基于诊断CT影像组学对食管癌放疗疗效早期评估

目的 探讨分析诊断级CT图像特征预测食管癌放疗效果的可行性。方法 33例接受调强放疗的Ⅰ-Ⅳ期食管癌患者;治疗期间采用CT-on-rail影像系统获取CT图像,MIM图像系统获取大体肿瘤、脊髓和无照射组织(NIT)的容积、CT平均值、标准差、斜度等影像特征数据,并观察其变化。根据治疗后3个月随访结果将患者分为有效组(完全缓解+部分缓解)24例、无效组(无变化+进展)9例。采用自行设计Matlab软件动态分析组间差异。结果 33例患者的肿瘤容积、CT平均值随照射剂量增加而逐渐减小,有效组、无效组容积和CT平均值分别减少42.46%、21.76%和5.76HU和3.66HU,有效组下降幅度较无效组更显著(P<0.005);无效组的斜度随放疗剂量增加而下降,有效组的斜度变化趋势则相反,两组变化值分别为0.503和-0.450(P=0.034);脊髓和NIT随照射剂量增加无变化。结论 分析放疗期间食管癌患者CT图像的特征数据可能早期预测放疗疗效。     

Radiomic Feature Robustness and Reproducibility in Quantitative Bone Radiography: A Study on Radiologic Parameter Changes

The purpose of this study was to investigate the robustness of different radiography radiomic features over different radiologic parameters including kV, mAs, filtration, tube angles, and source skin distance (SSD). A tibia bone phantom was prepared and all imaging studies was conducted on this phantom. Different radiologic parameters including kV, mAs, filtration, tube angles, and SSD were studied. A region of interest was drawn on the images and many features from different feature sets including histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet derived parameters were extracted. All radiomic features were categorized based on coefficient of variation (COV). Bland-Altman analysis also was used to evaluate the mean, standard deviation, and upper/lower reproducibility limits for radiomic features in response to variation in each testing parameters. Results on COV in all features showed that 22%, 34%, and 45% of features were most robust (COV ≤ 5%) against kV, mAs, and SSD respectively and there was no robust features against filtration and tube angle. Also, all features (100%) and 76% of which showed large variations (COV > 20%) against filtrations and tube angle respectively. Autoregressive model feature set has no robust features against all radiologic parameters. Features including sum-average, sum-entropy, correlation, mean, and percentile (50, 90, and 99) belong to co-occurrence matrix and histogram feature sets were found as most robust features. Bland-Altman analysis showed the high reproducibity of some feature sets against radiologic parameter changes. The results presented here indicated that radiologic parameters have great impacts on radiomic feature values and caution should be taken into account when work with these features. In quantitative bone studies, robust features with low COV can be selected for clinical or research applications. Reproducible features also can be obtained using Bland-Altman analysis.     

肿瘤空间异质性影像学定量评价进展

肿瘤异质性是恶性肿瘤的特征之一,实现肿瘤异质性的可视化并应用现代影像方法给予精确定量,是影像学面临的新挑战。新兴的放射组学解决了这一难题,放射组学应用于肿瘤空间异质性分析并具有可视化效果,其影像定量方法主要包括直方图分析、纹理分析以及参数反应图,这些方法通过肿瘤空间异质性定量评估加快药物研发、改善疗效判断、完善治疗方案和预后评估。     

分层分析影像组学模型在肺腺癌诊断中的价值

目的:建立并验证可高效鉴别肺腺癌及其浸润程度的预测模型,并根据结节/肿块性质分层分析模型的预测效能.方法:回顾性分析本院2011年10月-2018年12月经病理证实的肺结节/肿块患者2105例.根据肿瘤性质,分为磨玻璃组(A组,1711例)和实性组(B组,394例),组内以2017年10月为界,分为训练集和测试集.收集...     

Prediction of Prognosis in Glioblastoma Using Radiomics Features of Dynamic Contrast-Enhanced MRI

ObjectiveTo develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for prognosis prediction in patients with glioblastoma.Materials and MethodsOne hundred and fifty patients (92 male [61.3%]; mean age ± standard deviation, 60.5 ± 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (K^trans), fractional volume of vascular plasma space (V_p), and fractional volume of extravascular extracellular space (V_e) maps of DCE MRI, wherein the regions of interest were based on both T1-weighted contrast-enhancing areas and non-enhancing T2 hyperintense areas. Using feature selection algorithms, salient radiomic features were selected from the 642 features. Next, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105); the risk score was validated in the validation set (n = 45) by investigating the difference in prognosis between the “radiomics risk score” groups. Finally, multivariable Cox regression analysis for progression-free survival was performed using the radiomics risk score and clinical variables as covariates.Results16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both p < 0.001 by the log-rank test). The radiomics risk score and presence of isocitrate dehydrogenase (IDH) mutation showed independent associations with progression-free survival in opposite directions (hazard ratio, 3.56; p = 0.004 and hazard ratio, 0.34; p = 0.022, respectively).ConclusionWe developed and validated the “radiomics risk score” from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.     

基于多参数MRI生境成像对胶质母细胞瘤评估的研究进展

胶质母细胞瘤（GB）是中枢神经系统中最常见的原发性恶性肿瘤,其显著的肿瘤异质性是导致肿瘤进展、治疗抵抗和复发的主要原因。生境成像是基于肿瘤病理、血流灌注、分子特征等差异,对MR影像进行肿瘤亚区域目的性划分的分割技术,可用于揭示肿瘤内部异质性,反映肿瘤内病理生理、分子特征等方面的生物学差异,在GB术前分级诊断、分子特征评估、治疗反应评估、指导立体定向病理活检及预测预后等方面均取得了进展。就目前基于多参数MRI的生境成像技术在GB中的最新应用和临床价值予以综述。     

影像基因组学在肝细胞癌诊疗中的应用研究进展

肝细胞癌（HCC）的发病率和病死率较高,早期精准诊疗对病人的预后至关重要。以形态学为主的影像诊断模式已不能满足临床需求。影像组学能深度挖掘影像提供的庞大数据信息,采用定量数据分析综合评价基因遗传、细胞分子、组织形态等各层次HCC的表型,从而对HCC进行精准诊疗和无创监测;基因组学从分子及基因层面揭示HCC的发病机制;影像基因组学主要为HCC形态学特征和影像组学表型特征与基因组学的关联。就HCC的影像基因组学研究进展进行综述。     

影像组学在心脏CT成像中的研究进展

心脏CT成像已被广泛用于冠状动脉及心脏结构性疾病的诊断。传统心脏CT影像主要依靠医生通过视觉进行评估。影像组学可以从医学影像中挖掘大量人眼看不见的定量特征,并与机器学习等数据分析方法相结合,构建模型预测疾病状态以及病人的临床结局。就影像组学方法在心脏CT成像领域（包括冠状动脉斑块、冠状动脉周围脂肪、心肌组织及心脏占位）的应用现状予以综述,并探讨面临的挑战及发展方向。     

基于冠状动脉周围脂肪组织影像组学特征及神经网络模型预测冠状动脉狭窄血流动力学严重程度

目的 评估基于神经网络方法构建的预测模型能否精准评估冠状动脉狭窄的血流动力学严重程度（缺血或不缺血）。 方法 回顾性收集行冠状动脉CT血管成像（CCTA）及有创冠状动脉造影的血流储备分数（FFR）测量的92例冠状动脉疾病病人的临床及影像资料,其中男66例,女26例;平均年龄（58.3±10.3）岁。共纳入122支冠状动脉血管。依据FFR值将122支冠状动脉血管分为2组,即狭窄组（FFR≤0.8,68支）和非狭窄组（FFR>0.8,54支）。基于CCTA影像数据,选取冠状动脉周围脂肪组织（PCAT）区域的468个影像组学特征进行分析。构建3种冠状动脉狭窄预测模型,包括神经网络模型、传统统计学模型和最小绝对值收敛与选择算子模型。采用受试者操作特征曲线下面积（AUC）评估3种模型预测冠状动脉狭窄的性能。采用Pearson相关分析神经网络特征、原始影像组学特征与真实标签的相关性。采用独立样本t检验比较2组的影像组学特征。 结果 3种预测模型中,神经网络模型的预测效能最高,其准确度、敏感度、特异度和AUC分别为81.19%、81.23%、81.16%和0.781 3（0.773 8~0.788 8）。神经网络特征与真实冠状动脉狭窄标签的相关性[最大绝对相关系数（r_最大）=0.683 8,P<0.001,平均绝对相关系数（r_平均）=0.261 1]高于原始影像组学特征与真实标签的相关性（r_最大=0.238 9,P=0.008和r_平均=0.090 5）。狭窄组的W6_surface_area高于非狭窄组,而W6_Auto Correlation_mean低于非狭窄组（均P<0.05）,其余特征差异均无统计学意义（均P>0.05）。 结论 以影像组学特征为输入的神经网络模型可以很好地预测冠状动脉狭窄,其中10个PCAT区域影像组学特征或许在预测冠状动脉狭窄的血流动力学方面具有重要意义。     

增强MRI影像组学在构建乳腺癌腋窝淋巴结转移预测模型中的应用研究

目的探讨基于增强MRI影像组学构建的预测模型对乳腺癌腋窝淋巴结转移的诊断效能。 方法根据纳入及排除标准,回顾性分析2016年1月至2020年6月在吉林省肿瘤医院接受手术治疗的376例女性乳腺癌患者的临床、病理及影像资料。患者术前接受乳腺增强MRI检查。用随机数字表法从所有患者中选择20例,由2名放射科医师对其影像独立分割病灶,勾画感兴趣区域(ROI)。采用组内相关系数(ICC)检验对2名医师勾画的ROI进行一致性分析。采用A.K.(Version 3.3.0)软件提取病灶区域的三维纹理特征参数,通过IPMs(Version 2.0.2)软件及最小绝对收缩和选择算子(LASSO)筛选出最佳影像组学特征,用于构建乳腺癌淋巴结转移预测模型。按7∶3的比例将患者随机分为训练组(n＝263)和验证组(n＝113)。采用6种机器学习模型,包括Logistic回归、随机森林(RF)、贝叶斯算法(NB)、决策树(DT)、邻近算法(KNN)和支持向量机(SVM)模型,对2组数据进行处理,采用受试者操作特征(ROC)曲线分析各机器学习模型对乳腺癌腋窝淋巴结转移的诊断效能,根据训练组ROC曲线下面积(AUC)大小,选择最佳模型。用决策曲线分析(DCA)评价最佳模型的临床获益。 结果共有腋窝淋巴结阳性患者114例,腋窝淋巴结阴性患者262例。操作者一致性检验结果显示：2名医师具有很强的操作者一致性(ICC＝0.915,95%CI: 0.894~0.932, P<0.001)。每个病灶提取得到396个三维纹理特征参数,筛选保留7个淋巴结阳性组与淋巴结阴性组间差异明显的影像组学特征,并将其与2个临床指标(年龄和绝经状态)和5个影像学指标(病灶位置、有无钙化、病灶数量、病灶长径和造影剂时间-信号曲线类型)一起,共14个参数,构建预测模型。6种机器学习模型中,Logistic回归模型在训练组中诊断乳腺癌腋窝淋巴结转移的AUC最高(0.798),准确率为73.0%(192/263)、特异度为75.4%(138/183)、敏感度为67.5%(54/80),在验证组的AUC为0.767、准确率为73.5%(83/113)、特异度为77.2%(61/79)、敏感度为64.7%(22/34),为最佳机器学习模型。基于Logistic回归模型的决策曲线分析结果显示：训练组中阈值为0.15~1.00,验证组中阈值为0.10~0.60,有临床获益。 结论基于增强MRI影像组学结合机器学习模型建立的预测模型能够鉴别乳腺癌淋巴结转移,其中以Logistic回归模型的诊断效能更优。