CN111539946A - 一种识别表现为磨玻璃结节的早期肺腺癌的方法 - Google Patents
一种识别表现为磨玻璃结节的早期肺腺癌的方法 Download PDFInfo
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Abstract
本发明涉及肺部磨玻璃结节的图像分析技术领域,具体涉及一种识别表现为磨玻璃结节的早期肺腺癌的方法,其包括病史采集、图像采集、图像分析、病例随访、资料收集、统计学分析以及建立预测模型七个步骤。本发明,采用多因素Logistic回归方法建立预测模型,并根据最小赤池信息标准选择最佳的模型参数,绘制预测模型的列线图以及校准曲线应用受试者工作特征曲线评估模型预测效能,在国内率先构建专用于磨玻璃结节且包含PET代谢参数的多因素预测模型,用以评估磨玻璃结节的良恶性。根据前期研究结果,对于可疑磨玻璃结节术前检查患者,基于PET/CT显像构建的预测模型预测效能良好(AUC为0.875),特异度高(0.923),可降低磨玻璃结节的错误识别率,避免不必要的外科手术,改善高危磨玻璃结节的术前预测。
Description
技术领域
本发明涉及肺部磨玻璃结节的图像分析技术领域,更具体地说,它涉及一种识别表现为磨玻璃结节的早期肺腺癌的方法。
背景技术
随着低剂量CT在肺癌筛查中广泛应用,肺磨玻璃结节的检出率逐年增加,磨玻璃结节常见于早期肺腺癌,但也可由炎症、间质性纤维化或局部出血所致,如何有效鉴别结节的良恶性至关重要。支气管镜或经皮肺穿刺活检术由于取材困难,成功率较低且为有创检查,临床应用存在局限性,影像学检查仍是鉴别磨玻璃结节的主要手段。指南推荐对于磨玻璃结节可根据初筛时结节的大小进行CT随访,依据磨玻璃结节大小、实性成分的变化等来鉴别良恶性,如为炎性病变可在随访期间缩小或消失,部分磨玻璃结节最长需随访5年。长期的CT随访使患者遭受反复的辐射暴露和巨大的心里压力,部分患者难以接受,因此,需要更准确有效的检查技术来识别表现为磨玻璃结节的早期肺腺癌
有关CT特征的单因素分析显示分叶、毛刺、胸膜凹陷、空泡征、血管集束征、结节大小、实性成分大小、肿瘤倍增时间等指标对鉴别磨玻璃结节良恶性有一定价值,但单一指标预测效能一般,灵敏度及特异度不高。多因素预测模型的构建有望改善磨玻璃结节的鉴别效能。目前有关肺结节良恶性鉴别的预测模型主要有Mayo模型、Brock模型、Herder模型及BIMC模型等。Mayo模型数据主要来源于X线或厚层CT检查,对于磨玻璃结节可能存在错误识别。Brock模型参数较多,应用复杂,且其建模数据来源于恶性率低(5.5%)的初筛患者,对于术前高危结节的鉴别准确性不高。Herder模型及BIMC模型是国内外较为公认的含有PET代谢信息的预测模型,但二者均是建立在实性肺结节的基础上,多项研究表明实性肺结节与磨玻璃结节在临床表现、生物学特征及预后等方面均有较大差异,因此两个模型均不适用于磨玻璃结节的良恶性预测。
发明内容
针对现有技术存在的不足,本发明的目的在于提供一种识别表现为磨玻璃结节的早期肺腺癌的方法,其针对磨玻璃结节,构建基于PET/CT技术的多因素预测模型,经验证该模型简单可行,稳定性好,且模型兼顾了患者临床特征、PET代谢信息及CT图像参数,有效提高了表现为磨玻璃结节的早期肺腺癌的识别效能,为临床提供指导思路,以解决上述背景技术中提出的问题。
为实现上述目的,本发明提供了如下技术方案:
一种识别表现为磨玻璃结节的早期肺腺癌的方法,包括如下步骤:
步骤一:病史采集
首先对因磨玻璃结节预行PET/CT检查的受检者问诊,采集病史资料内容包括:患者年龄、性别、吸烟史、近期外周血肿瘤标志物、肿瘤病史、是否伴随严重肝病或糖尿病可能影响PET/CT半定量指标的疾病;
步骤二:图像采集
采用德国西门子公司Biograph mCT 64型PET/CT显像仪进行图像扫描,显像剂18F-FDG,放化纯度>95%,受检者空腹>4小时,血糖≤10mmol/L,静脉注射18F-FDG,且18F-FDG的剂量为3.70-7.77MBq/kg,1h后行全身PET/CT显像;
采集体位:仰卧位,双手抱头;范围:头部到大腿中部,先行低剂量定位CT扫描,再行全身PET采集,时间为2min/床位,采用TureD软件(Siemens)进行图像重建,将肺窗的窗宽设置为1200 HU、窗位设置为-600 HU以及将纵隔窗的窗宽设置为350 HU、窗位设置为40 HU,PET/CT显像后,即刻行同机屏气下肺部HRCT扫描,采集及重建条件如下:管电压140kV,管电流依据人体解剖结构和组织密度自动调整,旋转时间0.5秒,螺距0.6,层厚1.0mm,层间隔0.5mm,矩阵512×512,肺窗的窗宽1200HU、窗位-600 HU,纵隔窗的窗宽350 HU、窗位40HU;
步骤三:图像分析
由两位经验丰富的核医学医师独立阅片,获取以下参数:
SUVmax:选择圆形ROI在结节部位逐层测量,记录最大值,且确保磨玻璃结节被完全包括在内;
肝脏SUVmean:选择直径60mm圆形ROI在肝右叶进行测量,尽量避开显像剂摄取不均匀部位;
SUV指数:即SUVmax与SUVmean之比;
结节类型:纯磨玻璃结节或混合性磨玻璃结节;
结节位置:外周或中央;
结节数量:单发或多发;
结节形状:类圆形或不规则形;
结节边缘:光滑或分叶;
异常支气管征、空泡征、胸膜凹陷及血管集束征;
结节直径:即肺窗横断面结节最大长径;
结节内实性成分直径:即肺窗横断面实性成分最大长径;
实性成分占比:即结节内实性成分直径与结节直径之比;
磨玻璃成分CT平均值:选择3个不同CT层面分别测量结节磨玻璃成分的平均CT值,再计算其平均值,测量时尽量避开结节内的血管、支气管、空泡及实性成分;
所有测量值取两名医师记录的平均值,意见不统一时与第三位观察者共同商讨确定;
步骤四:病例随访
PET/CT检查后一月内随访受检者结局,通过查阅本院电子病例、CT随访图像、电话询问方式获取患者术后病理结果或病灶动态变化;
步骤五:资料收集
按要求筛选符合标准的磨玻璃结节患者资料;
步骤六:统计学分析
采用R进行统计学分析,版本3.4.3(http://www.R-project.org;软件包:glmnet,pROC,rms),连续变量服从正态分布时以均数±标准差表示,非正态分布时以P50(P25、P75)表示,分类变量以频率(%)表示,组间比较时连续变量采用非配对的Student-t检验或Mann-Whitney U非参数检验,分类变量采用Pearson卡方检验或Fisher精确检验;
步骤七:建立预测模型
采用多因素Logistic回归方法建立预测模型,并根据最小赤池信息标准(minimalAkaike’s information criterion,AIC)选择最佳的模型参数,计算OR和95% CI;绘制预测模型的列线图,该列线图可以直观地显示每个磨玻璃结节的预测结果,同时绘制校准曲线以显示列线图的预测精度,以及绘制预测模型的受试者工作特征(Receiver operatingcharacteristic,ROC)曲线,并获得曲线下面积(AUC)及其95% CI,再应用z统计量比较建模组和验证组的AUC,所有统计检验均为双侧检验,以P<0.05被认为具有统计学差异。
进一步的,所述资料收集的筛选纳入标准为:同时接受PET/CT及HRCT检查的所有患者;病灶经手术切除或HRCT随访病灶明显缩小或消失、病灶最大直径≤30mm、恶性磨玻璃结节均为病理证实为IA期以内的患者(按第8版肺癌TNM分期标准);
所述资料收集的筛选排除标准为:PET/CT及HRCT图像质量差或病灶的直径难以测量;5年内有恶性肿瘤病史者;有严重肝病或糖尿病患者。
进一步的,所述外周血肿瘤标记物包括CEA、CYFRA21-1、CA199、NSE、SCCAg。
进一步的,在所述预测模型中,患者性别、结节位置、边缘、胸膜凹陷征及SUV指数共5个指标均为预测磨玻璃结节良恶性的独立因素。
综上所述,本发明主要具有以下有益效果:
本发明,获取患者的一般临床资料、术后病理、外周血肿瘤标记物、HRCT及PET图像参数,采用多因素Logistic回归方法建立预测模型,并根据最小赤池信息标准(minimalAkaike’s information criterion,AIC)选择最佳的模型参数,绘制预测模型的列线图,该图可以直观地显示每个磨玻璃结节的预测结果,此外,绘制了校准曲线以显示列线图的预测精度,应用ROC曲线评估模型预测效能,在国内率先构建专用于磨玻璃结节且包含PET代谢参数的多因素预测模型,用以评估磨玻璃结节的恶性概率。根据前期研究结论,对于可疑磨玻璃结节术前检查患者,基于PET/CT显像构建的预测模型预测效能良好(AUC为0.875),特异度高(0.923),可减少磨玻璃结节的错误识别率,避免不必要的外科手术,改善高危磨玻璃结节的术前预测。
附图说明
图1为一种实施方式的识别表现为磨玻璃结节的早期肺腺癌的方法的流程示意图;
图2为基于实施例1中患者性别、结节位置、边缘、胸膜凹陷征及SUV指数5个指标的列线图;
图3为图像分析使用的PET/CT图片。
具体实施方式
以下结合附图1-3对本发明作进一步详细说明。
实施例1
一种识别表现为磨玻璃结节的早期肺腺癌的方法,如图1所示,包括如下步骤:
步骤一:病史采集
首先对因磨玻璃结节预行PET/CT检查的受检者问诊,采集病史资料内容包括:患者年龄、性别、吸烟史、近期外周血肿瘤标志物、肿瘤病史、是否伴随严重肝病或糖尿病可能影响PET/CT半定量指标的疾病,所述血肿瘤标记物包括CEA、CYFRA21-1、CA199、NSE、SCCAg;
步骤二:图像采集
采用德国西门子公司Biograph mCT 64型PET/CT显像仪进行图像扫描,显像剂18F-FDG,放化纯度>95%,受检者空腹>4小时,血糖≤10mmol/L,静脉注射18F-FDG,且18F-FDG的剂量为3.70-7.77MBq/kg,1h后行全身PET/CT显像;
采集体位:仰卧位,双手抱头;范围:头部到大腿中部,先行低剂量定位CT扫描,再行全身PET采集,时间为2min/床位,采用TureD软件(Siemens)进行图像重建,将肺窗的窗宽设置为1200 HU、窗位设置为-600 HU以及将纵隔窗的窗宽设置为350 HU、窗位设置为40 HU,PET/CT显像后,即刻行同机屏气下肺部HRCT扫描,采集及重建条件如下:管电压140kV,管电流依据人体解剖结构和组织密度自动调整,旋转时间0.5秒,螺距0.6,层厚1.0mm,层间隔0.5mm,矩阵512×512,肺窗的窗宽1200HU、窗位-600 HU,纵隔窗的窗宽350 HU、窗位40HU;
步骤三:图像分析
由两位经验丰富的核医学医师独立盲法阅片,获取以下参数:
SUVmax:选择圆形ROI在结节部位逐层测量,记录最大值,且确保磨玻璃结节被完全包括在内(如图3中的A图);
肝脏SUVmean:选择直径60mm圆形ROI在肝右叶进行测量,尽量避开显像剂摄取不均匀部位(如图3中的B图);
SUV指数:即SUVmax与SUVmean的比值;
结节类型:纯磨玻璃结节或混合性磨玻璃结节;
结节位置:外周或中央;
结节数量:单发或多发;
结节形状:类圆形或不规则形;
结节边缘:光滑或分叶;
异常支气管征、空泡征、胸膜凹陷及血管集束征;
结节直径:即肺窗横断面结节最大长径(如图3中的C图);
结节内实性成分直径:即肺窗横断面实性成分最大长径(如图3中的D图);
实性成分占比:即结节内实性成分直径与结节直径之比;
磨玻璃成分CT平均值:选择3个不同CT层面分别测量结节磨玻璃成分的平均CT值,再计算其平均值,测量时尽量避开结节内的血管、支气管、空泡及实性成分(如图3中的E、F图);
所有测量值取两名医师记录的平均值,意见不统一时与第三位观察者共同商讨确定;
步骤四:病例随访
PET/CT检查后一月内随访受检者结局,通过查阅本院电子病例、CT随访图像、电话询问方式获取患者术后病理结果或病灶动态变化;
步骤五:资料收集
按要求筛选符合标准的磨玻璃结节患者资料,所述资料收集的筛选纳入标准为:同时接受PET/CT及HRCT检查的所有患者;病灶经手术切除或HRCT随访病灶明显缩小或消失、病灶最大直径≤30mm、恶性磨玻璃结节均为病理证实为IA期以内的患者(按第8版肺癌TNM分期标准);
所述资料收集的筛选排除标准为:PET/CT及HRCT图像质量差或病灶的直径难以测量;5年内有恶性肿瘤病史者;有严重肝病或糖尿病患者;
步骤六:统计学分析
采用R进行统计学分析,版本3.4.3(http://www.R-project.org;软件包:glmnet,pROC,rms),连续变量服从正态分布时以均数±标准差表示,非正态分布时以P50(P25、P75)表示,分类变量以频率(%)表示,组间比较时连续变量采用非配对的Student-t检验或Mann-Whitney U非参数检验,分类变量采用Pearson卡方检验或Fisher精确检验;
步骤七:建立预测模型
采用多因素Logistic回归方法建立预测模型,并根据最小赤池信息标准(minimalAkaike’s information criterion,AIC)选择最佳的模型参数,计算OR和95%CI;绘制预测模型的列线图(如图2所示),该列线图可以直观地显示每个磨玻璃结节的预测结果,同时绘制校准曲线(如图2所示)以显示列线图的预测精度,以及绘制预测模型ROC曲线(如图2所示),AUC及其95% CI,再应用z统计量比较建模组和验证组的AUC,所有统计检验均为双侧检验,以P<0.05被认为具有统计学差异,在所述预测模型中,患者性别、结节位置、边缘、胸膜凹陷征及SUV指数共5个指标均为预测磨玻璃结节良恶性的独立因素。
综上所述,本发明提出的识别表现为磨玻璃结节的早期肺腺癌的方法,通过病史采集、图像采集、图像分析、病例随访、资料收集、统计学分析以及建立预测模型,同时采用多因素Logistic回归方法建立预测模型,并根据最小赤池信息标准(minimal Akaike’sinformation criterion,AIC)选择最佳的模型参数,绘制预测模型的列线图,该列线图可以直观地显示每个磨玻璃结节的预测结果,此外,绘制了校准曲线以显示列线图的预测精度,应用ROC曲线评估模型预测效能,预测效能较高,灵敏度以及特异度较高,能够有效改善磨玻璃结节的鉴别效能,能够准确有效地识别表现为磨玻璃结节的早期肺腺癌,避免存在错误识别的可能,模型参数较少,应用简单,且其建模数据来源于恶性磨玻璃结节均为病理证实为IA期以内的患者,对于术前高危结节的鉴别准确性较高,其不是建立在实性肺结节的基础上,尤其适用于磨玻璃结节的良恶性预测。
本发明中未涉及部分均与现有技术相同或可采用现有技术加以实现。本具体实施例仅仅是对本发明的解释,其并不是对本发明的限制,本领域技术人员在阅读完本说明书后可以根据需要对本实施例做出没有创造性贡献的修改,但只要在本发明的权利要求范围内都受到专利法的保护。
Claims (4)
1.一种识别表现为磨玻璃结节的早期肺腺癌的方法,其特征在于:包括如下步骤:
步骤一:病史采集
首先对因磨玻璃结节预行PET/CT检查的受检者问诊,采集病史资料内容包括:患者年龄、性别、吸烟史、近期外周血肿瘤标志物、肿瘤病史、是否伴随严重肝病或糖尿病可能影响PET/CT半定量指标的疾病;
步骤二:图像采集
采用PET/CT显像仪进行图像扫描,显像剂18F-FDG,放化纯度>95%,受检者空腹>4小时,血糖≤10mmol/L,静脉注射18F-FDG,且18F-FDG的剂量为3.70-7.77MBq/kg,1h后行全身PET/CT显像;
采集体位:仰卧位,双手抱头;范围:头部到大腿中部,先行低剂量定位CT扫描,再行全身PET采集,时间为2min/床位,采用TureD软件进行图像重建,将肺窗的窗宽设置为1200HU、窗位设置为-600 HU以及将纵隔窗的窗宽设置为350 HU、窗位设置为40 HU,PET/CT显像后,即刻行同机屏气下肺部HRCT扫描,采集及重建条件如下:管电压140kV,管电流依据人体解剖结构和组织密度自动调整,旋转时间0.5秒,螺距0.6,层厚1.0mm,层间隔0.5mm,矩阵512×512,肺窗的窗宽1200HU、窗位-600 HU,纵隔窗的窗宽350 HU、窗位40HU;
步骤三:图像分析
由两位经验丰富的核医学医师独立阅片,获取以下参数:
SUVmax:选择圆形感兴趣区(ROI)在结节部位逐层测量,记录最大值,且确保结节被完全包括在内;
肝脏SUVmean:选择直径60mm圆形ROI在肝右叶进行测量,尽量避开显像剂摄取不均匀部位;
SUV指数:即SUVmax与SUVmean的比值;
结节类型:纯磨玻璃结节或混合性磨玻璃结节;
结节位置:外周或中央;
结节数量:单发或多发;
结节形状:类圆形或不规则形;
结节边缘:光滑或分叶;
异常支气管征、空泡征、胸膜凹陷及血管集束征;
结节直径:即肺窗横断面结节最大长径;
结节内实性成分直径:即肺窗横断面结节实性成分最大长径;
实性成分占比:即结节内实性成分直径与结节直径之比;
磨玻璃成分CT平均值:选择3个不同CT层面分别测量结节磨玻璃成分的平均CT值,再计算其平均值,测量时尽量避开结节内的血管、支气管、空泡及实性成分;
所有测量值取两名医师记录的平均值,意见不统一时与第三位观察者共同商讨确定;
步骤四:病例随访
PET/CT检查后一月内随访受检者结局,通过查阅本院电子病例、CT随访图像、电话询问方式获取患者术后病理结果或病灶动态变化;
步骤五:资料收集
按要求筛选符合标准的磨玻璃结节患者资料;
步骤六:统计学分析
采用R进行统计学分析,连续变量服从正态分布时以均数±标准差表示,非正态分布时以P50(P25、P75)表示,分类变量以频率(%)表示,组间比较时连续变量采用非配对的Student-t检验或Mann-Whitney U非参数检验,分类变量采用Pearson卡方检验或Fisher精确检验;
步骤七:建立预测模型
采用多因素Logistic回归方法建立预测模型,并根据最小赤池信息标准选择最佳的模型参数,计算优势比(odds ratio, OR)和95%可信区间(CI);绘制预测模型的列线图,该列线图可以直观地显示每个磨玻璃结节的预测概率,同时绘制校准曲线以显示列线图的预测精度,以及绘制预测模型的受试者工作特征曲线,并获得曲线下面积及其95% CI,再应用z统计量比较建模组和验证组的AUC,所有统计检验均为双侧检验,以P<0.05被认为具有统计学差异。
2.根据权利要求1所述的一种识别表现为磨玻璃结节的早期肺腺癌的方法,其特征在于:所述资料收集的筛选纳入标准为:同时接受PET/CT及HRCT检查的所有患者;病灶经手术切除或HRCT随访病灶明显缩小或消失、病灶最大直径≤30mm、恶性磨玻璃结节均为病理证实为IA期以内的患者;
所述资料收集的筛选排除标准为:PET/CT及HRCT图像质量差或病灶的直径难以测量;5年内有恶性肿瘤病史者;有严重肝病或糖尿病患者。
3.根据权利要求2所述的一种识别表现为磨玻璃结节的早期肺腺癌的方法,其特征在于:所述外周血肿瘤标记物包括CEA、CYFRA21-1、CA199、NSE、SCCAg。
4.根据权利要求1所述的一种识别表现为磨玻璃结节的早期肺腺癌的方法,其特征在于:在所述预测模型中,患者性别、结节位置、边缘、胸膜凹陷征及SUV指数共5个指标均为预测磨玻璃结节良恶性的独立因素。
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CN112767393A (zh) * | 2021-03-03 | 2021-05-07 | 常州市第一人民医院 | 一种基于机器学习的双模态影像组学磨玻璃结节分类方法 |
CN113643809A (zh) * | 2021-08-05 | 2021-11-12 | 上海市第六人民医院 | 基于人体成分的2型糖尿病预测方法及系统 |
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CN112767393A (zh) * | 2021-03-03 | 2021-05-07 | 常州市第一人民医院 | 一种基于机器学习的双模态影像组学磨玻璃结节分类方法 |
CN113643809A (zh) * | 2021-08-05 | 2021-11-12 | 上海市第六人民医院 | 基于人体成分的2型糖尿病预测方法及系统 |
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