CN116230227A - 一种肺癌脑转移风险预测方法 - Google Patents
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Abstract
本发明涉及一种肺癌脑转移风险预测方法,所述方法包括:对胸部CT图像上肺肿瘤影像组学特征提取;在对脑部MRI影像分割的基础上,计算脑实质区域、脑灰质区域和脑白质区域的定量影像特征;融合胸部CT影像上肺肿瘤和MRI影像上脑实质区域、脑灰质区域和脑白质区域影像特征,在运用特征选择方法筛选最佳特征的基础上,利用机器学习分类器构建脑转移预测模型;输出所述最终肺癌脑转移风险预测概率。本发明的方法自动化程度高,提高了肺癌脑转移风险预测的准确率。
Description
技术领域
本发明属于医学图像处理技术领域,尤其涉及一种肺癌脑转移风险预测方法。
背景技术
肺癌是全球范围内死亡率最高的恶性肿瘤,局部复发和远处转移是导致肺癌病死率高的主要原因,而中枢神经系统是常见的肺癌复发远处转移部位。肺癌患者出现脑转移,表明恶性肿瘤已经广泛播散往往预后较差,因此,预测晚期肺癌患者的脑转移风险具有重要的临床价值。
对于无驱动基因突变的肺癌脑转移患者,目前传统的化疗、全脑放疗、立体定向放疗以及手术切除仍然是主要的治疗手段。但是,传统的治疗方法如单纯放疗、化疗、外科手术对于改善肺癌脑转移的预后非常有限。近年来随着立体定向放射治疗、靶向治疗的发展,尤其是化疗联合靶向、放疗联合靶向等交叉领域的联合治疗,使肺癌脑转移患者的生存质量得到提高,中位总生存期得以延长。因此,若能及时检测和识别高风险肺癌脑转移患者,则可有针对性的设计治疗方案,改善患者预后。但是,目前尚无有效的技术手段可以有效识别和预测脑转移风险。因此,如果能设计和开发有效的生物标记物预测晚期肺癌的脑转移风险和生存,则能够实现不同病人的风险分层,并及时对高风险病人进行干预治疗,制订个性化的治疗方案,改善患者预后。
发明内容
为了克服上述现有技术中存在的缺点与不足,本发明提供一种肺癌脑转移风险预测方法,所述方法首先对胸部CT图像上肺肿瘤影像组学特征提取;其次,在对脑部MRI影像分割的基础上,计算脑实质区域、脑灰质区域和脑白质区域的定量影像特征;然后,融合胸部CT影像上肺肿瘤和MRI影像上脑实质区域、脑灰质区域和脑白质区域影像特征,在运用特征选择方法筛选最佳特征的基础上,利用机器学习分类器构建脑转移预测模型;最后,输出所述最终肺癌脑转移风险预测概率。本发明的方法自动化程度高,提高了肺癌脑转移风险预测的准确率。
本发明提供了一种肺癌脑转移风险预测方法,所述方法包括以下步骤:
S1:对胸部CT图像进行预处理,分割肺肿瘤区域,提取所述肺肿瘤区域的影像组学特征;
S2:对脑部MRI影像进行预处理,分割脑实质区域、脑灰质区域和脑白质区域,提取脑实质区域、脑灰质区域和脑白质区域的影像组学特征;
S3:融合所述胸部CT和所述脑部MR I的影像组学特征;
S4:使用特征选择方法对所述融合的影像组学特征进行筛选,利用机器学习分类器构建肺癌脑转移风险预测模型;
S5:利用所述肺癌脑转移风险预测模型输出所述肺癌脑转移风险概率。
进一步,所述对胸部CT图像进行预处理,分割肺肿瘤区域,提取所述肺肿瘤区域的影像组学特征包括:
S11:输入肺部CT序列断层影像I CT;
S12:运用三次B样条差值算法将所述影像I CT的图像分辨率重采样至[1mm,1mm,1mm];
S13:采用手动、半自动或全自动分割方法对CT影像上肺肿瘤区域进行分割;
S14:运用LoG滤波和小波滤波算法对胸部CT图像进行滤波处理;
S15:在原始CT影像、LoG滤波图像和小波图像上定量计算肺肿瘤区域的影像组学特征。
进一步,所述分割脑实质区域、脑灰质区域和脑白质区域,提取脑实质区域、脑灰质区域和脑白质区域的影像组学特征包括:
S21:运用基于体素形态学分析(voxe l-based morphometry,VBM)方法,对MR I脑部扫描图像进行分割,主要步骤包括:空间归一化、偏置场校正、分割、调制和平滑,获得脑实质区域、灰质区域和白质区域的分割结果;
S22:运用LoG滤波和小波变换算法对MRI图像进行滤波处理;
S23:在原始MRI影像、LoG滤波图像和小波图像上定量计算脑实质区域、灰质区域和白质区域的影像组学特征。
进一步,所述影像组学特征包括:灰度特征、形状特征和纹理特征。
进一步,所述对MRI脑部扫描图像进行分割之前用T1加权MRI脑部扫描图像。
进一步,所述融合所述胸部CT和所述脑部MRI的影像组学特征具体包括:将肺肿瘤CT影像特征、脑实质区域MRI影像特征、脑灰质区域MRI影像特征和脑白质区域MRI影像特征融合,构建影像组学特征;
进一步,所述使用特征选择方法对所述融合的影像组学特征进行筛选,利用机器学习分类器构建风险预测模型包括:
S41:运用z-sore归一化方法将所有影像特征进行归一化处理;
S42:利用特征选择方法筛选出有效的影像特征;
S43:利用合成少数类过采样技术对样本进行重采样,使训练集样本中脑转移样本和无脑转移样本数量一致;
S44:将训练集样本输入机器学习分类器训练分类模型,得到所述肺癌脑转移风险预测模型。
进一步,所述特征选择方法可以是基于Lasso回归的递归式特征消除法。
进一步,所述机器学习分类器可以是支持向量机。
基于上述技术方案,与现有技术相比本发明的有益效果:本发明通过建立基于胸部CT影像和脑部MRI影像组学分析的肺癌脑转移风险预测系统,和MRI影像上脑实质区域、脑灰质区域和脑白质区域的影像组学特征提取;利用特征选择方法筛选出最优的肺肿瘤和脑实质区域影像组学特征;使用机器学习分类器构建风险预测模型,实现对肺癌脑转移风险的预测,提高了肺癌脑转移风险的预测精度。
附图说明
图1是肺癌脑转移风险预测方法流程图;
图2是融合CT和MRI影像的肺癌脑转移风险预测结果示意图;
具体实施方式
下面结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。
实施例1
图1是肺癌脑转移风险预测方法流程图。如图1所示,本发明提供了一种基于胸部CT(电子计算机断层扫描,Computed Tomography)影像和脑部MRI(磁共振成像,Nuc l earMagnet i c Resonance Imagi ng)影像组学分析的肺癌脑转移风险预测系统,所述方法包括以下步骤:
S1:对胸部CT图像进行预处理,分割肺肿瘤区域,提取所述肺肿瘤区域的影像组学特征;
S2:对脑部MRI影像进行预处理,分割脑实质区域、脑灰质区域和脑白质区域,提取脑实质区域、脑灰质区域和脑白质区域的影像组学特征;
S3:融合所述胸部CT和所述脑部MR I的影像组学特征;
S4:使用特征选择方法对所述融合的影像组学特征进行筛选,利用机器学习分类器构建肺癌脑转移风险预测模型;
S5:利用所述肺癌脑转移风险预测模型输出所述肺癌脑转移风险概率。
实施例2是在实施例1的基础上进行进一步说明:
S1:对胸部CT图像进行预处理,分割肺肿瘤区域,提取所述肺肿瘤区域的影像组学特征;。
具体地,在对CT影像重采样的基础上,对肺肿瘤区域进行分割,运用LoG滤波和小波变换对原始图像进行滤波处理,分别在原始图像和滤波处理后图像上提取肺肿瘤区域的影像组学特征。
S2:对脑部MRI影像进行预处理,分割脑实质区域、脑灰质区域和脑白质区域,提取脑实质区域、脑灰质区域和脑白质区域的影像组学特征。
具体地,所述在对脑MRI图像分割基础上,运用LoG滤波和小波变换对原始图像进行滤波处理,分别在原始MRI图像和滤波处理后脑部MRI影像上提取脑实质区域、脑灰质区域和脑白质区域的影像组学特征。
S3:融合所述胸部CT和所述脑部MR I的影像组学特征。
S4:使用特征选择方法对所述融合的影像组学特征进行筛选,利用机器学习分类器构建肺癌脑转移风险预测模型。
具体地,将所述融合的影像组学特征做为输入,运用z-sore归一化方法对特征进行归一化处理,选用合适的特征选择方法筛选出有效的影像特征,利用合成少数类过采样技术对样本进行重采样,通过机器学习分类器构建分类模型,得到最终肺癌脑转移风险预测模型。
S5:利用所述肺癌脑转移风险预测模型输出所述最终肺癌脑转移风险预测结果。
具体地,利用所述肺癌脑转移风险预测模型来得到结果时可通过人机界面显示最终脑转移风险预测结果,所述结果是脑转移风险的预测概率。。
优选地,步骤S1包括:
S11:输入肺部CT序列断层影像I CT;
S12:运用三次B样条差值算法将所述影像I CT的图像分辨率重采样至[1mm,1mm,1mm];
S13:采用手动、半自动或全自动分割方法对CT影像上肺肿瘤区域进行分割;
S14:运用LoG滤波和小波滤波算法对胸部CT图像进行滤波处理;
S15:在原始CT影像、LoG(高斯-拉普拉斯Lap l ac i an of Gauss i an)滤波图像和小波图像上定量计算肺肿瘤区域的影像组学特征,主要包括:灰度特征、形状特征和纹理特征。
优选地,步骤S2包括:
S21:运用基于体素形态学分析(voxe l-based morphometry,VBM)方法,对MRI脑部扫描图像(一般用T1加权图像)进行分割,主要步骤包括:空间归一化、偏置场校正、分割、调制和平滑,获得脑实质区域、灰质区域和白质区域的分割结果;
S22:运用LoG滤波和小波变换算法对MRI图像进行滤波处理;
S23:在原始MRI影像、LoG滤波图像和小波图像上定量计算脑实质区域、灰质区域和白质区域的影像组学特征,主要包括:灰度特征、形状特征和纹理特征。
优选地,步骤S3包括:
将肺肿瘤CT影像特征、脑实质区域MRI影像特征、脑灰质区域MRI影像特征和脑白质区域MRI影像特征融合,构建影像组学特征集;
优选地,步骤S4包括:
S41:运用z-sore归一化方法将所述融合的影像组学特征集进行归一化处理;
S42:利用特征选择方法筛选出有效的影像特征,其中特征选择方法可以是基于Lasso回归的递归式特征消除法等;
S43:利用合成少数类过采样技术(Synthet i c Mi nor i ty Oversamp l i ngTechn i que)对样本进行重采样,使训练集样本中脑转移样本和无脑转移样本数量一致;
S44:将训练集样本输入机器学习分类器(如支持向量机等)训练分类模型;得到最终肺癌脑转移风险预测模型。
实施例3
以下给出一个对本实施例的肺癌脑转移风险预测方法的实验过程:
1、实验条件:
本实验训练集数据来自复旦大学附属肿瘤医院,共搜集2005年至2020年期间,256名患者的胸部CT和脑部MRI影像。所有影像均为治疗期拍摄,无其他原发肿瘤,脑MRI上未出现转移病灶。经过3年随访,128名患者出现脑转移,另外128名患者未出现脑转移。将所有样本按照7:3等比例分为训练组和验证组。
2、实验结果及结果分析
通过融合CT和MRI影像组学特征构建预测模型,在训练集中准确率为77.7%、敏感性74.4%、特异性80.9%、阴性预测值79.8%、阳性预测值75.8%,在测试集中准确率为71.4%、敏感性78.9%、特异性64.1%、阴性预测值68.2%、阳性预测值75.8%。融合CT和MRI影像的肺癌脑转移风险预测结果的ROC曲线如图2所示。
本发明提出的肺癌脑转移风险预测算法,运用一系列医学图像处理方法实现了CT图像和脑MRI影像上影像组学特征的提取,为探究基于医学影像肺癌脑转移风险预测的计算机辅助诊断技术奠定了基础。
以上所述仅为本发明的优选实施例,并非因此限制本发明的专利范围,凡是在本发明的构思下,利用本发明说明书及附图内容所作的等效结构变换,或直接/间接运用在其他相关的技术领域均包括在本发明的专利保护范围内。
Claims (9)
1.一种肺癌脑转移风险预测方法,其特征在于,所述方法包括以下步骤:
S1:对胸部CT图像进行预处理,分割肺肿瘤区域,提取所述肺肿瘤区域的影像组学特征;
S2:对脑部MRI影像进行预处理,分割脑实质区域、脑灰质区域和脑白质区域,提取脑实质区域、脑灰质区域和脑白质区域的影像组学特征;
S3:融合所述胸部CT和脑部MRI的影像组学特征;
S4:使用特征选择方法对所述融合的影像组学特征进行筛选,利用机器学习分类器构建肺癌脑转移风险预测模型;
S5:利用所述肺癌脑转移风险预测模型输出所述肺癌脑转移风险概率。
2.根据权利要求1所述的方法,其特征在于,所述对胸部CT图像进行预处理,分割肺肿瘤区域,提取所述肺肿瘤区域的影像组学特征包括:
S11:输入肺部CT序列断层影像ICT;
S12:运用三次B样条差值算法将所述影像ICT的图像分辨率重采样至[1mm,1mm,1mm];
S13:采用手动、半自动或全自动分割方法对CT影像上肺肿瘤区域进行分割;
S14:运用LoG滤波和小波滤波算法对胸部CT图像进行滤波处理;
S15:在原始CT影像、LoG滤波图像和小波图像上定量计算所述肺肿瘤区域的影像组学特征。
3.根据权利要求1所述的方法,其特征在于,所述分割脑实质区域、脑灰质区域和脑白质区域,提取脑实质区域、脑灰质区域和脑白质区域的影像组学特征包括:
S21:运用基于体素形态学分析方法,对MRI脑部扫描图像进行分割,主要步骤包括:空间归一化、偏置场校正、分割、调制和平滑,获得脑实质区域、灰质区域和白质区域的分割结果;
S22:运用LoG滤波和小波变换算法对MRI图像进行滤波处理;
S23:在原始MRI影像、LoG滤波图像和小波图像上定量计算脑实质区域、灰质区域和白质区域的影像组学特征。
4.根据权利要求2或3所述的方法,其特征在于,所述影像组学特征包括:灰度特征、形状特征和纹理特征。
5.根据权利要求3所述的方法,其特征在于,所述对MRI脑部扫描图像进行分割之前用T1加权MRI脑部扫描图像。
6.根据权利要求1所述的方法,其特征在于,融合所述所述胸部CT和脑部MRI融合所述胸部CT和所述脑部MRI的影像组学特征具体包括:将肺肿瘤CT影像特征、脑实质区域MRI影像特征、脑灰质区域MRI影像特征和脑白质区域MRI影像特征融合,构建影像组学特征。
7.根据权利要求6所述的方法,其特征在于,所述使用特征选择方法对所述融合的影像组学特征进行筛选,利用机器学习分类器构建风险预测模型包括:
S41:运用z-sore归一化方法将所有影像特征进行归一化处理;
S42:利用特征选择方法筛选出有效的影像特征;
S43:利用合成少数类过采样技术对样本进行重采样,使训练集样本中脑转移样本和无脑转移样本数量一致;
S44:将训练集样本输入机器学习分类器训练分类模型,得到所述肺癌脑转移风险预测模型。
8.根据权利要求7所述的方法,其特征在于,所述特征选择方法可以是基于Lasso回归的递归式特征消除法。
9.根据权利要求7所述的方法,其特征在于,所述机器学习分类器可以是支持向量机。
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