广东省人民医院 乳腺科
中山大学孙逸仙纪念医院乳腺外科博士,中共党员,科研秘书,纪检委员
擅长外科为主导的乳腺癌多学科综合治疗
至今已发表SCI论文20余篇,累计总影响因子122.598,包括: EBioMedicine, Advanced Science,Ther Adv Med Oncol, Journal of Translational Medicine, Advanced Therapeutics等国际著名杂志
主持国家自然科学基金面上项目一项
主持国家自然科学基金青年项目一项
主持广东省人民医院科技专项(人才类)一项
主持广州市基础研究计划基础与应用基础研究项目一项
主持广东省医学科研基金项目一项
参与国家自然科学基金面上项目两项
参与广东省重点领域研发计划项目(新一代人工智能)一项
中国抗癌协会肿瘤标志专业委员会 委员
广东省抗癌协会肿瘤分子医学专业委员会 委员
广东省抗癌协会抗肿瘤药物专业委员会 委员
广东省抗癌协会肿瘤转移委员会委员 委员
广东省基层医药学会乳腺癌专业委员会 委员
一、前言
乳腺癌是女性最常见的恶性肿瘤,严重损害了广大女性人群的身心健康[1-3]。腋窝淋巴结转移(Axillary lymph node metastasis, ALNM)是乳腺癌最早且最常见的转移途径[4-7]。术前精准预测ALNM,对乳腺癌患者的临床-病理分期、治疗决策及生存预后具有重要临床意义[8-12]。腋窝分期愈高,乳腺癌患者的生存预后愈差[13-15]。目前,前哨淋巴结活检(Sentinel lymph node biopsy, SLNB)已代替腋窝淋巴结清扫(Axillary lymph node dissection, ALND),是早期乳腺癌腋窝淋巴结(Axillary lymph node, ALN)分期的金标准术式[16, 17]。前哨淋巴结(Sentinel lymph node, SLN)是指原发性乳腺癌发生ALNM必经的第一站ALN。然而,SLNB仍有一些术后并发症,包括:过敏反应、手臂麻木、伤口感染、皮下积液和血肿[18, 19]。美国肿瘤外科学会主席Armando Giuliano教授提出的著名治疗理念—“少即是多”(Less is more),指乳腺癌患者通过最小创伤性的手术,获得最佳的生存预后。同时,目前精准医学的大力推行下,乳腺外科手术越做越小,腋窝超声检查将有可能取代SLNB成为术前腋窝分期的重要评估方法[20]。2012年,意大利米兰–欧洲肿瘤中心发起SOUND研究(NCT 02167490),入组研究人群为肿瘤直径≤2 cm的接受保乳手术的乳腺癌患者,比较腋窝超声检查结果阴性的乳腺癌患者,在接受SLNB与未接受SLNB两组研究人群之间的远处转移是否有统计差异[21]。2015年,德国罗斯托克大学医学院Toralf Reimer教授牵头开展的INSEMA研究(NCT02466737),探索乳腺癌患者腋窝超声检查阴性是否可避免SLNB,比较接受SLNB与未接受SLNB两组DFS是否有统计差异。正在进行的SOUND研究和INSEMA研究,尚未公布研究结果,若为阳性结果,一定会改变乳腺癌腋窝手术的临床实践。但是,这两项研究结果仍在很大程度上取决于腋窝超声的诊断效能。
医学影像技术的高速发展,改善了乳腺癌ALN影像学的诊断效能,可术前检测到可疑ALN。目前,有各种检测手段来评估乳腺癌ALN状态,包括触诊、彩色多普勒B超、钼靶检查、磁共振MRI检查、CT检查、PET/CT检查等手段,但各种检查方法的各有优缺点。就目前而言,相比CT及PET/CT检查具有很强放射性和价格高昂,耗时费力的MRI检查,腋窝超声检查具有价格低廉、无辐射和操作简便的独特优势。因此,在临床实践中,腋窝超声检查广泛用于术前评估乳腺癌ALN状态[22-28]。腋窝超声检查可检测到ALN的形态、大小、横径、纵径、皮质厚度、髓质、淋巴结门、血流分布、可疑ALN数目及内部回声状态等参数[29, 30]。一方面,腋窝超声检查可有效地术前评估ALN状态,腋窝超声检查的灵敏度为25%–87%,特异度为77%–100%。另一方面,在腋窝超声引导下,对可疑ALN进行穿刺活检术,因此,约有8–28%乳腺癌患者可免于SLND,直接行ALND[31-33]。在一项纳入31个临床研究共4830例乳腺癌患者的荟萃分析中,Houssami等学者发现超声引导下ALN穿刺活检术的灵敏度为75%,特异度为98.5%,这使约20%乳腺癌患者免于SLND并直接行ALND[34]。但是,我们必须指出的是:尽管通过腋窝超声检查,可获得各种超声参数来评估ALN状态,但是,ALN超声参数繁多且诊断效能存在不同程度差异,很难形成广泛统一共识来有效地指导临床诊疗工作。
因此,如果我们可以尝试构建一个广泛可接受使用的精准实用工具来个体化术前预测ALNM,有助于临床医师做关于腋窝手术的临床决策。Nomogram预测模型作为一个实用预测工具,可通过纳入多种不同的重要风险因素,来建立方便使用的统计预测模型,可个体化预测临床事件[35]。本研究旨在基于超声–临床病理特征,构建一个术前ALNM预测模型,来个体化预测乳腺癌患者ALNM风险,帮助临床医师指导乳腺癌患者腋窝手术的最佳治疗决策, 服务于临床实践,满足于临床需求。
二、材料及方法
1.研究对象
本研究回顾性地收集了接受SLNB或ALND的原发性女性乳腺癌患者。建模组(N=1,024例),验证组(N=249例)来。本研究入组及排除标准如下(图1):
图1.入组及排除标准
2.研究方法
2.1 研究参数
本研究纳入临床病理及ALN超声变量, 包括:年龄、肿瘤大小(Tumor size)、病理类型、组织学分级、ER、PR、HER2、分子分型、淋巴结横径(Transverse diameter)、淋巴结纵径(Longitudinal diameter)、皮质厚度(Cortical thickness)、淋巴结门(Hilum)和ALN状态。
2.2 术前超声检查
一般情况下,正常ALN表现为形态规则,椭圆形,具有完整的包膜,边缘光滑,与周围组织边界清晰。对于转移性ALN,肿瘤细胞先侵犯并破坏淋巴结外层包膜,当肿瘤细胞逐渐增多,广泛浸润淋巴结内部时,会使其整体轮廓模糊,甚至消失不见。正常ALN的皮质厚度,在超声下呈均一低回声状态。若肿瘤细胞侵犯淋巴结皮质后,逐渐侵犯淋巴结副皮质、淋巴结门,随着肿瘤细胞在其中的聚集,可导致皮质局灶性或弥漫性不同程度的增厚。一般情况下,淋巴结门含有超声下高回声脂肪组织,当大量肿瘤细胞侵犯脂肪组织及髓质淋巴窦,可造成淋巴结门结构消失不见[36]。ALN解剖形态学特征表现可见图2。在本研究中,可疑ALN的超声形态学标准包括:i).ALN最厚部分的皮质厚度大于3 mm; ii).无高回声的淋巴结门[37-39]。
图2.腋窝淋巴结解剖
3. 预测模型的建立与验证
Nomogram预测模型的建立是基于多因素logistic回归分析结果。首先,在建模组对所有变量与ALNM的关系,做单因素logistic回归分析。然后,将单因素logistic回归分析中有意义变量(P<0.05)纳入多因素logistic回归分析中。同时,将多因素logistic回归分析中的变量(P<0.05)作为独立风险因素,构建Ultrasonographic–clinicopathologic Nomogram预测模型。Ultrasonographic–
clinicopathologic Nomogram预测模型建立后,需要严格验证。Ultrasonographic–clinicopathologic Nomogram预测模型的预测能力评价主要包括:1).鉴别能力(Discrimination ability):ROC曲线和ROC–AUC值; 2).校正能力(Calibration ability):Bootstrap重复抽样1000次建立校正曲线,通过拟合优度检验(Hosmer–Lemeshow goodness–of–fit test)来评价校正能力,主要用来比较临床事件实际概率和模型预测概率的情况,所得统计量卡方值越小,对应的P值越大,校准越好。3).风险分层能力(Risk stratification ability):根据Ultrasonographic–clinicopathologic Nomogram预测模型给予每一个患者评分,得出最佳临界值, 将研究人群进行风险分层。
4.统计学方法
采用单因素及多因素logistic回归分析,筛选出独立风险因素。预测模型评分最佳Cut-off值,以约登指数(Youden’s index)最大选取,约登指数=灵敏度+特异度–1,约登指数在0–1之间,Youden’s index越大,表明其准确性越高。本研究中以P值小于0.05表示差异具有统计学意义。统计软件采用Stata/MP, version 13.0 (StataCorp LP, College Station, TX, USA) and R (version 3.4.1; R Foundation for Statistical Computing, Vienna, Austria)。
三、结果
1.研究人群基线特征
本研究共纳入1,273例符合入组条件的原发性浸润性乳腺癌患者,包括建模组(N=1,024例)和验证组(N=249例)。全组研究人群的中位年龄为50岁(IQR: 24–86岁), 建模组研究人群的中位年龄为50岁(IQR: 24–86岁),验证组研究人群的中位年龄为48岁(IQR: 25–85岁)。正常及异常ALN的典型超声征像如图3所示。
图3.正常及异常腋窝淋巴结超声征像
2. 单因素及多因素logistic回归分析
在建模组单因素logistic回归分析结果表明: 肿瘤大小(Tumor size)、组织学分级(Histological grade)、病理类型(Pathological type)、淋巴结横径(Transverse diameter)、淋巴结纵径(Longitudinal diameter)、皮质厚度(Cortical thickness)及淋巴结门(Hilum status)均是乳腺癌ALNM的有意义风险因素。在建模组多因素logistic回归分析结果显示:肿瘤大小、组织学分级、淋巴结纵径、皮质厚度及淋巴结门均是乳腺癌ALNM独立风险因素。
3.预测模型的建立与验证
3.1Ultrasonographic-clinicopathologic Nomogram及Ultrasonographic Nomogram预测模型的建立
基于建模组多因素logistic回归分析结果,我们将5个独立风险因素(临床病理变量:肿瘤大小、组织学分级;超声变量:淋巴结纵径、皮质厚度及淋巴结门)构建Ultrasonographic–clinicopathologic Nomogram预测模型(图4)。因此,在Ultrasonographic–clinicopathologic Nomogram预测模型中,皮质厚度占权重最大,其次是肿瘤大小、淋巴结门、淋巴结纵径和组织学分级。
图4. 乳腺癌腋窝淋巴结转移预测模型
3.2Ultrasonographic-clinicopathologic Nomogram及Ultrasonographic Nomogram预测模型的验证
Ultrasonographic–clinicopathologic Nomogram及Ultrasonographic Nomogram预测模型建立后,进行严格验证。Ultrasonographic–clinicopathologic Nomogram预测模型在建模组及验证组的ROC–AUC值分别为0.873(95%CI: 0.851–0.896)和0.876(95%CI: 0.830–0.923),表明Ultrasonographic–clinicopathologic Nomogram预测模型有良好的鉴别能力(图5A–B)。Ultrasonographic Nomogram预测模型在建模组及验证组的ROC–AUC值分别为0.848(95%CI: 0.826–0.871)和0.845(95%CI: 0.798–0.893),表明Ultrasonographic Nomogram预测模型也有良好的鉴别能力(图5C–D)。Ultrasonographic–clinicopathologic Nomogram预测模型校正曲线显示了该模型预测概率与实际观测概率之间有很好的一致性,而且拟合优度检验(P=0.44>0.05),表明Ultrasonographic–clinicopathologic Nomogram预测模型有良好的校正能力(图5)。
图5.Ultrasonographic–clinicopathologic Nomogram及Ultrasonographic Nomogram
预测模型ROC曲线 图6.Ultrasonographic–clinicopathologic Nomogram预测模型校正曲线
4.Ultrasonographic–clinicopathologic Nomogram与Ultrasonographic Nomogram预测模型及其他预测因素的比较
Ultrasonographic–clinicopathologic Nomogram预测模型的构建主要基于5个临床病理超声变量,包括:肿瘤大小、组织学分级、淋巴结纵径、皮质厚度及淋巴结门。Ultrasonographic Nomogram预测模型的构建主要基于3个超声变量,包括:淋巴结纵径、皮质厚度及淋巴结门。因此,Ultrasonographic–clinicopathologic Nomogram预测模型和Ultrasonographic Nomogram的ROC–AUC值进行统计差异性分析(0.873 VS 0.848,P<0.0001),这个结果说明,与单独的超声变量相比,纳入超声变量和临床病理变量的Nomogram预测模型,表明Ultrasonographic–
clinicopathologic Nomogram预测模型的预测能力优于Ultrasonographic Nomogram预测模型。我们也将Ultrasonographic–clinicopathologic Nomogram预测模型的预测能力,与其他预测因素进行了比较(图7)。ROC–AUC值分别为Ultrasonographic–clinicopathologic Nomogram预测模型(0.873, 95%CI: 0.851–0.896),Ultrasonographic Nomogram预测模型(0.848,95%CI: 0.826–0.871),淋巴结纵径(0.634,95%CI: 0.612–0.656),淋巴结门状态(0.399,95%CI: 0.380–0.418),皮质厚度(0.831,95%CI: 0.8082–0.8541)。
图7. Ultrasonographic–clinicopathologic Nomogram预测模型与其他预测因素比较
因此,研究人群可分为两组,包括:低风险组(≤132),高风险组(>132)。在全组研究人群中,低高风险两组患者构成比分别为760 例(59.7%)和513例(40.3 %);建模组研究人群中,低高风险两组患者构成比分别为607例(59.3%)和417例(40.7%);验证组研究人群中,低高风险两组患者构成比分别为153例(61.4%)和96例(38.6%)。更重要的是,不管在建模组还是验证组研究人群中,低高风险两组患者的Ultrasonographic–clinicopathologic Nomogram预测模型风险评分,均具有统计学差异(P<0.0001)(图 8)。
图8. Ultrasonographic–clinicopathologic Nomogram预测模型评分高低风险组比较
四、讨 论
在本研究中,基于超声–临床病理特征,我们构建和验证了Ultrasonographic– clinicopathologic Nomogram预测模型来术前预测乳腺癌ALNM风险。Ultrasonographic–clinicopathologic Nomogram预测模型在建模组及验证组的ROC–AUC值分别为0.873(95%CI: 0.851–0.896)和0.876(95%CI: 0.830–0.923),表明Ultrasonographic–clinicopathologic Nomogram预测模型有良好的鉴别能力。Ultrasonographic–clinicopathologic Nomogram预测模型校正曲线显示,模型预测的ALN概率与实际ALN概率很好的一致性,并且预测模型的拟合优度非常好,表明该模型有良好的校正能力。根据Ultrasonographic–clinicopathologic Nomogram评分能合理有效地将乳腺癌患者ALNM风险分为高低2个风险组,说明该模型有良好的风险分层能力,为腋窝手术提供临床决策的证据参考。因此,临床医生及患者可利用我们构建的Ultrasonographic–clinicopathologic Nomogram预测模型,实现精准个体化术前评估乳腺癌患者的ALN状态,并具有较高的鉴别能力、校正能力及风险分层能力。
Ultrasonographic–clinicopathologic Nomogram预测模型包括五个重要参数:肿瘤大小、组织学分级、淋巴结纵径、皮质厚度和淋巴结门。肿瘤大小、淋巴结纵径、皮质厚度和淋巴结门,这四个变量可通过术前超声检查进行测量及评估;组织学分级,可通过术前乳腺肿物穿刺活检病理获得。皮质厚度是超声评估ALN状态的重要指标[29, 30, 39-41]。但是,皮质厚度的最佳临界值未达到统一共识,尚存在争议,有待进一步探索。Moore等学者通过收集268例ALN的临床研究揭示了,当皮质厚度的最佳临界值为2.3 mm时,腋窝超声的灵敏度为95%,特异度为44%[41]。Mainiero等学者在收集226例ALN的临床研究中,确定将3 mm作为皮质厚度的最佳临界值,发现腋窝超声灵敏度为88%,特异度为75%[42]。Amonkar等学者在收集439例ALN的临床研究中,分别将2 mm、2.3 mm、3 mm作为皮质厚度的最佳临界值,比较三种情况下灵敏度与特异度,结果发现3 mm可获得最好的灵敏度与特异度[43]。Cho等学者在收集191例ALN的临床研究中也认为皮质厚度大于3 mm是ALNM最可靠的特征指标[23]。Abe等学者在收集559例ALN的临床研究中认为淋巴结门缺失是ALNM最强特征性指标,特异性达100%,但其敏感性偏低[29]。Lee等学者在收集224例ALN的临床研究中揭示,皮质增厚与淋巴结门缺失是ALNM的非常重要预测指标[38]。既往也有研究表明,肿瘤大小是乳腺癌ALNM的相关变量,乳腺肿瘤越大,乳腺癌ALNM风险就越高[44]。这些与我们的研究结果都是一致的。
ALN状态是乳腺癌重要的预后风险因素。据我们所知,大多数既往研究中已建立的乳腺癌ALN预测模型都不完美,ROC–AUC范围为0.702–0.79[45-48]。首先,多项既往研究表明:年龄、肿瘤位置、肿瘤大小、肿瘤类型、组织学分级、多灶性、ER和PR表达为乳腺癌ALNM的独立风险因素[45, 49-52]。然而,需要指出的是病理肿瘤大小、多灶性及淋巴血管侵犯等参数,在术前无法准确获得[51, 53]。因此,在临床实践中,这些独立风险因素的实际效能不能帮助临床医生术前评估乳腺癌ALN状态。例如,MSKCC Bevilacqua教授等共收集5,331例乳腺癌患者(建模组3,786例和验证组1,545例)率先构建ALN预测工具(MSKCC Nomogram),纳入9个临床病理变量,以术前预测乳腺癌患者ALNM风险[46]。然而,MSKCC Nomogram预测模型在不同人群中的验证结果差异较大,这可能原因是乳腺癌种族人群差异导致的[54-59]。同时,必须指出的是MSKCC Nomogram预测模型纳入的变量,如:脉管癌栓、多灶性等,在术前无法准确获得,因此,MSKCC Nomogram预测模型的实用效能是有限的。芬兰大学中心医院的Meretoja教授等通过入组1,395例术前腋窝超声阴性的乳腺癌患者,筛选出四个独立风险因素,包括:病理肿瘤大小、多灶性、脉管癌栓和肿瘤可触及性,构建了一个统计公式评估乳腺癌ALNM风险[45]。该芬兰模型尽管考虑到腋窝超声检查的作用,但是并未纳入ALN超声参数。其次,该研究中缺乏验证组,未将该芬兰模型进行外部验证。再者,该芬兰模型是通过复杂的函数统计公式进行展示,不方便临床上使用。而且,该芬兰模型中的病理肿瘤大小、多灶性、脉管癌栓,都是术后病理变量,不能在术前准确获得,因此,该模型无法在临床上实际运用。既往有中国学者开展多中心回顾性研究,共入组4,211例乳腺癌患者(建模组3,158例和验证组1,053例),通过纳入年龄、肿瘤大小、肿瘤位置、病理类型和分子分型等变量,构建乳腺癌腋窝淋巴结预测模型。但是,该模型仅仅考虑到临床病理变量的预测价值,未提及腋窝超声检查的重要作用,而且ROC–AUC值不高,具有一定的局限性。有中国学者通过12对乳腺癌ALNM与ALN未转移的组织芯片分析,发现ALNM相关的9个差异miRNA,共收集804例早期乳腺癌患者,再整合年龄、肿瘤大小、肿瘤位置、腋窝超声ALN有无转移和miRNA等变量,构建了早期乳腺癌患者ALNM风险模型[60]。尽管,该模型考虑到ALNM相关miRNA的预测价值,但是未充分利用腋窝ALN超声参数的重要预测作用。既往研究已表明腋窝超声检查是一种在术前评估乳腺癌ALN状态的重要方法[20, 29, 30, 33, 45, 61]。同时,腋窝超声检查可以提供ALN的大小,形状和皮质形态等,这些参数与乳腺癌ALNM风险相关。因此,为了提高预测模型的预测效能,我们在临床病理特征的基础上,纳入ALN超声参数,构建Ultrasonographic–clinicopathologic Nomogram预测模型来预测乳腺癌ALNM风险。最终,与Ultrasonographic– clinicopathologic Nomogram相比较,既往研究中已建立的乳腺癌ALNM预测模型的ROC–AUC值仍有待提高[45, 47, 51]。基于本研究构建的Ultrasonographic–clinicopathologic Nomogram预测模型,可实现精准个体化术前评估乳腺癌患者ALN状态,有助于临床医生做出最佳临床治疗策略。同时,在展开临床研究设计时,利用Ultrasonographic–clinicopathologic Nomogram预测模型评分可实现有效的风险分层分析,合理分配研究队列人群进行分层分组。而且,利用Ultrasonographic–clinicopathologic Nomogram预测模型,获得精准个体化乳腺癌ALNM风险,有利于医患之间进行良好的疾病沟通。
五、结 论
总之,基于超声临床病理特征,我们构建了一个Ultrasonographic– clinicopathologic Nomogram预测模型,以精准个体化术前预测乳腺癌ALNM风险。因此,运用Ultrasonographic–clinicopathologic Nomogram预测模型,可术前鉴别出乳腺癌ALNM的低风险组患者,并帮助临床医师做出腋窝手术的最佳治疗决策,服务于临床实践,满足于临床需求。
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