基于18F-FDG PET/CT影像组学的非小细胞肺癌病理亚型分类

目的：旨在建立一种基于¹⁸F-FDG PET/CT的临床—影像组学相结合的综合模型用于区分非小细胞肺癌中的腺癌和鳞癌。方法：回顾性收集上海交通大学附属胸科医院120例经病理学验证为腺癌(65例)和鳞癌(55例)的患者,从预处理的CT图像和PET图像中分别提取1218、108个影像组学特征,并纳入10个临床特征因素;卡方检验和Wilcoxon检验用于对临床特征进行筛选,并使用Relief算法和最小绝对收缩和选择算子(LASSO)对影像组学特征进行筛选;通过6种机器学习分类器分别建立临床、影像组学、综合模型。通过受试者工作特征(ROC)曲线及曲线下面积(AUC)来评价模型的分类能力。结果：综合模型在训练集和测试集中均表现出最高的AUC值和准确率,其中随机森林(RF)和Bagging分类器表现出的分类效果最佳。经五折交叉验证后,训练集中RF和Bagging的AUC值和准确率分别为0.92±0.03、0.86±0.06和0.92±0.02、0.83±0.02;测试集中RF和Bagging的AUC值和准确率分别为0.92、0.81和0.91、0.86。结论：结合^1...     

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。     

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.     

Radiomic Analysis of CT Predicts Tumor Response in Human Lung Cancer with Radiotherapy

PurposeRadiomics features can be positioned to monitor changes throughout treatment. In this study, we evaluated machine learning for predicting tumor response by analyzing CT images of lung cancer patients treated with radiotherapy.Experimental DesignFor this retrospective study, screening or standard diagnostic CT images were collected for 100 patients (mean age, 67 years; range, 55–82 years; 64 men [mean age, 68 years; range, 55–82 years] and 36 women [mean age, 65 years; range, 60–72 years]) from two institutions between 2013 and 2017. Radiomics analysis was available for each patient. Features were pruned to train machine learning classifiers with 50 patients, then trained in the test dataset.ResultA support vector machine classifier with 2 radiomic features (flatness and coefficient of variation) achieved an area under the receiver operating characteristic curve (AUC) of 0.91 on the test set.ConclusionThe 2 radiomic features, flatness, and coefficient of variation, from the volume of interest of lung tumor, can be the biomarkers for predicting tumor response at CT.     

Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics: a Preliminary Machine Learning Study

In this study, the ability of radiomics features extracted from myocardial perfusion imaging with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post-percutaneous coronary intervention (PCI) treatment. A total of 52 patients who had undergone pre-PCI MPI-SPECT were enrolled in this study. After normalization of the images, features were extracted from the left ventricle, initially automatically segmented by k-means and active contour methods, and finally edited and approved by an expert radiologist. More than 1700 2D and 3D radiomics features were extracted from each patient’s scan. A cross-combination of three feature selections and seven classifier methods was implemented. Three classes of no or dis-improvement (class 1), improved EF from 0 to 5% (class 2), and improved EF over 5% (class 3) were predicted by using tenfold cross-validation. Lastly, the models were evaluated based on accuracy, AUC, sensitivity, specificity, precision, and F-score. Neighborhood component analysis (NCA) selected the most predictive feature signatures, including Gabor, first-order, and NGTDM features. Among the classifiers, the best performance was achieved by the fine KNN classifier, which yielded mean accuracy, AUC, sensitivity, specificity, precision, and F-score of 0.84, 0.83, 0.75, 0.87, 0.78, and 0.76, respectively, in 100 iterations of classification, within the 52 patients with 10-fold cross-validation. The MPI-SPECT-based radiomic features are well suited for predicting post-revascularization EF and therefore provide a helpful approach for deciding on the most appropriate treatment.

     

基于多参数磁共振影像组学的乳腺癌病理信息预测模型研究

联合动态增强磁共振成像(DCE-MRI)、T2加权成像(T2WI)以及弥散加权成像(DWI)的影像特征,建立基于多参数影像组学的预测模型,分别对乳腺癌分子分型、组织学分级和Ki-67表达进行预测。采集150例术前、化疗前的浸润性导管癌患者乳腺MRI数据,获取DCE-MRI、T2WI和DWI影像。分割各参数影像的病灶区域,并提取多参数影像特征。在训练集采用支持向量机递归特征消除(SVM-RFE)算法,获得影像组学最优特征子集并构建基于SVM的预测模型,在测试集中测试模型性能。采用概率平均法、概率投票法和概率模型优化法,分别将基于不同参数影像构建的预测模型进行融合,得到多参数影像联合预测结果,并计算ROC曲线下的面积(AUC)评估模型的分类性能。单参数影像模型预测LuminalA、LuminalB、HER2和Basal-like等4种分子分型的最佳AUC分别为0.6721、0.6940、0.6777和0.7086,多参数影像模型的预测结果提高到AUC分别为0.7995、0.7279、0.7375和0.7925。单参数影像模型预测分级的最佳AUC为0.7533,多参数影像模型的预测结果提高到0.8017。单参数影像模型预测Ki-67表达的最佳AUC为0.6647,多参数影像模型预测结果提高到0.7718。相比于单参数影像模型的预测结果,多参数影像模型的预测结果有所提升,且差异具有显著性(P<0.05)。实验结果表明,采用多参数磁共振影像(DCE-MRI、T2WI以及DWI)组学的联合,可以显著提高单一参数影像模型预测乳腺癌病理信息的性能,对乳腺癌的诊断和个性化治疗方案的选择具有重要意义。     

基于MR T2加权成像的影像组学标签预测直肠癌KRAS基因突变的价值

目的 探讨基于MR T₂加权成像(T₂WI)的影像组学标签预测直肠癌KRAS基因突变的潜在价值。方法 回顾性研究。纳入山西省肿瘤医院2017年4月—2019年4月行盆腔MR检查并具有KRAS基因检测结果的304例直肠癌患者的临床和影像资料,其中男175例、女129例,中位年龄59.6岁。按7∶3比例将患者随机分为训练组(213例)和验证组(91例)。选取每例患者的高分辨率T₂WI进行图像分割及影像组学特征提取,使用单变量统计分析为主的“五步法”进行特征降维,并分别采用多变量logistic回归、决策树(DT)以及支持向量机(SVM)三种分类算法构建影像组学标签,用于预测直肠癌KRAS基因状态。受试者操作特征(ROC)曲线、校正曲线、决策曲线分析(DCA)评估影像组学标签的预测性能及临床效益。结果 训练组和验证组患者的基线资料比较以及两组中KRAS突变型与野生型患者的临床特征比较,差异均无统计学意义(P值均＞0.05)。从每位患者的T₂WI中提取960个影像组学特征,经特征筛选后得到7个与直肠癌KRAS基因相关的特征(P值均＜0.05)。采用多变量logistic回归、DT及SVM构建的三个预测模型的ROC曲线下面积,训练组分别为0.677、0.604和0.722,验证组分别为0.626、0.600和0.682,其中SVM模型在预测KRAS基因状态方面效能最好。DCA曲线示三种预测模型均有一定的临床效益,其中SVM预测模型净收益值最大。结论 基于MR T₂WI的影像组学标签在预测直肠癌KRAS基因状态方面有一定的价值。     

MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 − Early Breast Cancer Patients

Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 − invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 − breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10⁻³). When combining “early” and “peak” DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 − breast cancer patients.

     

基于CT的影像组学特征同临床物理剂量特征预测肺癌放疗放射性肺炎研究

目的:探讨基于CT的影像组学特征同临床物理剂量特征预测肺癌放疗放射性肺炎研究。方法:回顾性收集2013年1月至2017年1月进行放射治疗的83例肺癌患者的临床物理剂量参数和CT影像以及随访数据。从病例的CT图像中提取107个影像组学特征,结合对应的45个临床物理剂量特征,每例病例共收集152个特征。基于22种特征提取算法和8种分类器构建的176个鉴别模型分析152个特征预测放射性肺炎的准确性以及筛选优势特征的能力。结果:临床物理剂量特征和影像组学特征预测放射性肺炎的鉴别模型中AUC值最高为0.90。前5位的优势特征是:shape_Maximum2DDiameterColumn、shape_Maximum3DDiameter、V20、glcm_Imc1、V45。结论:临床物理剂量特征和影像组学特征通过不同分类器和特征选择算法组合的鉴别模型,可以筛选出理想的鉴别模型以及优势预测特征。     

Differential Diagnosis and Molecular Stratification of Gastrointestinal Stromal Tumors on CT Images Using a Radiomics Approach

Treatment planning of gastrointestinal stromal tumors (GISTs) includes distinguishing GISTs from other intra-abdominal tumors and GISTs’ molecular analysis. The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status, and mitotic index (MI). Patients diagnosed at the Erasmus MC between 2004 and 2017, with GIST or non-GIST intra-abdominal tumors and a contrast-enhanced venous-phase CT, were retrospectively included. Tumors were segmented, from which 564 image features were extracted. Prediction models were constructed using a combination of machine learning approaches. The evaluation was performed in a 100?×?random-split cross-validation. Model performance was compared to that of three radiologists. One hundred twenty-five GISTs and 122 non-GISTs were included. The GIST vs. non-GIST radiomics model had a mean area under the curve (AUC) of 0.77. Three radiologists had an AUC of 0.69, 0.76, and 0.84, respectively. The radiomics model had an AUC of 0.52 for c-KIT, 0.56 for c-KIT exon 11, and 0.52 for the MI. The numbers of PDGFRA, BRAF, and other c-KIT mutations were too low for analysis. Our radiomics model was able to distinguish GISTs from non-GISTs with a performance similar to three radiologists, but less observer dependent. Therefore, it may aid in the early diagnosis of GIST, facilitating rapid referral to specialized treatment centers. As the model was not able to predict any genetic or molecular features, it cannot aid in treatment planning yet.

     

Using different machine learning models to classify patients into mild and severe cases of COVID-19 based on multivariate blood testing

COVID-19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system. Using 38 blood test indicators on the first day of admission for the 422 patients diagnosed with COVID-19 (from January 2020 to June 2021) to construct different machine learning (ML) models to classify patients into either mild or severe cases of COVID-19. All models show good performance in the classification between COVID-19 patients into mild and severe disease. The area under the curve (AUC) of the random forest model is 0.89, the AUC of the naive Bayes model is 0.90, the AUC of the support vector machine model is 0.86, and the AUC of the KNN model is 0.78, the AUC of the Logistic regression model is 0.84, and the AUC of the artificial neural network model is 0.87, among which the naive Bayes model has the best performance. Different ML models can classify patients into mild and severe cases based on 38 blood test indicators taken on the first day of admission for patients diagnosed with COVID-19.     

Pre-operative Microvascular Invasion Prediction Using Multi-parametric Liver MRI Radiomics

Microvascular invasion (mVI) is the most significant independent predictor of recurrence for hepatocellular carcinoma (HCC), but its pre-operative assessment is challenging. In this study, we investigate the use of multi-parametric MRI radiomics to predict mVI status before surgery. We retrospectively collected pre-operative multi-parametric liver MRI scans for 99 patients who were diagnosed with HCC. These patients received surgery and pathology-confirmed diagnosis of mVI. We extracted radiomics features from manually segmented HCC regions and built machine learning classifiers to predict mVI status. We compared the performance of such classifiers when built on five MRI sequences used both individually and combined. We investigated the effects of using features extracted from the tumor region only, the peritumoral marginal region only, and the combination of the two. We used the area under the receiver operating characteristic curve (AUC) and accuracy as performance metrics. By combining features extracted from multiple MRI sequences, AUCs are 86.69%, 84.62%, and 84.19% when features are extracted from the tumor only, the peritumoral region only, and the combination of the two, respectively. For tumor-extracted features, the T2 sequence (AUC = 80.84%) and portal venous sequence (AUC = 79.22%) outperform other MRI sequences in single-sequence-based models and their combination yields the highest AUC of 86.69% for mVI status prediction. Our results show promise in predicting mVI from pre-operative liver MRI scans and indicate that information from multi-parametric MRI sequences is complementary in identifying mVI.     

An Effective Approach to Improve the Automatic Segmentation and Classification Accuracy of Brain Metastasis by Combining Multi-phase Delay Enhanced MR Images

The objective of this study is to analyse the diffusion rule of the contrast media in multi-phase delayed enhanced magnetic resonance (MR) T1 images using radiomics and to construct an automatic classification and segmentation model of brain metastases (BM) based on support vector machine (SVM) and Dpn-UNet. A total of 189 BM patients with 1047 metastases were enrolled. Contrast-enhanced MR images were obtained at 1, 3, 5, 10, 18, and 20 min following contrast medium injection. The tumour target volume was delineated, and the radiomics features were extracted and analysed. BM segmentation and classification models in the MR images with different enhancement phases were constructed using Dpn-UNet and SVM, and differences in the BM segmentation and classification models with different enhancement times were compared. (1) The signal intensity for BM decreased with time delay and peaked at 3 min. (2) Among the 144 optimal radiomics features, 22 showed strong correlation with time (highest R-value = 0.82), while 41 showed strong correlation with volume (highest R-value = 0.99). (3) The average dice similarity coefficients of both the training and test sets were the highest at 10 min for the automatic segmentation of BM, reaching 0.92 and 0.82, respectively. (4) The areas under the curve (AUCs) for the classification of BM pathology type applying single-phase MRI was the highest at 10 min, reaching 0.674. The AUC for the classification of BM by applying the six-phase image combination was the highest, reaching 0.9596, and improved by 42.3% compared with that by applying single-phase images at 10 min. The dynamic changes of contrast media diffusion in BM can be reflected by multi-phase delayed enhancement based on radiomics, which can more objectively reflect the pathological types and significantly improve the accuracy of BM segmentation and classification.

     

基于机器学习的鼻咽癌转移淋巴结鉴别模型

目的:研究使用机器学习与影像组学建立用于鼻咽癌CT图像中鉴别转移淋巴结的模型。方法:选择50例鼻咽癌患者初诊CT平扫及静脉灌注增强图像及18F-FGD-PET图像,患者均经病理及PET检查证实为鼻咽癌伴局部淋巴结转移。手动勾画患者CT图像中体积＞1 cm3的淋巴结,由18F-FGD-PET图像中对应区域SUVmax＞2.5及现行影像学标准作为转移与否的分类标准。研究中共获得143枚淋巴结,其中转移淋巴结103枚。使用机器学习方法对上述分类结果进行训练,其中列入训练组淋巴结100枚,验证组43枚,分组方式为随机分组以避免特定的分组方式造成的系统误差。结果:机器学习过程中获得由淋巴结体积、最大横截面短轴及数个影像组学特征构建模型,模型对转移淋巴结的鉴别准确率可达86%。特征选择结果得出:最大横截面直径、平均宽度、灰度强度能量、像素数量、频度、形态密实度等可作为诊断转移淋巴结的重要特征。结论:研究中建立的鉴别模型可在CT图像中实现辅助诊断转移淋巴结,为影像检查中快速判定鼻咽癌患者淋巴结是否转移提供一种新思路,有利于个体化放疗中靶区的精准勾画。     

原发性肝细胞癌的CT影像组学分析

目的:应用影像组学方法量化原发性肝细胞癌在CT增强扫描时“快进快出”的影像学表现。 方法:在平扫期、动脉期、门脉期上勾画肿瘤靶区（GTV）和部分正常肝脏组织,提取所勾画靶区的特征值,量化GTV以及正常肝脏组织在不同时相上影像组学特征值的差异。 结果:共提取55个特征,正常肝脏组织和GTV在平扫期与动脉期、平扫期与门脉期、动脉期与门脉期所提取的特征差异具有统计学意义的分别有7、8、22个和35、41、33个;GTV与正常肝脏组织在平扫期差异具有统计学意义的特征有49个,动脉期46个,门脉期38个;有6个特征与 “快进快出”现象有关。 结论:基于影像组学技术量化不同强化时机肝细胞癌和正常肝脏组织的特征,为追踪肝细胞癌肿瘤异质性及动态变化提供有效的手段。     

多参数MRI的影像组学融合模型在术前预测宫颈癌淋巴结转移的应用价值

目的 探讨基于多参数MRI及临床特征的融合模型在术前预测宫颈癌患者淋巴结转移的价值。方法 回顾性分析山西省肿瘤医院2016年6月-2019年3月经病理证实为宫颈鳞癌并于术前行MRI检查的168例患者的资料。按照7∶3的比例,采用完全随机法将所有患者分为训练组115例和验证组53例。由两名影像科医师在MRI图像上手动勾画三维容积感兴趣区(VOI),并进行一致性分析。根据临床手术病理结果将所有患者分为淋巴结转移阴性(LN-)和阳性(LN+),临床及影像资料也对应分组。分别基于每例患者的T₂WI、表观扩散系数(ADC)和增强T₁WI(cT₁WI)序列图像上均提取3 111个影像组学特征,然后对训练组采用以最大相关最小冗余(MRMR)和最小绝对收缩与选择(LASSO)回归为主的四步法进行特征选择和影像组学标签的构建,并进行分层分析。通过多变量逻辑回归筛选独立临床危险因素并联合影像组学标签构建影像组学融合模型,并制作列线图。采用ROC曲线、校正曲线、决策分析曲线(DCA)评估列线图的预测性能及临床效益。结果 训练组和验证组患者基线资料差异均无统计学意义(P值均>0.05)。基于T₂WI、ADC和cT₁WI合并特征降维后共得到6个影像组学特征(P值均＜0.05),其中包括3个小波类特征参数和3个LoG类特征参数,均与淋巴结转移显著相关。单序列影像组学标签在训练组中ROC曲线下面积(AUC)值为0.763和0.829,显示具有良好的预测效能,合并上述序列构建的影像组学标签对应的AUC值0.859,其诊断效能优于其中任意单一序列,并在验证组得到验证。联合影像组学标签和MRI评价淋巴结状态构建的列线图在训练组和验证组中均显示出良好的鉴别能力和校正性能,对应的AUC分别为 0.865和0.861。在独立验证组中的决策曲线示,当风险阈值＞10%时,采用影像组学方法预测LN+的净收益优于将所有患者都看作LN+或LN-,也优于MRI评价淋巴结状态。结论 通过联合基于多参数MRI的影像组学标签和MRI评价淋巴结状态建立的融合模型可作为术前评估宫颈癌淋巴结转移的一种辅助方法。     

Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project

Background

Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality).

Methods

We use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used.

Results

Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC?=?0.97) among all models trained using the SMOTE sampling.

Conclusions

The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness data.

     

18F-FDG PET/CT与CT诊断胃癌淋巴结转移直接比较的系统评价

目的 采用Meta分析和直接比较方法系统评价18F-FDG PET/CT与CT对胃癌淋巴结转移的诊断价值.方法 使用计算机系统检索中国期刊全文数据库、中文科技期刊数据库、万方数据库、PubMed、Embase、The Cochrane Library,从建库至2016年11月,搜索直接比较18F-FDG PET/CT与CT诊断胃癌淋巴结转移的诊断性比较试验.用Meta-Disc1.4软件进行分析,计算两种影像学诊断方法的合并灵敏度(sensitivity,SEN)、合并特异性(specificity,SPE)、合并阳性似然比(positive likelihood ratio,+LR)、合并阴性似然比(negative likelihood ratio,-LR),诊断优势比(diagnostic OR,DOR),并绘制SROC (summary receiver operating characteristic)曲线,计算曲线下面积(area under curve,AUG).结果 最终共纳入9篇文章,Meta分析结果显示,18F-FDG PET/CT对胃癌淋巴结转移诊断的合并SEN为0.51(95％ CI =0.47～0.55),合并SPE为0.92(95％ CI =0.89 ～0.94),合并+LR为5.77(95％ CI =4.38 ～7.59),合并-LR为0.54(95％ CI =0.45 ～0.64),DOR为12.71(95％ CI =8.97～ 18.01),AUC为0.8101.CT诊断的合并SEN为0.71(95％ CI=0.67～ 0.74),合并SPE为0.82(95％ CI =0.78 ～0.84),合并+LR为3.52(95％ CI=2.52 ～4.93),合并-LR为0.37(95％CI=0.32～0.44),DOR为10.73(95％ CI =7.35 ～ 15.66),AUC为0.8176.结论 18F-FDG PET/CT显像诊断胃癌淋巴结转移的灵敏度比CT低,但其特异性较好,有更高的诊断价值,可作为胃癌淋巴结转移的临床诊断方法之一.     

基于DCE-MRI影像组学非负矩阵分解的乳腺癌病理信息缺失填充研究

乳腺癌病理报告是乳腺癌诊断和治疗的主要依据,在实际诊疗过程中可能存在临床病理信息缺失的问题.利用动态增强磁共振影像(DCE-MRI)病灶区域的影像特征,结合对应乳腺癌患者的临床病理信息,建立影像组学非负矩阵分解填充模型,以实现对缺失的乳腺癌分子分型和细胞角蛋白5/6(CK5/6)基因表达信息的填充.共采集139例乳腺癌...     

基于影像组学的脑脊液细胞分类方法

目的：基于影像组学构建出更为高效、准确的脑脊液细胞判别模型。方法：回顾性收集3331张脑脊液细胞显微图像,其中吞噬细胞167张、单核细胞332张、淋巴细胞1081张、中性粒细胞1751张。首先在显微图像上分割出细胞核、细胞核凸包区域和细胞核凸包区域的部分细胞质,然后设计3种细胞核形状特征,即圆度、凸度、坚固性。针对细胞核、凸包区域和凸包区域的部分细胞质设计48种颜色特征。基于细胞核凸包区域提取4 676种纹理特征。结果：共提取了4 727个影像组学特征,在经过ANOVA和LASSO特征选择之后,保留了519个特征,且形状特征和颜色特征都得到了较高比例的保留（100.0%, 66.7%）。特征选择之后,利用SMOTE数据增强和SVM分类器在测试集上进行预测,各项评价指标Accuracy、Sensitivity、Specificity、Precision、F1＿score、AUC高达0.953、0.948、0.990、0.961、0.955、0.996。结论：本文提出的新的细胞显微图像特征提取方案和分类模型对细胞分类问题非常有效,且避免了细胞质分割的难题,无需分割细胞,只需分割细胞核和细胞...