脑转移瘤影像组学研究进展

脑转移瘤是成人最常见的颅内肿瘤,发病率呈上升趋势。影像组学可对医学影像进行定量分析处理来指导临床实践。近年来,基于CT、MRI的影像组学逐渐应用于脑转移瘤的精准诊疗,如肿瘤精准检测和分割、与其他脑肿瘤的鉴别诊断、原发肿瘤的判别、疗效评价及预后预测等。本文就脑转移瘤影像组学研究现状予以综述。     

Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer

In this paper, several radiomics-based predictive models of response to induction chemotherapy (IC) in sinonasal cancers (SNCs) are built and tested. Models were built as a combination of radiomic features extracted from three types of MRI images: T1-weighted images, T2-weighted images and apparent diffusion coefficient (ADC) maps. Fifty patients (aged 54 ± 12 years, 41 men) were included in this study. Patients were classified according to their response to IC (25 responders and 25 nonresponders). Not all types of images were acquired for all of the patients: 49 had T1-weighted images, 50 had T2-weighted images and 34 had ADC maps. Only in a subset of 33 patients were all three types of image acquired. Eighty-nine radiomic features were extracted from the MRI images. Dimensionality reduction was performed by using principal component analysis (PCA) and by selecting only the three main components. Different algorithms (trees ensemble, K-nearest neighbors, support vector machine, naïve Bayes) were used to classify the patients as either responders or nonresponders. Several radiomic models (either monomodality or multimodality obtained by a combination of T1-weighted, T2-weighted and ADC images) were developed and the performance was assessed through 100 iterations of train and test split. The area under the curve (AUC) of the models ranged from 0.56 to 0.78. Trees ensemble, support vector machine and naïve Bayes performed similarly, but in all cases ADC-based models performed better. Trees ensemble gave the highest AUC (0.78 for the T1-weighted+T2-weighted+ADC model) and was used for further analyses. For trees ensemble, the models based on ADC features performed better than those models that did not use those features (P < 0.02 for one-tail Hanley test, AUC range 0.68–0.78 vs 0.56–0.69) except the T1-weighted+ADC model (AUC 0.71 vs 0.69, nonsignificant differences). The results suggest the relevance of ADC-based radiomics for prediction of response to IC in SNCs.     

Intelligent Imaging: Radiomics and Artificial Neural Networks in Heart Failure

BackgroundOur previous work with ¹²³iodine meta-iodobenzylguanidine (¹²³I-mIBG) radionuclide imaging among patients with cardiomyopathy reported limitations associated with the prognostic power of global parameters derived from planar imaging [1]. Employing multivariate analysis, we further showed the regional washout associated with territories adjacent to infarcted myocardium obtained from single-photon emission computed tomography imaging (SPECT) yielded superior prognostic power over the other planar and SPECT indices in predicting future cardiac events [1]. The aim of this study was to apply an artificial neural network (Neural Analyser version 2.9.5) to the original data from the same patient cohort to evaluate the most potent prognostic index for future cardiac events among patient with cardiomyopathy.MethodsThe original data were reevaluated using an artificial neural network (Neural Analyser version 2.9.5). There were 84 input variables in the original 22 patients from clinical data, electrocardiogram (rest, stress, and continuous ambulatory electrocardiogram recording), transthoracic echocardiography, coronary angiogram, sestamibi myocardial perfusion SPECT, planar and SPECT 123I-mIBG, and genetic and biomarkers, detailed in the previous work. A single binary output was a cardiac event or no cardiac event in the follow-up period.ResultsFollowing training and validation phases, the optimal number of inputs was determined to be two with a training loss of 0.025 and selection loss <0.001. The final architecture had inputs of a change in left ventricular ejection fraction (Δ > −10%) and 123I-mIBG planar global washout (>30%), two hidden layers of 6 and 1 node, respectively, and a binary output. Using receiver operator characteristics analysis demonstrated an area under the curve of 0.75 correlating to a sensitivity of 100% and specificity of 50%.ConclusionThe premise that regional washout of 123I-mIBG SPECT from noninfarcted tissue is the best predictor of cardiac events was built on has a sound and logical foundation. By artificial neural network analysis; however, 123I-mIBG planar global washout of >30% was shown to be the best indicator for risk of cardiac event when accompanied by a decline in left ventricular ejection fraction of >10%. Further investigation should be undertaken assessing assimilation into big data and the potential for automated feature extraction from raw image datasets with convolutional neural networks.     

肾上腺腺瘤的影像组学研究进展

肾上腺腺瘤是临床上较常见的一种肿瘤。目前,影像组学在肾上腺良、恶性肿瘤,功能性肾上腺腺瘤亚型以及脂质性腺瘤和嗜铬细胞瘤的鉴别中取得初步进展,主要通过纹理分析、直方图分析等影像组学特征对肾上腺腺瘤展开研究,可有效评估肿瘤的异型性,可在临床决策上提供有效的帮助。此外,临床特征与影像组学特征结合的诺模图模型对功能性肾上腺腺瘤亚型具有良好的鉴别效果。现综述影像组学在肾上腺腺瘤的诊断和鉴别诊断以及治疗策略的疗效预测、监测和预后评估的研究现状、存在问题,并对未来进行初步展望。     

基于临床及影像组学构建中轴性脊柱关节病支持向量机预测模型

目的：基于临床及影像组学采用支持向量机（SVM）构建中轴性脊柱关节病（axSpA）的预测模型。方法：回顾性收集2012 年10月至2019 年2月在温州医科大学附属第一医院就诊的568 例腰背痛患者,最终诊断axSpA 319 例,非axSpA 249 例。按7:3 将患者随机分为训练组与验证组。于骶髂关节CT上手动勾画三维感兴趣区（VOI）并提取影像组学特征,应用最小冗余最大相关性（mRMR）及最小绝对收缩和选择算子（LASSO）算法进行降维及选择最优影像组学特征;采用单因素和多因素Logistic回归分析寻找诊断axSpA的临床危险因素。最后使用SVM分别构建临床、影像组学及临床-影像组学联合模型,利用受试者工作特征（ROC）曲线及Delong检验评估模型的诊断效能。结果：临床-影像组学联合模型在验证组中具有最佳诊断效能,诊断准确性为0.83,灵敏度和特异度分别为85.2%、79.7%,其ROC曲线下面积（AUC=0.91）高于临床模型（AUC=0.81）及影像组学模型（AUC=0.83）,差异均有统计学意义（P ＜0.05）。结论：基于临床和影像组学构建SVM模型对诊断axSpA具有较高价值。     

基于CT影像组学胰十二指肠切除术后胰瘘风险预测模型及其价值

目的 探讨基于CT影像组学模型术前预测胰十二指肠切除术后胰瘘（POPF）的应用价值。方法 回顾性分析106例接受胰十二指肠切除术患者的临床及腹部CT资料,其中POPF（+）组36例,POPF（–）组70例。采用ITAK-SNAP软件勾画CT图像感兴趣区域（ROI）,Python程序的radiomics包进行影像组学特征提取,使用最小绝对收缩和选择算子（least absolute shrinkage and selection operator,LASSO）回归进一步筛选特征、建立影像组学评分（Rad-score）,构建影像组学预测模型。然后将临床特征、Rad-score纳入多因素Logistic回归分析,筛选出POPF发生的独立危险因素,构建临床预测模型以及联合影像学组学特征的混合模型。最后采用受试者工作特征曲线（ROC）评估不同模型的预测效能。结果 共筛选出7个非零影像组学特征并建立了Rad-score。BMI、胰管扩张及Rad-score均是发生POPF的独立危险因素。影像组学预测模型、临床特征预测模型及混合预测模型预测POPF的曲线下面积（AUC）为分别为0.72、0.69、0.80;Delong检验表明临床特征预测模型与混合预测模型间的差异具有统计学意义。结论 基于CT影像组学模型在术前辅助预测胰十二指肠切除术POPF方面具有一定的价值,联合临床指标能够提高模型的预测效能。     

18F-FDG PET/CT影像组学预测恶性肿瘤预后

¹⁸F-FDG PET/CT影像组学可通过自动、高通量方法自PET/CT图像中提取大量定量特征。应用人工智能和机器学习技术进一步推动了¹⁸F-FDG PET/CT影像组学在恶性肿瘤预后研究的进展,有望更好地指导临床管理恶性肿瘤患者。本文对¹⁸F-FDG PET/CT影像组学用于评估常见恶性肿瘤预后进展进行综述。     

基于超声的影像组学应用于乳腺肿瘤进展

乳腺癌是威胁女性健康的常见恶性肿瘤。影像组学利用大数据及算法挖掘图像的微观定量特征,分析其与肿瘤性质、基因表达及预后等临床信息之间的关系。本文对基于超声的影像组学在乳腺肿瘤中的应用进展进行综述。     

直肠充盈对直肠壁CT影像组学特征的影响

目的:探讨直肠充盈对直肠壁CT影像组学特征的影响。方法:收集95例宫颈癌后装治疗定位CT扫描图像,患者在直肠填充苦参凝胶前后分别进行CT扫描,手动勾画直肠壁,计算提取7类共计851个特征,包括形态、统计、灰度相关矩阵、灰度游程矩阵、灰度共生矩阵、灰度区域矩阵及邻域灰度差分矩阵特征,采用一致性相关系数评估特征稳定性,威尔科克森符号秩检验分析直肠充盈对直肠壁CT影像组学特征的影响。结果:直肠充盈前后较稳定（一致性相关系数值小于0.8）的特征占总数的13%（113/851）,有显著性差异（P<0.05）的特征占总数的92%（782/851）。结论:直肠充盈对直肠壁CT影像组学特征影响较大,应予以关注。     

基于定量影像组学的乳腺肿瘤良恶性诊断

乳腺癌是女性致死率最高的恶性肿瘤之一。为提高诊断效率,提供给医生更加客观和准确的诊断结果。借助影像组学的方法,利用公开数据集BreaKHis中82例患者的乳腺肿瘤病理图像,提取乳腺肿瘤病理图像的灰度特征、Haralick纹理特征、局部二值模式(LBP)特征和Gabor特征共139维影像组学特征,并用主成分分析(PCA)对影像组学特征进行降维,然后利用随机森林(RF)、极限学习机(ELM)、支持向量机(SVM)、k最近邻(kNN)等4种不同的分类器构建乳腺肿瘤良恶性的诊断模型,并对上述不同的特征集进行评估。结果表明,基于支持向量机的影像组学特征的分类效果最好,准确率能达到88.2%,灵敏性达到86.62%,特异性达到89.82%。影像组学方法可为乳腺肿瘤良恶性预测提供一种新型的检测手段,使乳腺肿瘤良恶性临床诊断的准确率得到很大提升。