cn/Article/CJFDTOTAL-XYXZ202204016.htm WANG Y, ZHAO P, JIA Y, et al. The application of zoomed IVIM-DWI in diagnosis of non-small cell lung cancer and small cell lung cancer[J]. Journal of Medical Imaging, 2022, 32(4): 614-618. https://www.cnki.com.cn/Article/CJFDTOTAL-XYXZ202204016.htm
[30]	翟渌澜, 陆文杰, 李彭. 磁共振弥散加权序列在小细胞肺癌脑转移瘤诊断中的应用[J]. 医学影像学杂志, 2016, 26(5): 775-778. https://www.cnki.com.cn/Article/CJFDTOTAL-XYXZ201605005.htm ZHAI L L, LU W J, LI P. The application of diffusion weighted sequence in magnetic resonance for the diagnosis of small cell lung cancer with brain metastases[J]. Journal of Medical Imaging, 2016, 26(5): 775-778. https://www.cnki.com.cn/Article/CJFDTOTAL-XYXZ201605005.htm
[31]	BOZDAǦ M, ER A, ČINKOOǦLU A. Histogram analysis of ADC maps for differentiating brain metastases from different histological types of lung cancers[J]. Can Assoc Radiol J, 2021, 72(2): 271-278. doi: 10.1177/0846537120933837
[32]	MVLLER S J, KHADHRAOUI E, NEEF N E, et al. Differentiation of brain metastases from small and non-small lung cancers using apparent diffusion coefficient (ADC) maps[J]. BMC Med Imaging, 2021, 21(1): 70. doi: 10.1186/s12880-021-00602-7
[33]	LOCOCO F, TREGLIA G, CESARIO A, et al. Functional imaging evaluation in the detection, diagnosis, and histologic differentiation of pulmonary neuroendocrine tumors[J]. Thorac Surg Clin, 2014, 24(3): 285-292. doi: 10.1016/j.thorsurg.2014.04.004
[34]	JIANG R, DONG X, ZHU W, et al. Combining PET/CT with serum tumor markers to improve the evaluation of histological type of suspicious lung cancers[J]. PLoS One, 2017, 12(9): e0184338. DOI: 10.1371/journal.pone.0184338.
[35]	李红, 王晓明, 徐微娜, 等. 肺癌术前¹⁸F-FDG PET/CT对纵隔淋巴结外科分期的临床价值[J]. 中华核医学与分子影像杂志, 2012, 32(3): 180-182. https://www.cnki.com.cn/Article/CJFDTOTAL-CTMR202403014.htm LI H, WANG X M, XU W N, et al. Clinical value of preoperative ¹⁸F-FDG PET/CT in surgical staging of mediastinal lymph nodes in lung cancer[J]. Chinese Journal of Nuclear Medicine and Molecular Imaging, 2012, 32(3): 180-182. https://www.cnki.com.cn/Article/CJFDTOTAL-CTMR202403014.htm
[36]	KISHIDA Y, SEKI S, YOSHIKAWA T, et al. Performance comparison between ¹⁸F-FDG PET/CT plus brain MRI and conventional staging plus brain MRI in staging of small cell lung carcinoma[J]. AJR Am J Roentgenol, 2018, 211(1): 185-192.
[37]	KUMAR V, GU Y, BASU S, et al. Radiomics: the process and the challenges[J]. Magn Reson Imaging, 2012, 30(9): 1234-1248.
[38]	孙瑶, 王祥, 萧毅. 深度学习技术在肺癌影像学诊断中的应用进展[J]. 肿瘤影像学, 2021, 30(6): 525-531. https://www.cnki.com.cn/Article/CJFDTOTAL-YXYX202106015.htm SUN Y, WANG X, XIAO Y. Application progress of deep learning techniques in lung cancer imaging diagnosis[J]. Oncoradiology, 2021, 30(6): 525-531. https://www.cnki.com.cn/Article/CJFDTOTAL-YXYX202106015.htm
[39]	E L, LU L, LI L, et al. Radiomics for classification of lung cancer histological subtypes based on nonenhanced computed tomography[J]. Acad Radiol, 2019, 26(9): 1245-1252.
[40]	E L, LU L, LI L, et al. Radiomics for classifying histological subtypes of lung cancer based on multiphasic contrast-enhanced computed tomography[J]. J Comput Assist Tomogr, 2019, 43(2): 300-306.
[41]	CHEN B T, CHEN Z, YE N, et al. Differentiating peripherally-located small cell lung cancer from non-small cell lung cancer using a CT radiomic approach[J]. Front Oncol, 2020, 10: 593.
[42]	GUO Y X, SONG Q, JIANG M M, et al. Histological subtypes classification of lung cancers on CT images using 3D deep learning and radiomics[J]. Acad Radiol, 2021, 28(9): e258-e266.
[43]	郭炎兵, 党珊, 段海峰, 等. 基于磁共振图像的影像组学特征对小细胞与非小细胞肺癌的鉴别诊断价值研究[J]. 临床放射学杂志, 2020, 39(9): 1776-1779. https://www.cnki.com.cn/Article/CJFDTOTAL-LCFS202009024.htm GUO Y B, DANG S, DUAN H F, et al. The value of MR-based radiomics signature for differentiating small cell lung cancer from non-small cell lung cancer[J]. Journal of Clinical Radiology, 2020, 39(9): 1776-1779. https://www.cnki.com.cn/Article/CJFDTOTAL-LCFS202009024.htm
[44]	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.
[45]	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.
[46]	SHI J X, ZHAO Z L, JIANG T, et al. A deep learning approach with subregion partition in MRI image analysis for metastatic brain tumor[J]. Front Neuroinform, 2022, 16: 973698. DOI: 10.3389/fninf.2022.973698.
[47]	GROSSMAN R, HAIM O, ABRAMOV S, et al. Differentiating small-cell lung cancer from non-small-cell lung cancer brain metastases based on MRI using efficientnet and transfer learning approach[J]. Technol Cancer Res Treat, 2021, 20: 153303382110 04919. DOI: 10.1177/15330338211004919.