修回日期: 2025-05-12
接受日期: 2025-06-04
在线出版日期: 2025-06-28
肝细胞癌(hepatocellular carcinoma, HCC)是全世界第三大癌症相关死亡原因, 50%的HCC患者诊断时已经是晚期, 不适合接受根治性治疗. 对晚期HCC患者的系统治疗成为提高生存期的重要方法, 是临床关注的重要领域. 抗血管内皮生长因子(anti-vascular endothelial growth factor)药物联合免疫检查点抑制剂是晚期HCC患者的一线疗法. 尽管联合治疗的效果优于其他治疗手段, 但大部分患者的临床获益有限. 目前尚缺乏准确评估靶向联合免疫治疗反应的可靠指标. 影像学检查作为HCC研究中的关键方法, 有望为疗效评估提供有效的预测指标, 从而实施个体化治疗.
核心提要: 基于不同影像学检查的定量分析能够无创预测及评价肝细胞癌(hepatocellular carcinoma, HCC)患者靶向联合免疫治疗效果, 增强影像学检查可反应HCC的血流灌注变化情况, 联合患者临床特征与人工智能技术建立模型进一步提高了影像预测疗效的能力.
引文著录: 周丽萍, 杨薇. 影像预测及评价肝细胞癌靶向联合免疫治疗效果的应用现状. 世界华人消化杂志 2025; 33(6): 441-447
Revised: May 12, 2025
Accepted: June 4, 2025
Published online: June 28, 2025
Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide. Approximately 50% of patients with HCC are diagnosed at advanced stages, making them ineligible for curative treatments. Systemic therapy for advanced HCC has emerged as a critical approach to improving survival outcomes and remains an important field of clinical practice. Among systemic therapies, anti-vascular endothelial growth factor agents and immune checkpoint inhibitors are the first-line treatments for unresectable HCC. Although combined therapy is better than other treatments, most patients still have limited clinical benefits. Unfortunately, there are no reliable indicators to predict the efficacy of combined therapy. Imaging examination, as the crucial tool of HCC research, is expected to provide a new predictive method for therapeutic response to facilitate individualized treatment.
- Citation: Zhou LP, Yang W. Application of imaging modalities in predicting and evaluating response to anti-vascular endothelial growth factor agents and immune checkpoint inhibitors in patients with hepatocellular carcinoma. Shijie Huaren Xiaohua Zazhi 2025; 33(6): 441-447
- URL: https://www.wjgnet.com/1009-3079/full/v33/i6/441.htm
- DOI: https://dx.doi.org/10.11569/wcjd.v33.i6.441
肝细胞癌(hepatocellular carcinoma, HCC)是原发性肝癌最常见的类型(75%-85%), 是全球第六大常见恶性肿瘤和第三大癌症相关死亡原因[1]. 由于HCC发病隐匿且症状进展缓慢, 几乎有50%的HCC患者诊断时已经是晚期, 不适合接受根治性治疗, 如手术切除、肝移植或消融等[2], 使得系统抗肿瘤治疗的需求与日俱增. 目前, 抗血管内皮生长因子(anti-vascular endothelial growth factor, anti-VEGF)药物和免疫检查点抑制剂(immune checkpoint inhibitors, ICIs)联合应用成为不可切除HCC的一线新标准治疗方法[3-5], 已经有多项证据表明联合治疗控制HCC的有效性[6-9]. 但有研究显示不同患者对靶免联合治疗的疾病缓解情况和预后差异很大[10,11], 不可避免的不良反应和治疗高成本导致一些患者无法从中受益. 若能在患者开始联合治疗前确定预测治疗反应的因素, 会对指导临床医师及时调整优化个体治疗方案至关重要. 然而, 现阶段尚未发现合适的方法来准确预测靶免联合治疗效果. 因此, 有必要探讨有效的预测联合治疗反应的方法来识别潜在获益人群. 影像检查手段因其经济、无创、敏感度高, 在HCC的诊断、治疗、疗效预测中发挥着重要作用[12], 笔者就不同影像手段在HCC靶免联合治疗疗效评估中的应用现状及研究进展综述如下.
Anti-VEGF药物包括多靶点酪氨酸激酶抑制剂(tyrosine kinase inhibitors, TKI)和单克隆抗体. 索拉非尼是美国食品药品监督管理局(the United States Food and Drug Administration, US FDA)批准的第一个用于治疗不可切除HCC的TKI[13], 随后乐伐替尼、瑞格拉非尼、卡博替尼和雷莫芦单抗也被批准作为不可切除的HCC的一线或二线治疗药物[14-17]. 程序性细胞死亡1(programmed cell death protein 1, PD-1)或程序性细胞死亡配体1(programmed death-ligand 1, PDL-1)的ICIs包括阿替利珠单抗、纳武利尤单抗、卡瑞利珠单抗等[18-20]. 据报道[6,8,21], anti-VEGF药物和ICIs联合会产生协同作用对抗肿瘤, anti-VEGF药物有助于诱导肿瘤血管的正常化, 减轻肿瘤微环境的免疫抑制, 增加免疫细胞的浸润和活化, 同时PD-1/PDL-1抑制剂可通过靶向免疫检查点来增强免疫细胞的刺激. 已有多项研究证实联合治疗的效果优于单药治疗[22,23], Imbrave150 Ⅲ期临床试验报告显示, anti-VEGF药物联合ICIs治疗的总生存期为19.2 mo, 中位无进展生存期(progression-free survival, PFS)为6.9 mo[24], 2020年, US FDA批准联合治疗策略作为晚期HCC的一线治疗. 尽管联合治疗延长了患者的生存期, 但有研究称, 患者对治疗的反应不同, 部分患者存在原发性或继发性耐药和严重的不良反应, 因此早期评估患者是否适合联合治疗至关重要.
超声是监测HCC患者治疗反应最常用的检查方法之一[25], 超声造影(contrast enhanced ultrasonography, CEUS)能够揭示HCC的血流灌注变化情况并进行定量分析, 在早期评价或预测各种类型肿瘤对抗血管生成治疗的反应领域备受认可[26]. Takada等[27] 2023年报道28例HCC患者在治疗前后的CEUS时间强度曲线(time-intensity curve, TIC)分析, 发现治疗后造影剂上升支斜率减小、3-7 d达峰时间延长与早期更好的肿瘤缓解有关(P = 0.049、0.002), 不过研究中大部分患者的既往系统化疗经历可能干扰靶免联合治疗的效果. Uchikawa等[28]的研究对30例患者进行CEUS检查, 分析结果显示治疗五周后TIC曲线下面积受试者工作特征曲线及曲线下面积(area under the curve, AUC)<61.3与更长的PFS有关(P = 0.039), 然而治疗缓解组与非缓解组治疗前后AUC值的变化却没有显著差异, 可能受到部分患者既往接受过其他治疗的影响. 由于临床一定数量的患者缺乏治疗前后的影像资料对比, 研究者们进行了HCC患者靶免治疗前的CEUS研究, 分析其特征是否能够提前预测疗效. Sun等[29]考虑到TIC曲线的定量参数可能受到肝脏背景或图像深度的影响, 提出使用肿瘤区域(tumor region, TR)与瘤周区域(peritumoral region, PTR)定量参数的比值进行更客观的比较, 分别分析33例HCC患者联合治疗前的TIC-TR和TIC-PTR, 发现门静脉内无癌栓、较低的造影剂上升支斜率比与更好的治疗反应有关(P = 0.008、0.023). 基于门静脉癌栓和造影剂上升支斜率比这两个因素建立的诺模图效能良好, AUC为0.909, 经500次重复的bootstrapping验证后, 模型的AUC为0.91. 本中心Liang等[30]基于62例HCC患者的超声特征及临床特征建立了三种预测模型: 二维超声+彩色多普勒血流显像、CEUS和FULL模型, C指数分别为0.918、0.920和0.973, 综合病灶数量、血流信号、晕征、造影剂廓清时间(67 s为截断值)的FULL模型的预测性能最高, 证明了多模式超声模型联合应用预测疾病治疗效果的潜力. 以上研究表明通过CEUS实时显示肿瘤血管变化情况可为临床预测治疗反应提供重要参考, 联合治疗后肿瘤血流减少是预后良好的标志.
不仅常规超声及CEUS有助于治疗效果的早期评估, 另有研究者发现剪切波弹性成像(shear wave elastography, SWE)也能很好地反应治疗效果. Yuan等[31]的多中心研究通过SWE测量133例HCC患者靶免治疗前的基线肿瘤硬度值, 发现基线肿瘤硬度值>19.53 kPa、白蛋白-胆红素分级(albumin-bilirubin grade, ALBI)(2级)与更高的疾病进展率有关(P<0.001、<0.001), 联合肿瘤硬度值和ALBI分级建立诺模图, 训练组和验证组中C指数分别为0.828, 0.827, 由此说明肝硬度值可作为HCC患者联合治疗效果的预测指标.
CT是一种广泛用于HCC患者的诊断和随访疗效评价的技术, 无论是CT平扫或是增强都可以提供丰富的参数信息. 研究表明[32], CT定量参数与肿瘤反应之间存在相关性, 参数定量分析可用于评价肿瘤缓解. Awiwi等[33]回顾性分析55例HCC患者治疗前的CT及临床信息, 发现平扫肿瘤CT值衰减<40 HU、胸腔积液、腹水、肝静脉癌栓与更差的总生存期有关(P = 0.007、0.003、0.008), 这种关系在既往接受过治疗的患者中更为明显. Feng等[34]探究117例HCC患者治疗前不同期相肿瘤相对CT值衰减指数(relative Hounsfield unit attenuation index, rHUAI)(rHUAI = rHU/各时相时间差, rHU = HU肿瘤/HU肝)对靶免疗效的预测价值, 训练组中发现较低的rHU-ap(动脉期到门静脉期)和较高的rHU-pd(门静脉期到延迟期)与更好的肿瘤缓解相关(P<0.01, P<0.05), 这可能由于靶免联合治疗后使肿瘤细胞异常动脉供血减少, 门静脉供血增加, rHU-ap越大, 门静脉血供越丰富; rHU-pd越大, 门静脉流速越慢. 由此导致血液可以在肿瘤微环境中停留更长的时间, 增加T细胞迁移到肿瘤基质的机会, 提高抗肿瘤免疫效果. 验证组证实了训练组中rHU-ap与肿瘤反应之间的关联(P<0.05), 但发现rHU-pd与肿瘤反应的关联不稳定(P = 0.001至0.134). 上述研究说明CT值衰减预测治疗反应的应用价值需要进一步验证.
肝细胞癌在增强CT图像上具有其特征性的影像学表现: 动脉期高增强(arterial-phase hyperenhancement, APHE)和门静脉期或延迟期快速廓清[12], 因此可通过增强CT图像上HCC的治疗前强化程度估测治疗效果. Choi等[35]对108例患者的多期CT图像资料进行研究, 发现治疗前有肝外转移、肿瘤呈浸润性外观、不存在APHE的患者预后更差(P = 0.03、0.04、<0.001), 列线图的c指数为0.78, 这可能是因为低分化HCC缺乏分化成熟的血管. Kuwano等[36]也曾报道过存在边缘APHE患者的治疗效果更好. 但这种方法对不典型HCC的疗效预测存在一定的局限性.
核磁共振成像(magnetic resonance imaging, MRI)扫描软组织分辨力高, 具有多方位多序列多参数成像的特点, 对肝脏疾病的诊断和局部或全身治疗后肿瘤反应的监测具有重要意义[37]. Sheng等[38]通过分析37例HCC患者治疗前的多序列MRI图像发现: 无或不完全的肿瘤包膜、T2加权成像(T2-weighted imaging, T2WI)信号不均匀和动脉期病变肿瘤-肝脏对比噪声比(contrast-to-noise ratio, CNR)≤95.45[CNR = (肿瘤信号强度-肝脏信号强度)/噪声标准差]是疾病进展的独立危险因素(P = 0.002、0.008、0.008), 合并三个独立影像特征构建的诺模图C指数是0.880, 包膜不完整可能导致肿瘤细胞扩散, T2WI信号不均匀可能提示肿瘤内存在坏死、出血等多种成分, 动脉期低CNR代表肿瘤血供不足可能会削弱拮抗作用和肿瘤反应. 2023年美国肝病研究学会肝细胞癌实践指南中强调了钆塞酸二钠(gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid, Gd-EOB-DTPA)增强MRI对HCC研究的重要性[12], 该成像技术具有较高的敏感性和诊断准确性[39]. Gd-EOB-DTPA是一种肝胆特异性造影剂, 静脉注射后可以通过存在于肝血窦肝细胞膜上的有机阴离子转运多肽1B 3(organic anion transporting polypeptide 1B3, OATP1B3)被正常肝细胞吸收, 然后通过肝膜胆道上的多药耐药相关蛋白2排泄到胆道中, 这个特异性的时期被称为肝胆特异期或肝胆期[40]. 因此在肝胆期, 正常肝细胞不断摄取Gd-EOB-DTPA在T1加权成像(T1-weighted imaging, T1WI)上呈高信号, 相反, 由于HCC细胞OATP1B3表达水平降低, 不能特异性摄取Gd-EOB-DTPA, 而常显示为低信号[41]. Sasaki等[42]对68例晚期HCC患者治疗前肝胆期最大HCC病灶进行视觉评估和相对增强比(relative enhancement ratio, RER)的测量[RER = (肝胆期病灶信号强度/肝胆期肝实质信号强度)/(平扫时病灶信号强度/平扫时肝实质信号强度)], 得出结论肝胆期肿瘤不均匀型和高信号(RER≥0.9)与更短的无进展生存期有关(P = 0.07, P = 0.012). Kwon等[43]的研究验证了治疗前肝胆期的结节强化程度可无创预测靶向联合免疫治疗效果这一观点. 但同样使用目测方法判断肿瘤的均匀性, Kwon等[43]人却未发现肝胆期肿瘤均匀程度与治疗缓解有关, 除去视觉评估的主观性可能造成这种影响, 研究病灶数目不同也可能是结论不一致的原因, Sasaki等[42]仅选择1个最大的病灶分析, 而Kwon等[43]选择2个最大的病灶进行研究.
目前大多数靶免治疗的影像学研究聚焦于探索预测治疗反应的相关因素, 基于MRI治疗前后检查对比评价治疗效果的研究尚少. 裴豆豆等[44]对81例HCC患者在治疗前后进行多模态MRI扫描[T1WI、T2WI和弥散加权成像(diffusion weighted imaging, DWI)]并定量分析, 对比不同疗效组T1、T2、表观弥散系数(apparent diffusion coefficient, ADC)参数的差异, 发现T1水平降低, T2、ADC水平升高与更好的治疗效果有关(P<0.05), 其中的原因可能是靶免联合治疗后肿瘤细胞缺血、坏死, 组织发生水肿, 从而表现为T1水平降低, T2水平升高; DWI反映组织中水分子的弥散情况, 联合治疗后肿瘤缩小水分子弥散自由, DWI图像上表现为ADC值升高, 但本研究仅观察这三个定量参数对治疗结果的评估能力, 后续可考虑纳入更多参数进行研究.
人工智能的快速发展促进了影像数据的高效处理, 近年来以深度学习为代表的人工智能技术取得了显著的发展, 其应用范围已经扩大到预测一系列疾病治疗效果[45]. 深度学习是将复杂的多层神经网络体系结构通过输入信息转换为多个抽象级别来自动学习数据[46]. HCC的临床诊断和治疗涉及多种复杂的数据模态, 深度学习的出现为从复杂数据中提取临床相关信息提供了新的可能[47]. Liao等[48]将120例HCC患者联合治疗前的CT图像(包括平扫、动脉期和门静脉期)输入ResNet-18基线模型, 分别构建单相、双相和多相深度学习模型, 经比较多相模型的预测性能最高, AUC值为0.802, 疗效预测的准确性、敏感性、特异性分别为0.725、0.829、0.803, 并发现静脉期是提高模型性能的关键(AUC = 0.719). CT与深度学习模型相结合使肿瘤复杂空间变异性的识别成为可能, 极大提升了预测结果的可靠性.
不仅可以借助治疗前的CT资料进行疗效预测, 还可通过治疗前后资料对比进行预后评估. Mohseni等[49]对40例患者治疗前后CT图像上肿瘤体积参数进行比较分析, 发现肿瘤活性体积百分比(%Viability = 肿瘤存活体积/肿瘤总体积x100%)和肿瘤活性体积降低百分比(%Viability reduction)是影响患者生存的独立预测因子(P = 0.001, P = 0.004). 研究者们结合这两个参数提出一个新的预后分层模型(quantitative response evaluation criteria in solid tumors, qRECIST), 将患者分为三个预后组: 预后较好组(治疗前基线肿瘤活性>69%且活性降低≥8%)的总生存期最长, 预后较差组(治疗前基线肿瘤活性≤69%且活性降低<8%)的总生存期最短, 其余不符合预后较好或较差的患者被归类为中等预后组. 虽然qRECIST为临床实践中的风险分层和治疗决策提供了一种新颖实用的方法, 弥补了通过二维测量肿瘤大小评估治疗反应的局限性, 但未来仍需要与mRECIST和iRECIST标准进行性能比较.
影像组学是一种新技术, 可以从医学图像中提取定量特征并将其转化为可挖掘的数据[50]. 基于人工智能的影像组学在HCC患者的临床决策中发挥着越来越重要的作用, 为HCC的预测、诊断和预后提供了新的技术保障[51]. 李瑞峰等[52]分析148例HCC患者治疗前临床及增强MRI资料, 发现了5个与疗效不佳相关的变量: 碱性磷酸酶<125.98 U/L、BCLC B2期、肿瘤位置(肝右叶)、肿瘤大小≥3.19 cm、动脉期强化(部分强化), 基于以上因素分别建立临床-放射学模型、影像组学模型以及联合模型, 经比较影像组学模型的预测效能优于临床放射模型(训练集: AUC, 0.863 vs 0.773; 验证集: AUC, 0.871 vs 0.770), 联合模型的预测效能最佳(训练集: AUC = 0.897; 验证集: AUC = 0.888). 姚致远等[53]基于191例HCC患者治疗前MRI资料进行影像组学分析, 发现基于K-近邻、朴素贝叶斯和支持向量机算法建立的模型表现出良好的效能, 验证组AUC值分别为0.830、0.836、0.825. 但这些模型的敏感性和阳性预测值偏低, 提示其可能对排除靶向免疫治疗不敏感的HCC患者更有价值. 多参数MRI为疗效评估提供了更全面的信息, 肝胆特异性造影剂对正常肝细胞和病变细胞的信号差异显示极其敏感, 组学的出现更是显著提升了疗效预测的准确性.
准确有效地预测晚期HCC患者靶向联合免疫治疗反应具有重要的临床指导意义. 超声、CT、MRI检查为疗效评估提供了丰富的非侵入性的预测方法, 影像组学与机器学习算法的整合完善了肿瘤空间特征的提取与分析, 进一步提高了影像预测疗效的能力. 然而, 不同研究中患者的临床特征不同, 设备型号及参数、检查方式、造影剂种类、治疗方案难以实现标准化, 评估分析时医师主观因素的影响也会对研究结果造成干扰, 从而限制了现有研究结果外推性. 虽然缺少标准化协议, 但大部分研究者提出的诊断标准都是常见且通用的, 因此不会显著影响诊断结果的整体准确率. 此外, 由于靶向联合免疫治疗广泛应用于临床的年限较短, 目前大多数研究为早期疗效评估, 随访时间短, 并且多为小样本、单中心的回顾性分析, 影像特征评估长期预后的应用价值仍需在大样本、多中心的前瞻性研究中验证. 超声、CT、MRI在预测疗效方面具有各自的优势与不足, 综合应用有助于提升预测的准确性. 相信随着影像检查技术及人工智能的不断融合发展, 临床应用时间的积累, 更多高级别循证医学研究的报道会为疗效评估提供更多准确有价值的信息.
学科分类: 胃肠病学和肝病学
手稿来源地: 北京市
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