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Article(id=1198558271792575061, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198558265329152414, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.0994.2024.0104, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1690300800000, receivedDateStr=2023-07-26, revisedDate=null, revisedDateStr=null, acceptedDate=1694361600000, acceptedDateStr=2023-09-11, onlineDate=1763688160131, onlineDateStr=2025-11-21, pubDate=1722096000000, pubDateStr=2024-07-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1763688160131, onlineIssueDateStr=2025-11-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1763688160131, creator=13701087609, updateTime=1763688160131, updator=13701087609, issue=Issue{id=1198558265329152414, tenantId=1146029695717560320, journalId=1189873630562394117, year='2024', volume='49', issue='7', pageStart='733', pageEnd='854', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1763688158589, creator=13701087609, updateTime=1763689196450, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1198562618517581944, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198558265329152414, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1198562618517581945, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198558265329152414, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=848, endPage=854, ext={EN=ArticleExt(id=1198558273109586536, articleId=1198558271792575061, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=Research progress of tumor autoantibodies and CT artificial intelligence in early diagnosis of NSCLC, columnId=1190243275882729994, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Review, runingTitle=null, highlight=null, articleAbstract=

Non-small cell lung cancer ( NSCLC ) is one of the most prevalent and deadly malignant tumors in the world. In recent years, artificial intelligence (AI) in computed tomography (CT) has harnessed the power of big data to automatically extract and learn imaging features, thereby assisting radiologists in reducing the workload and missed diagnosis rate of pulmonary nodules. An ELISA kit for detecting seven lung cancer autoantibodies (p53, SOX2, PGP9.5, CAGE, MAGE-A1, GAGE7, and GBU4-5) has been clinically implemented in China, showing high specificity in the early screening of NSCLC. Additionally, other liquid biopsy techniques such as circulating tumor DNA (ctDNA) methylation markers are also continually being explored. However, existing methods for the early diagnosis of lung cancer all have their limitations, and optimizing their combination or establishing diagnostic models has become a trend. This review summarizes the research progress and value of the seven lung cancer autoantibodies and CT AI in the early diagnosis of NSCLC, with the aim of providing a reference for their combined use in the early diagnosis of lung cancer in Chinese population.

, correspAuthors=Cheng-Kai Zhai, authorNote=null, correspAuthorsNote=
E-mail:
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非小细胞肺癌(NSCLC)是全球发病和死亡原因排位居前的恶性肿瘤。近年来，CT人工智能利用大数据自动提取与学习影像学特征，可帮助影像科医师减少肺结节诊断的工作量与漏诊率。肺癌7种自身抗体(p53、SOX2、PGP9.5、CAGE、MAGE-A1、GAGE7、GBU4-5)试剂盒投入中国临床使用，在NSCLC的早期筛查中表现出高特异度。此外，ctDNA甲基化等其他液体活检技术也在不断探索中。然而，现有的各种方法用于肺癌早期诊断均有不足，将其优化组合或建立诊断模型已成为一种趋势。本文综述肺癌7种自身抗体与CT人工智能在NSCLC早期诊断中的相关研究进展及其价值，以期为其联合用于中国人群肺癌的早期诊断提供参考。

, correspAuthors=翟成凯, authorNote=null, correspAuthorsNote=
翟成凯，E-mail：
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王晶，硕士研究生，主要从事肺癌早期诊断等方面的研究

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王晶，硕士研究生，主要从事肺癌早期诊断等方面的研究

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王晶，硕士研究生，主要从事肺癌早期诊断等方面的研究

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肿瘤自身抗体及CT人工智能在NSCLC早期诊断中的应用研究进展
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王晶 , 翟成凯 *
解放军医学杂志 | 综述 2024,49(7): 848-854
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解放军医学杂志 | 综述 2024, 49(7): 848-854
肿瘤自身抗体及CT人工智能在NSCLC早期诊断中的应用研究进展
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王晶, 翟成凯*
作者信息
  • 新乡医学院第五临床学院/新乡市第一人民医院呼吸与危重症医学科，河南新乡 453000

    • 王晶，硕士研究生，主要从事肺癌早期诊断等方面的研究

通讯作者:

翟成凯，E-mail：
Research progress of tumor autoantibodies and CT artificial intelligence in early diagnosis of NSCLC
Jing Wang, Cheng-Kai Zhai*
Affiliations
  • Department of Respiratory and Critical Care Medicine, the Fifth Clinical College of Xinxiang Medical University/the First People's Hospital of Xinxiang, Xinxiang, Henan 453000, China
出版时间: 2024-07-28 doi: 10.11855/j.issn.0577-7402.0994.2024.0104
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摘要
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非小细胞肺癌(NSCLC)是全球发病和死亡原因排位居前的恶性肿瘤。近年来，CT人工智能利用大数据自动提取与学习影像学特征，可帮助影像科医师减少肺结节诊断的工作量与漏诊率。肺癌7种自身抗体(p53、SOX2、PGP9.5、CAGE、MAGE-A1、GAGE7、GBU4-5)试剂盒投入中国临床使用，在NSCLC的早期筛查中表现出高特异度。此外，ctDNA甲基化等其他液体活检技术也在不断探索中。然而，现有的各种方法用于肺癌早期诊断均有不足，将其优化组合或建立诊断模型已成为一种趋势。本文综述肺癌7种自身抗体与CT人工智能在NSCLC早期诊断中的相关研究进展及其价值，以期为其联合用于中国人群肺癌的早期诊断提供参考。

非小细胞肺癌  /  自身抗体  /  人工智能  /  肺结节  /  早期诊断

Non-small cell lung cancer ( NSCLC ) is one of the most prevalent and deadly malignant tumors in the world. In recent years, artificial intelligence (AI) in computed tomography (CT) has harnessed the power of big data to automatically extract and learn imaging features, thereby assisting radiologists in reducing the workload and missed diagnosis rate of pulmonary nodules. An ELISA kit for detecting seven lung cancer autoantibodies (p53, SOX2, PGP9.5, CAGE, MAGE-A1, GAGE7, and GBU4-5) has been clinically implemented in China, showing high specificity in the early screening of NSCLC. Additionally, other liquid biopsy techniques such as circulating tumor DNA (ctDNA) methylation markers are also continually being explored. However, existing methods for the early diagnosis of lung cancer all have their limitations, and optimizing their combination or establishing diagnostic models has become a trend. This review summarizes the research progress and value of the seven lung cancer autoantibodies and CT AI in the early diagnosis of NSCLC, with the aim of providing a reference for their combined use in the early diagnosis of lung cancer in Chinese population.

non-small-cell lung carcinoma  /  autoantibodies  /  artificial intelligence  /  pulmonary nodules  /  early diagnosis
王晶, 翟成凯. 肿瘤自身抗体及CT人工智能在NSCLC早期诊断中的应用研究进展. 解放军医学杂志, 2024 , 49 (7) : 848 -854 . DOI: 10.11855/j.issn.0577-7402.0994.2024.0104
Jing Wang, Cheng-Kai Zhai. Research progress of tumor autoantibodies and CT artificial intelligence in early diagnosis of NSCLC[J]. Medical Journal of Chinese People’s Liberation Army, 2024 , 49 (7) : 848 -854 . DOI: 10.11855/j.issn.0577-7402.0994.2024.0104
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癌症是威胁人类健康的重大慢性疾病。GLOBOCAN 2020数据显示，在全球所有癌症中，肺癌发病占比11.4%，居第2位，死亡病因占比18.0%，居第1位[1]。中国肺癌的发病率和病死率均高于世界平均水平，造成严重的经济和社会负担[2]。据国际肺癌研究协会(The International Association for the Study of Lung Cancer，IASLC)统计，ⅠA期肺癌患者的5年生存率>75%，ⅢA期为36%，ⅢB期为19%，而Ⅳ期仅为6%[3]。非小细胞肺癌(non-small cell lung cancer，NSCLC)是肺癌的主要病理类型(占75%~85%)，其早期识别对于肺癌的早诊断早治疗至关重要[4]。国内外多个医学组织均推荐低剂量CT(low-dose computed tomography，LDCT)用于肺癌高危人群筛查，但其假阳性率高达96.4%，可导致≤67%的过度诊断[5-6]。近年来，CT人工智能(artificial intelligence，AI)技术快速进步，通过对大型影像数据库的自我学习，非侵入性捕获、分类及量化通常人眼难以识别的表型特征，可显著提高肺结节良恶性的诊断效能和工作效率[7]。液体活检技术也成为肿瘤早期诊断的研究热点，肿瘤相关自身抗原及抗体作为液体活检的标志物之一，在肺癌的早期诊断方面体现出巨大潜能。2018年，经中国食品药品监督管理总局(CFDA)批准的专门为中国人群选择的7种肿瘤自身抗体(seven tumor autoantibodies，7-TAABs)投入临床使用，展现了对肺癌尤其是NSCLC早期筛查的高度特异性。鉴于每种抗体单独应用均有一定的局限性，将其优化组合或结合临床特征建立诊断模型已经成为一种新趋势。本文主要综述7-TAABs [p53蛋白、G抗原7(G antigen 7，GAGE7)、蛋白基因产物9.5(PGP9.5)、癌症相关基因(cancer-associated gene，CAGE)、黑色素瘤相关抗原A1(melanoma associated antigen-A1，MAGE-A1)、性别决定区Y框2(SOX2)蛋白和肿瘤抗原4-5(GBU4-5)的肿瘤自身抗体] 与CT人工智能在肺癌早期诊断中的研究进展与应用前景，旨在为两者联合用于中国人群肺癌的早期诊断提供参考。
1 7-TAABs
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肿瘤相关抗原(tumor-associated antigens，TAAs)及自身抗体是一类高特异性的肿瘤指标[8]。TAAs目前已知的产生机制包括通过突变、翻译后修饰等产生新的抗原表位来增加免疫原性，过表达来突破免疫耐受，以及抗原的异位表达，从而产生相应的自身抗体。由于免疫系统的敏感性和稳定性，自身抗体可在影像学筛查出肺癌的5年前被检出。自身抗体的半衰期通常可达30 d，且性质稳定、表达量高、易被酶联免疫吸附实验检测，这些优势使自身抗体成为肿瘤早期诊断的研究热点。
与肺癌相关的自身抗体种类繁多，单独使用其中一种的诊断敏感度很低，常将其联合应用于肺癌的早期诊断。国外著名的EarlyCDT-Lung面板包括7种自身抗体(p53、NY-ESO-1、CAGE， GBU4-5、SOX2、HuD和MAGE-A4)，对美国早期肺癌患者具有良好的诊断效能。但由于不同种族的基因组差异，EarlyCDT-Lung面板对亚洲肺癌人群的适用性可能不高。2018年，周彩存教授团队开展了针对中国人群的大规模多中心前瞻性研究，从43种TAAs中鉴定出7种抗原(p53、GAGE7、PGP9.5、CAGE、MAGE-A1、SOX2和GBU4-5)，确定了对应各抗体的最佳截断值，训练集中该抗体谱诊断肺癌的敏感度和特异度分别为61%和90%，并在样本量为2008例的验证集中表现出7-TAABs的良好重复性[9]。鉴于该研究，7-TAABs联合检测的试剂盒已被CFDA批准用于肺癌的筛查与诊断。
1.1 p53蛋白
人类基因组卫士——p53基因位于人类染色体17p13.1，编码53 kD的核内磷酸化蛋白，即p53蛋白。该蛋白作为一种转录因子，可激活或抑制某些基因如p21、BaX、Bcl-2，从而阻滞细胞周期、诱导细胞凋亡[10]。近年来研究显示，p53蛋白还可通过自噬、改变肿瘤微环境、调节铁死亡等途径抑制肿瘤的产生[11]。一旦p53基因发生突变，野生型p53蛋白的抑癌作用消失，基因组不稳定可导致染色体非整倍体形成。更有研究显示，突变的p53可能参与肿瘤浸润转移，促使癌细胞逃避免疫监控，帮助肿瘤形成新生血管，并参与肿瘤代谢重编程[12-13]。当NSCLS患者TNM Ⅲ/Ⅳ期、癌组织高分化、存在淋巴结转移、病理类型为鳞癌时，血清中p53阳性率更高[14-15]。
1.2 SOX2蛋白
SOX2基因位于染色体3q26.3-q27上，其表达产物SOX2蛋白是一组能使人类体细胞转化为多能干细胞的因子之一，可作为癌基因调控恶性肿瘤干细胞，在肿瘤的发生发展中起重要作用。研究显示，SOX2可与EGFR之间形成负反馈环而促进肺癌细胞生长繁殖[16]，可促使NSCLC上皮间充质转化而增强肿瘤细胞的侵袭能力[17]，可与Oct4共同参与肺癌组织微淋巴和微血管的生成而推进肿瘤淋巴转移和血行转移，调节survivin的表达并发挥其抗凋亡作用，上调胱氨酸转运体SLCA711在肺癌干细胞样细胞中的表达而增加癌细胞对铁下垂的抗性等[18]。多项研究结果显示，SOX2过表达可能提示肿瘤体积大、肺鳞癌病理类型、低分化水平、临床分期晚与预后不良[19-21]。然而，Shao等[22]和Li等[23]研究发现，SOX2过表达可能有利于NSCLC的整体生存。
1.3 PGP9.5
PGP9.5是泛素羧基末端水解酶家族的成员之一，可使细胞周期蛋白去泛素化以躲避蛋白酶体的水解，致使细胞不可控生长，进而促进肿瘤形成。PGP9.5广泛存在于神经元和神经内分泌细胞，可作为肺癌神经内分泌(neuroendocrine，NE)分化的标志，有学者报告PGP9.5在NSCLC中与神经烯醇化酶(neuron specific enolase，NSE)、突触素(synaptophysin，SYN)表达呈正相关，但其表达不依赖于神经的分化而独立存在。Hibi等[24]发现54%的早期NSCLC表达PGP9.5。多项研究提示，肺鳞癌患者的PGP9.5水平显著高于肺腺癌患者[15,25]。PGP9.5与肿瘤的侵袭密切相关，是肺癌预后不良的预测因子[26]。
1.4 肿瘤/睾丸抗原(cancer/testis antigens，CTA)
CTA只在睾丸、卵巢、胎盘及恶性肿瘤中表达，在人体其他正常组织及良性肿瘤中不表达。研究显示，CTA在肺癌、黑色素瘤、肝细胞癌、结肠癌、肾癌等多种恶性肿瘤中存在。启动子去甲基化可激活CTA基因的表达，从而促进肿瘤的发生。CTA还可通过影响肿瘤的表型特征包括侵袭性、免疫逃避和转移能力，加快肿瘤的发展。7-TAABs中与肺癌关系密切的CTA包括GAGE7、MAGE-A1、CAGE和GBU4-5。
GAGE基因作为CTA基因家族的一员，编号为CT4，位于染色体Xp11.2-11.4，目前已发现十余个成员。GAGE在NSCLC中的表达率与临床分期晚相关，其高水平表达(>50%)在肺鳞癌中更为常见[27]。
MAGE-A基因的表达受表观遗传的调控——启动子的去甲基化和组蛋白的乙酰化，但启动子去甲基化是诱发MAGE-A基因表达的首要原因。MAGE-A与p53基因存在较为复杂的联系。Marcar等[28]发现MAGE-A可阻断p53与启动子结合，从而抑制其转录；而桑梅香等[29]却得到相反的结论。MAGE-A1在NSCLC和小细胞肺癌(small-cell lung cancer，SCLC)中均有表达，但在NSCLC中的表达程度更高[30]。关于NSCLC病理类型及临床分期与MAGE表达率的联系，目前仍存在争议[31-32]。
CAGE基因是2002年Cho等[33]通过用肺癌患者血清筛选cDNA文库报告的一个新的CTA基因，位于Xp22，编码DEAD box解旋酶。CAGE基因表达产物通过诱导转录因子激活蛋白1(activator protein‑1，AP-1)和早期2因子-1(E2F-1)依赖的细胞周期蛋白D1和E的表达来促进细胞周期进程[34]。CAGE还可通过激活ERK和p38 MAPK，诱导过氧化氢酶活性和降低ROS水平，增强癌细胞的侵袭扩散能力[35]。
GBU4-5基因也编码DEAD box蛋白质，即RNA解旋酶。DEAD box蛋白质参与RNA加工与降解、核糖体组装、精子与胚胎发生，以及细胞生长和分裂等多个过程，在致癌途径中也有重要作用。随着TNM分期的增加，肺癌患者血清GBU4-5浓度也降低，提示其在肺癌早期诊断中价值较高[25]。关于GBU4-5表达情况与NSCLC病理类型的关系，目前仍有争议[25,36]。
1.5 7-TAABs联合诊断
在以上7种自身抗体中，多项研究均报告GBU4-5的受试者工作特征曲线下面积(area under curve，AUC)最高[37-38]，PGP9.5的敏感度最高[36-37]，几乎所有抗体均具有较高的特异度。然而，单一自身抗体的敏感度远不能满足肺癌早期筛查的需要，多项研究均报告7-TAABs联合检测比单个自身抗体检测更能明显提升肺癌的诊断效能[36,39-40]。其中，对于直径≤8 mm的恶性肺结节，7-TAABs联合诊断敏感度可达56.7%~57.3%，提示其在肺癌早期具有较高的诊断价值[41-42]。北京协和医院通过对100例不确定实性结节的长期随访，报告7-TAABs与肺癌患病高风险相关，具有良好的预测价值[43]。
关于7-TAABs联合诊断在不同病理类型肺癌中的差异，多项研究显示其对于肺鳞癌的敏感性高于肺腺癌[36, 44-45]。贺侠琴等[46]认为7-TAABs联合检测对SCLC的诊断价值最优，其AUC可达0.837。对于7-TAABs在实性结节和磨玻璃结节中的诊断率差异问题，意见尚不一致[41,47]。关于7-TAABs水平与肿瘤侵袭性的关系，一项预测纯磨玻璃结节中肺腺癌浸润性的研究显示，自身抗体阳性率在浸润性病变中明显高于浸润前病变，使用影像学指标(结节直径、平均CT值)联合自身抗体判别肺腺癌是否浸润的敏感度、特异度及AUC可分别达92.5%、89.6%和0.956[48]。此外，多项研究报告，随着肿瘤分期的增高，7-TAABs阳性率也明显增高[41,49-50]。
7-TAABs预测肺癌预后情况的相关研究显示，在7-TAABs中，p53、SOX2、PGP9.5和MAGE-A1水平与NSCLC患者的总生存期(overall survival，OS)呈负相关，过表达p53和PGP9.5可预示较短的无进展生存期(progression free survival，PFS)[15,51-52]。关于7-TAABs与NSCLC病理及高通量基因测序(next generation sequencing，NGS)结果的关系，有研究显示，浸润性腺癌中，7-TAABs与分化低、分期晚、高Ki-67及高EGFR突变率、较低的ERBB2突变与ROS1融合突变有关；而在肺鳞癌中则不明显[53]。需要注意的是，7-TAABs可能在其他癌症患者体内也存在，但在肺癌中的水平仍高于其他癌症[54]。
2 CT人工智能
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1956年，McCarthy等在达特茅斯学会上正式提出“人工智能”的概念[55]。随着计算机硬件性能的不断增强，作为人工智能的重要分支，机器学习登上科技舞台。研究显示，计算机辅助诊断系统(computer aided diagnosis，CAD)对肺结节的检出能力可赶上甚至超过传统医师[56]。随后，深度学习(deep learning)成为机器学习的一种新范式，可自我学习以前未知的特征，常见的有卷积神经网络(convolutional neural networks，CNNs)、随机森林(random forest，RF)等。其中，相较于RF，CNNs具有更高的阳性预测值(95%)和敏感度(90%)[57]。2012年，Lambin等[58]提出“放射组学”(radiomics)的概念，用来描述从医学影像中高通量提取定量特征。放射组学与CNNs作为人工智能分析的两项核心技术，可选择性提取和量化肉眼不易看出的影像“纹理”特征，并将其自动化分类，使人工智能在医学领域特别是肿瘤影像学领域的应用不断进步，在肿瘤的检出、诊断上展现了巨大潜能。例如，肺结节大小的测量，由传统的最长径转变为三维体积，加上“时间减法”技术使得对肺结节变化的敏感度增高，体积测量更加准确；利用容积倍增时间(volume doubling time，VDT)鉴别良恶性肺结节更加可靠。
深度学习与大型管理数据库强强联手，研发出多个肺结节的良恶性诊断模型，经多次验证及优化更新后投入医学影像市场，使得大量重复繁重的工作自动化，不仅使影像结果的分析时间大大缩短，还使肺结节检测敏感度和准确率得到提升，工作效率明显提高[59-60]。
但目前人工智能仍存在多项不足：第一，由于CNNs的“自我学习”，关于开发放射组特征的方法模糊，使其稳定性和可重复性不佳；第二，诊断模型的开发需要大量测试数据集和验证数据集，理想情况下二者需分离，但实际数据库规模有限，两种数据集常存在交集，导致“过拟合”的问题；第三，迄今为止，很多相关研究都是在肺图像数据库(the Lung Image Database Consortium，LIDC)进行的，其诊断结论多为影像科医师的主观判断。关于肺结节的单纯影像学人工智能诊断也有一定不足：首先，其特异度不够理想，既往研究显示，这些诊断模型的特异度为70%~80%[57-60]；其次，抛开其他影响肺癌诊断的因素如年龄、吸烟史、临床表现、实验室指标等，单从影像学层面鉴别肺结节良恶性有失偏颇。因此，建立人工智能联合其他高特异度的指标如7-TAABs的诊断模型尤为重要。
近年来，除了应用较多的CT图像，人工智能还尝试整合其他放射组学资料(如核医学)及人口统计数据、生物标志物等，以对肺结节进行肺癌风险评估，并预测组织学和分子结果。此外，在肺癌确诊患者中，人工智能还可引入病理组学、基因组学及蛋白质组学数据，创建集成模型，以预测术后肿瘤复发率、放化疗、免疫治疗和靶向治疗的疗效及不良反应，从而促进肺癌的精准医疗。人工智能目前的相关应用仍在不断推进，可以预见，在未来肺癌的临床诊治中将发挥更大作用。
3 7-TAABs联合影像学
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在肺癌早期发现、早期诊断方面，影像学检查一直是临床医师的首选方法，然而，其高假阳性率常导致需要长期影像学随访以明确肺结节性质，加重了患者潜在的辐射风险。近年来，7-AABs展现了对肿瘤早期筛查的高特异度，但其灵敏度并不理想；将其联合诊断可能补其不足，并获得附加价值，具有一定的肺癌早期诊断潜力。
一项仅纳入临床分期为Ⅰ期及Ⅱ期肺癌的回顾性研究显示，将LDCT与血清7-TAABs联合应用时，其灵敏度及AUC分别提升至96.7%和0.886，明显高于LDCT或血清7-TAABs单独检测(96.7% vs. 80.0% vs. 66.7%；0.886 vs. 0.704 vs. 0.725)[61]。另一项针对肺癌高危人群的前瞻性研究显示，7-TAABs联合LDCT检测的灵敏度、AUC及阳性预测值(positive predictive value，PPV)均明显高于LDCT组及血清7-TAABs检测组(灵敏度：88.4% vs. 65.7% vs. 74.6%；AUC：0.863 vs. 0.651 vs. 0.733；PPV：86.4% vs. 57.0% vs. 56.4%)[62]。Chang等[63]将7-AABs面板、LDCT筛查和多平面重组联合应用于肺结节良恶性诊断，PPV提升至89.5%，假阳性率则降低为21.1%。
通过纳入207例NSCLC患者(其中92.3%为I期)及61例肺部良性病变患者，Wang等[64]报道，对于非侵袭性腺癌或初步影像学诊断不确定的患者，7-TAABs的诊断效能包括敏感度均优于CT；提示在肺癌未浸润前，或肺结节在影像上恶性征象表现不明显时，使用该抗体谱能引导临床医师做出相对准确的判断。此外，他们尝试构建了包含7-TAABs、影像科医师诊断结果、CT四种恶性征象、结节组成、结节直径、结节数量、性别和年龄的计分表，以方便临床医师较客观地鉴别良恶性肺结节。经验证组测试，该评分表在截断值为9.75时，其AUC值明显高于单独使用7-AAB面板或CT扫描(0.860 vs. 0.660 vs. 0.531)。
相似地，刘霄等[45]将7-TAABs、CT结节大小、CT结节类型、年龄、性别及吸烟史纳入肺癌风险预测模型，当检测临床分期以I期为主的NSCLC患者时，与单独应用7-TAAbs相比，该模型的敏感度由48.84%提升为89.36%，PPV由93.33%提升为97.67%。
影像学诊断常受影像科医师的经验和主观性影响而差异较大，故用人工智能来分析CT图像逐渐成为肺癌早期诊断的热点。Xu等[41]尝试建立了7-TAABs联合人工智能的诊断模型，与单独7-TAABs相比，肺结节良恶性诊断敏感度由59.7%提升至96.4%，AUC由0.7476提升至0.96。
4 其他液体活检标志物的研究进展
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液体活检指通过获取血液、胸腔积液等体液中的分子标志物来探查实体肿瘤，这些标志物除了肿瘤相关抗原及自身抗体外，还包括循环肿瘤DNA (circulating tumor DNA，ctDNA)、甲基化标记、循环肿瘤细胞(circulating tumor cell，CTC)、微小RNAs、外泌体等，具有无创或微创性、可多次取样、动态实时检测、特异度高等优点，成为肿瘤筛查、诊断及评估疗效及预后相关研究的热点。
肿瘤细胞释放到外周循环系统的DNA片段称为ctDNA，常包含肿瘤特异性信息。一个整合了5个循环游离DNA(circulating free DNA，cfDNA)片段组学特征和5种机器学习算法的堆叠集成模型对Ⅰ期肺癌的敏感度高达83.2%[65]。甲基化标记作为一种表观遗传修饰，可在ctDNA片段中检测。一项纳入33项研究的荟萃分析结果显示，目前被研究较多的甲基化ctDNA包括SHOX2、RASSF1A和APC[66]。华西医院开发了两种使用多位点qPCR检测血浆ctDNA甲基化的模型，分别用于肺癌的筛查(敏感度达90.6%)及辅助诊断 (特异度达83.3%)[67]。CTC由肿瘤细胞向血液中迁移形成，在血液中含量低，因此该项检测面临的首要问题是CTC的富集分离。近年来开发的免疫磁珠法、CANpatrol等检测技术提高了其敏感度，将CTC与其他方法组合能进一步提高肺癌早期诊断效能。叶酸受体阳性的CTC在结合6个传统肿瘤标志物后，鉴别良恶性肺结节的AUC由0.746提升至0.922[68]。微小RNA是一组长度为19~24个核苷酸的内源性非编码单链RNA分子。意大利国立癌症研究所基金会招募了4119例吸烟者参与一项结合LDCT和血液microRNA检测的每3年一次的肺部筛查计划，经4次筛查，两项均阴、任一项阳性及两项均阳的个体累积发病率分别为0.6%、3.8%和20.1%[69]。由此可见，将微小RNA与影像学结合可显著提高肺癌筛查的检出率。外泌体是一组异质性纳米级膜结构囊泡，携带蛋白质、脂质、核酸和各种小分子，参与细胞内或细胞间的信号转导或生物物质的运输。Jin等[70]采用高通量测序(NGS)技术分离筛选出分别适用于诊断NSCLC、肺腺癌及肺鳞癌的特异性外泌体组合，它们的AUC分别达0.899、0.936和0.911。
目前，上述液体活检标志物在肺癌诊断中的实际临床应用较少，主要有以下不足：第一，内部参考标志物的选择不一致；第二，标志物的分离和提取方法缺乏统一标准；第三，灵敏度低，迫切需要技术革新以提高其诊断效能。近年来，各种新型检测技术陆续被开发，这些液体活检标志物的诊断优势逐渐突出，有望成为肺癌早期诊断的未来发展方向。
将影像学与多项液体活检技术联合应用于肺癌的早期筛查与诊断，Lastwika等[71]使用包含超过3200种抗体的大型列阵平台，探测出有肺癌诊断意义的蛋白质、糖类和自身抗原及抗体，经验证共筛选出8个血清标志物，将它们与2个放射学CT特征及5个语义特征结合起来，对肺结节良恶性判断的AUC超过0.95。因此，将多种非侵袭性肺癌早期筛查方法优化组合，有望进一步提高肺癌的早期诊断效能。当然，组合哪些项目、怎样组合，仍需大量前瞻性临床研究进行探索与验证，并调查其对患者的实际临床效用。
5 总结与展望
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肺癌相关自身抗体的高特异度使其具有较好的临床应用前景。影像学筛查有助于发现早期肺癌，人工智能放射组学通过将肺结节影像资料整理与分类，使其更加客观及量化；一些基于二者联合应用的模型已显示出早期肺癌诊断的潜力。ctDNA甲基化等其他液体活检标志物离实际临床应用还有一段距离，需要设计研发统一的试剂盒，以便临床推广应用。然而，每种方法都有其各自的优缺点，将其优化组合或结合临床特征建立诊断模型已成为一种新趋势，未来有望将放射组学、蛋白质组学、人口学等特征进一步整合，构建多元集成模型，以更全面、准确地评估肺癌风险。另外，各种单独或联合检测方法对区分肺癌病理类型、临床分期，评估疗效及预后等方面的意义仍需进一步探索。
国内上市的7-TAABs面板仍存在以下不足：第一，抗体的阳性判断值有待调整。目前的截断值是在155例肺癌患者和145例健康对照者中确定的，具有一定的选择偏倚；可以早期肺癌患者为主要对象，扩大样本量进一步研究；此外，可增加区分肿瘤浸润情况的判断界值，以便评估临床干预时机。第二，7-TAABs面板的受试对象主要是NSCLC患者，目前对SCLC的早期诊断研究较少，未来有望筛选出更多相关的自身抗体及其他液体活检标志物。
总之，肺癌相关自身抗体与CT人工智能在NSCLC早期诊断方面已展现巨大潜能，随着医学科研的不断推进，肺癌的早诊率将不断提高，肺癌患者的生存及预后情况有望改善。
基金
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  • 新乡市科技攻关计划项目(GG2019033)
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2024年第49卷第7期
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doi: 10.11855/j.issn.0577-7402.0994.2024.0104
  • 接收时间：2023-07-26
  • 首发时间：2025-11-21
  • 出版时间：2024-07-28
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  • 收稿日期：2023-07-26
  • 录用日期：2023-09-11
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Scientific and Technological Project of Xinxiang City(GG2019033)
新乡市科技攻关计划项目(GG2019033)
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    新乡医学院第五临床学院/新乡市第一人民医院呼吸与危重症医学科，河南新乡 453000

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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