Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network–based US radiomics model

To develop a machine learning–based ultrasound (US) radiomics model for predicting tumour deposits (TDs) preoperatively. From December 2015 to December 2017, 127 patients with rectal cancer were prospectively enrolled and divided into training and validation sets. Endorectal ultrasound (ERUS) and shear-wave elastography (SWE) examinations were conducted for each patient. A total of 4176 US radiomics features were extracted for each patient. After the reduction and selection of US radiomics features , a predictive model using an artificial neural network (ANN) was constructed in the training set. Furthermore, two models (one incorporating clinical information and one based on MRI radiomics) were developed. These models were validated by assessing their diagnostic performance and comparing the areas under the curve (AUCs) in the validation set. The training and validation sets included 29 (33.3%) and 11 (27.5%) patients with TDs, respectively. A US radiomics ANN model was constructed. The model for predicting TDs showed an accuracy of 75.0% in the validation cohort. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and AUC were 72.7%, 75.9%, 53.3%, 88.0% and 0.743, respectively. For the model incorporating clinical information, the AUC improved to 0.795. Although the AUC of the US radiomics model was improved compared with that of the MRI radiomics model (0.916 vs. 0.872) in the 90 patients with both ultrasound and MRI data (which included both the training and validation sets), the difference was nonsignificant (p = 0.384). US radiomics may be a potential model to accurately predict TDs before therapy. • We prospectively developed an artificial neural network model for predicting tumour deposits based on US radiomics that had an accuracy of 75.0%. • The area under the curve of the US radiomics model was improved than that of the MRI radiomics model (0.916 vs. 0.872), but the difference was not significant (p = 0.384). • The US radiomics–based model may potentially predict TDs accurately before therapy, but this model needs further validation with larger samples.     

基于MR T2WI的影像组学对直肠癌新辅助治疗疗效的评估

目的:探讨基于高分辨T2WI的影像组学模型对评估直肠癌新辅助治疗疗效的价值。方法:回顾性分析2018年1月-2018年12月经手术病理证实且在接受新辅助治疗前、后均行MRI检查的80例直肠癌患者的病例资料。根据术后病理检查确定的肿瘤退缩分级(TRG),将TRG为0、1级者纳入疗效良好组,2、3级者纳入疗效不良组。在高分辨T2WI上勾画病灶的三维容积兴趣区(VOI)并使用两种模型提取影像组学特征,模型1:仅提取治疗前基线影像组学特征;模型B2:提取基线和治疗后的影像组学特征。随机选取70%的病例作为训练集,30%的病例作为测试集进行验证。对两种模型分别利用LASSO(least absolute shrinkage and selection operator)算法进行特征降维后,与TRG标签建立随机森林(RF)分类器,并分别进行受试者操作特征(ROC)曲线分析,比较两种模型的曲线下面积(AUC)并分析其诊断效能(敏感度、特异度、准确度、阳性预测值、阴性预测值、阳性似然比、阴性似然比)。采用决策曲线分析(DCA)评估临床获益。结果:模型1经降维后得到28个组学特征,模型2共获得3个组学特征,分别建立RF分类器模型,ROC曲线分析得到测试集模型1、2的AUC分别为0.943和0.950,两者间的差异无统计学意义(P>0.05)。模型1的特异度及阳性似然比较高,模型B的敏感度及阴性似然比较高。DCA显示总体上两种方法均可以临床获益。结论:基于治疗前及综合治疗前、后MR T2WI高分辨率图像的影像组学模型均可较准确地预测直肠癌新辅助治疗后的肿瘤退缩程度,可应用于临床上对直肠癌新辅助治疗疗效的评估。     

CT影像组学对中轴骨脊索瘤与骨巨细胞瘤的鉴别诊断价值

目的:建立术前鉴别中轴骨脊索瘤与骨巨细胞瘤的影像组学模型,并验证其诊断效能.方法:回顾性纳入中轴骨脊索瘤59例、骨巨细胞瘤33例共92例患者,64例为训练集,28例为验证集.基于CT图像进行影像组学特征提取,采用LASSO模型进行特征选择,构建影像组学模型,并计算影像组学得分(Rad-score).通过Logistic...     

Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection

To identify a CT-based radiomics nomogram for survival prediction in patients with resected pancreatic ductal adenocarcinoma (PDAC). A total of 220 patients (training cohort n = 147; validation cohort n = 73) with PDAC were enrolled. A total of 300 radiomics features were extracted from CT images. And the least absolute shrinkage and selection operator algorithm were applied to select features and develop a radiomics score (Rad-score). The radiomics nomogram was constructed by multivariate regression analysis. Nomogram discrimination, calibration, and clinical usefulness were evaluated. The association of the Rad-score and recurrence pattern in PDAC was evaluated. The Rad-score was significantly associated with PDAC patient’s disease-free survival (DFS) and overall survival (OS) (both p < 0.001 in two cohorts). Incorporating the Rad-score into the radiomics nomogram resulted in better performance of the survival prediction than that of the clinical model and TNM staging system. In addition, the radiomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts. There was no association between the Rad-score and recurrence pattern. The radiomics nomogram integrating the Rad-score and clinical data provided better prognostic prediction in resected PDAC patients, which may hold great potential for guiding personalized care for these patients. The Rad-score was not a predictor of the recurrence pattern in resected PDAC patients. • The Rad-score developed by CT radiomics features was significantly associated with PDAC patients’ prognosis. • The radiomics nomogram integrating the Rad-score and clinical data has value to permit non-invasive, low-cost, and personalized evaluation of prognosis in PDAC patients. • The radiomics nomogram outperformed clinical model and the TNM staging system in terms of survival estimation.     

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

To create a radiomics approach based on multiparametric magnetic resonance imaging (mpMRI) features extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant (CS) peripheral zone (PZ) prostate cancer (PCa). This study included 206 patients with 262 prospectively called mpMRI prostate imaging reporting and data system 3–5 PZ lesions. Gleason scores > 6 were defined as CS PCa. Features were extracted with an auto-fixed 12-mm spherical VOI placed around a pin point in each lesion. The value of dynamic contrast-enhanced imaging(DCE), multivariate feature selection and extreme gradient boosting (XGB) vs. univariate feature selection and random forest (RF), expert-based feature pre-selection, and the addition of image filters was investigated using the training (171 lesions) and test (91 lesions) datasets. The best model with features from T2-weighted (T2-w) + diffusion-weighted imaging (DWI) + DCE had an area under the curve (AUC) of 0.870 (95% CI 0.980–0.754). Removal of DCE features decreased AUC to 0.816 (95% CI 0.920–0.710), although not significantly (p = 0.119). Multivariate and XGB outperformed univariate and RF (p = 0.028). Expert-based feature pre-selection and image filters had no significant contribution. The phenotype of CS PZ PCa lesions can be quantified using a radiomics approach based on features extracted from T2-w + DWI using an auto-fixed VOI. Although DCE features improve diagnostic performance, this is not statistically significant. Multivariate feature selection and XGB should be preferred over univariate feature selection and RF. The developed model may be a valuable addition to traditional visual assessment in diagnosing CS PZ PCa. • T2-weighted and diffusion-weighted imaging features are essential components of a radiomics model for clinically significant prostate cancer; addition of dynamic contrast-enhanced imaging does not significantly improve diagnostic performance. • Multivariate feature selection and extreme gradient outperform univariate feature selection and random forest. • The developed radiomics model that extracts multiparametric MRI features with an auto-fixed volume of interest may be a valuable addition to visual assessment in diagnosing clinically significant prostate cancer.     

MRI影像组学对早期口腔舌鳞状细胞癌隐匿性颈淋巴结转移的预测价值

目的:探讨基于术前常规MRI的影像组学对早期口腔舌鳞状细胞癌（OTSCC）隐匿性颈淋巴结转移的预测价值。方法:回顾性收集2015年1月至2019年12月上海交通大学医学院附属第九人民医院77例早期OTSCC患者（临床分期cT1~2N0M0）的术前MRI数据。所有患者均行原发灶切除及选择性颈淋巴结清扫术,且具有术后淋巴结...     

治疗前CT影像组学结合机器学习预测非小细胞肺癌患者EGFR突变亚型

目的探讨基于治疗前胸部平扫CT影像组学特征和临床特征结合机器学习算法预测非小细胞肺癌(NSCLC)患者表皮生长因子受体(EGFR)突变状态和突变亚型(19Del/21L858R)的可行性和价值。方法回顾性分析南华大学附属第一医院和附属第二医院经活检病理证实和接受EGFR基因检测的280例NSCLC患者的治疗前胸部平扫CT和临床特征数据, 其中EFGR突变患者为136例。由两位高年资影像和肿瘤医师勾画原发肺部大体肿瘤区域(GTV), 然后提取851个影像组学特征, 采用Spearman相关分析和RELIEFF算法筛选具有预测性的特征, 两家医院分别为训练组和验证组。经特征选择的影像组学特征和临床特征构建临床-影像组学模型, 并与单独采用影像组学特征和临床特征模型进行比较。采用序贯建模流程, 使用支持向量机(SVM)建立机器学习模型预测EGFR突变状态和突变亚型。受试者工作曲线下面积(AUC-ROC)评估预测模型的诊断效能。结果经特征筛选各有21个影像组学特征在预测EGFR突变和突变亚型时具有预测效能并用于建立影像组学模型。临床-影像组学模型表现出最好的预测效能, 预测EGFR突变状态的模...     

A preoperative radiomics model for the identification of lymph node metastasis in patients with early-stage cervical squamous cell carcinoma

Objectives:To develop and validate a radiomics model for preoperative identification of lymph node metastasis (LNM) in patients with early-stage cervical squamous cell carcinoma (CSCC).Methods:Total of 190 eligible patients were randomly divided into training (n = 100) and validation (n = 90) cohorts. Handcrafted features and deep-learning features were extracted from T2W fat suppression images. The minimum redundancy maximum relevance algorithm and LASSO regression with 10-fold cross-validation were used for key features selection. A radiomics model that incorporated the handcrafted-signature, deep-signature, and squamous cell carcinoma antigen (SCC-Ag) levels was developed by logistic regression. The model performance was assessed and validated with respect to its calibration, discrimination, and clinical usefulness.Results:Three handcrafted features and three deep-learning features were selected and used to build handcrafted- and deep-signature. The model, which incorporated the handcrafted-signature, deep-signature, and SCC-Ag, showed satisfactory calibration and discrimination in the training cohort (AUC: 0.852, 95% CI: 0.761–0.943) and the validation cohort (AUC: 0.815, 95% CI: 0.711–0.919). Decision curve analysis indicated the clinical usefulness of the radiomics model. The radiomics model yielded greater AUCs than either the radiomics signature (AUC = 0.806 and 0.779, respectively) or the SCC-Ag (AUC = 0.735 and 0.688, respectively) alone in both the training and validation cohorts.Conclusion:The presented radiomics model can be used for preoperative identification of LNM in patients with early-stage CSCC. Its performance outperforms that of SCC-Ag level analysis alone.Advances in knowledge:A radiomics model incorporated radiomics signature and SCC-Ag levels demonstrated good performance in identifying LNM in patients with early-stage CSCC.     

基于MRI征象及影像组学的列线图预测腮腺良性与恶性肿瘤的价值

目的建立并验证基于MRI征象和影像组学的列线图鉴别腮腺良性与恶性肿瘤的效能。方法回顾性收集2015年1月至2020年5月青岛大学附属医院86例经手术病理证实的腮腺肿瘤患者为训练集,收集2013年1月至2020年1月香港大学深圳医院35例患者为独立外部验证集。采用logistic回归基于临床及MRI征象建立临床诊断模型。基于术前平扫T1WI和预饱和脂肪抑制T2WI(fs-T2WI)进行影像组学特征提取,建立影像组学诊断模型。基于影像组学评分及临床诊断模型,通过logistic回归建立影像组学+临床联合诊断模型及列线图。采用受试者操作特征(ROC)曲线评价各模型诊断腮腺良性与恶性肿瘤的效能,ROC曲线下面积(AUC)的比较采用DeLong检验。结果Logistic回归结果显示,腮腺深叶受累(OR值为3.285,P=0.040)和周围组织结构侵犯(OR值为15.919,P=0.013)是腮腺恶性肿瘤的独立影响因素,将二者构建临床诊断模型。基于平扫T1WI和fs-T2WI,共提取19个特征构建影像组学诊断模型。联合影像组学评分以及腮腺深叶受累、周围组织结构侵犯2个常规影像学特征建立联合诊断模型及列线图。临床诊断模型、影像组学诊断模型、联合诊断模型在训练集和验证集中诊断腮腺良性与恶性肿瘤的AUC分别为0.758、0.951、0.953和0.752、0.941、0.964。在训练集和验证集中,影像组学诊断模型、联合诊断模型的AUC均高于临床诊断模型(训练集:Z=3.95、4.31,P均<0.001;验证集:Z=2.16、2.67,P=0.031、0.008),影像组学诊断模型、联合诊断模型间AUC差异无统计学意义(训练集:Z=0.39,P=0.697;验证集:Z=1.10,P=0.273)。结论本研究所建立的MRI影像组学模型以及由腮腺深叶受累、周围组织结构侵犯、MRI影像组学特征组成的联合诊断模型,能有效鉴别腮腺良恶性肿瘤,具有较高的预测效能。     

基于乳腺X线图像影像组学列线图对乳腺癌腋窝淋巴结转移的预测价值

目的:探讨基于乳腺X线图像影像组学列线图对乳腺癌腋窝淋巴结(ALN)转移的预测价值.方法:回顾性分析188例乳腺癌患者的乳腺X线图像和临床资料,按照7:3的比例将患者随机分割为训练组(n=130)和验证组(n=58).使用MaZda软件在乳腺X线图像内提取影像组学特征,应用方差选择法和最小绝对收缩与选择算子算法(LAS...     

CT影像组学预测局部进展期直肠癌新辅助治疗的效果

目的探讨CT影像组学在治疗前预测局部进展期直肠癌新辅助治疗效果的价值。资料与方法回顾性分析168例新辅助治疗后行根治术的局部进展期直肠癌患者,收集治疗前临床及CT资料,根据术后病理肿瘤退缩分级分组。采用A.K.软件提取CT影像组学特征并构建影像组学标签。通过多变量Logistic回归筛选疗效预测因子并构建诺莫图模型。利用ROC曲线评价模型诊断效能,并对模型进行内部验证、校准度评价及临床应用价值分析。结果每例患者各提取了396个CT影像组学特征,降维后筛选出6个与局部进展期直肠癌新辅助治疗效果高度相关的特征。联合独立预测因子影像组学标签、癌胚抗原≥3.4 ng/ml和临床T分期(cT4)构建的诺莫图模型ROC曲线下面积(0.881)高于影像组学标签(0.791),且具有较高的校准度、内部验证一致性及临床应用价值(P>0.05)。结论基于治疗前CT及临床资料构建的模型对局部进展期直肠癌新辅助治疗效果预测具有较高的预测效能,且联合预测模型的预测效能优于影像组学标签。     

基于MR T1WI的影像组学机器学习模型预测软组织肉瘤分级的价值

目的 探讨基于MR T1WI的最优影像组学机器学习模型及其预测软组织肉瘤分级的价值。方法 回顾性分析2009年5月至2018年11月青岛大学附属医院113例软组织肉瘤患者的术前MR T1WI资料,采用随机分层抽样的方法将患者随机分为训练组(n=80)和验证组(n=33)。根据法国国家癌症研究中心(FNCLCC)系统将软组织肉瘤病理分级分为Ⅰ~Ⅲ三个级别。Ⅰ级为低级别,Ⅱ、Ⅲ级为高级别。训练组中18例为低级别、62例为高级别病变,验证组中7例低级别、26例高级别病变。图像进行标准化后,采用A.K软件对肿瘤感兴趣区进行特征提取,并基于不同特征选择方法(加入和不加入递归式特征消除)、机器学习算法(随机森林和支持向量机算法)和采样技术(不进行过采样、使用少数样本合成过采样技术、使用随机过采样技术),组合成12种机器学习算法组合,应用弃一法交叉验证进行验证,建立分类模型。采用受试者操作特征(ROC)曲线评价模型预测软组织肉瘤病理级别的效能。结果 在12种机器学习算法建立的软组织肉瘤分级预测模型中,联合使用递归式特征消除和少数样本合成过采样技术的随机森林分类算法效能最佳,其在验证组中预测软组织肉瘤分级的ROC曲线下面积为0.909 (95%可信区间为0.808~1.000),准确率、灵敏度和特异度分别为84.85%、86.21%和75.00%。结论 基于影像组学的机器学习方法在预测软组织肉瘤病理分级方面有较大的应用价值。     

Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke

ObjectiveTo develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes.Materials and MethodsData from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses.ResultsAge, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825–0.910) in the training cohort and 0.890 (0.844–0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness.ConclusionThe clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.     

Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning

ObjectiveWe aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms.Materials and MethodsWe retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score.ResultsThe radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869–0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815–0.948] and 0.899 [95% CI, 0.820–0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all).ConclusionModels based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.     

基于MRI的机器学习模型预测PI-RADS 3病变的良恶性及侵袭性

目的 构建基于MRI影像组学特征的机器学习模型,以预测PI-RADS 3前列腺病变的良恶性及侵袭性。 方法 回顾性分析296例PI-RADS 3前列腺病变病人的MRI影像资料。其中,PCa病人141例,非PCa病人155例。并将病人以7∶3的比例随机划分为训练集和独立验证集。由2名医师使用3D Slicer软件在T₂WI、DWI、DCE-T₁WI上手动勾画病变,并分别提取影像组学特征。采用组内相关系数（ICC）评估2名医师病变勾画的稳定性。采用t检验和最小绝对值收敛和选择算子（LASSO）算法进行特征筛选。使用支持向量机（SVM）算法分别构建前列腺病变良恶性预测模型及PCa侵袭性预测模型。采用Mann-Whitney U检验比较2组间前列腺特异性抗原（PSA）水平。采用受试者操作特征（ROC）曲线下面积（AUC）、准确度、敏感度和特异度评估模型的预测效能。 结果 141例PCa病人的PSA水平高于非PCa病人（P<0.05）。141例PCa病人中,临床有意义PCa（csPCa）100例,临床无意义PCa（ciPCa）41例。csPCa病人的PSA水平也高于ciPCa病人（P<0.05）。共分析296个病灶,每个病灶均提取2 553个影像组学特征。2名医师对病变勾画均具有良好的一致性（ICC：组间0.81,组内0.84）。在前列腺病变的良恶性预测中,训练集207个病灶,验证集89个病灶,最终筛选出14个特征（9个DWI、3个T₂WI、2个DCE特征）,构建的预测模型在训练集中的AUC、准确度、敏感度和特异度分别为0.93（95%CI：0.91~0.95）、0.82、0.78、0.85,在独立验证集中分别为0.89（95%CI：0.86~0.92）、0.81、0.86、0.77;在PCa的侵袭性预测中,训练集98个病灶,验证集43个病灶,最终筛选出12个特征（5个DWI,4个T₂WI,3个DCE特征）,构建的预测模型在训练集中的AUC、准确度、敏感度和特异度分别为0.92（95%CI：0.89~0.94）、0.85、0.87、0.84,在独立验证集中分别为0.85（95%CI：0.81~0.89）0.72、0.73、0.70。 结论 基于MRI影像组学特征的机器学习模型能有效预测PI-RADS 3前列腺病变的良恶性及侵袭性。     

超声影像组学标签预测三阴性乳腺癌的价值

目的 探讨基于常规超声的影像组学标签在术前诊断三阴性乳腺癌(TNBC)的价值.资料与方法 回顾性连续收集230例经手术病理证实的肿块型浸润性乳腺癌患者的临床资料和术前超声图像,按照1:2随机抽样选取TNBC与非TNBC共102例患者纳入本研究.按超声检查时间顺序,将患者分为训练组66例和验证组36例.通过ImageJ软...     

基于薄层CT的影像组学和形态学特征联合模型在预测磨玻璃样肺腺癌浸润程度的价值

目的 探讨基于薄层CT的影像组学和形态学特征联合模型在预测磨玻璃样肺腺癌中的原位癌(AIS)、微浸润腺癌(MIA)、浸润性腺癌(IAC)的价值.方法 回顾性分析2018年6月至2021月3月经病理证实的327例肺腺癌患者(335个病灶)术前肺部CT检查图像,随机选取201个为训练集,134个为测试集.肺腺癌参照病理金标...     

基于增强CT影像组学列线图预测单发肝细胞癌GPC3表达状态

目的 探讨增强CT影像组学列线图在鉴别单发肝细胞癌（HCC）磷脂酰肌醇蛋白聚糖3（GPC3）表达中的价值。方法 回顾性收集来自2个医疗机构共152例单发HCC病人的临床及影像资料,所有病人均行上腹部增强CT扫描并记录GPC3表达水平。天津市第一中心医院的106例病人资料作为训练集（GPC3阳性83例、阴性23例）,天津医科大学肿瘤医院的46例病人资料作为验证集（GPC3阳性35例、阴性11例）。对所有病人术前1个月内增强CT影像进行影像组学特征提取。在训练集中,对所有影像组学特征进行降维并得到最优子集,计算影像组学评分（Radscore）;比较GPC3阳性组和阴性组间临床资料[包括血清甲胎蛋白（AFP）、糖类抗原199（CA199）等]的差异,将差异有统计学意义的指标进行二元logistic回归分析,获得GPC3阳性的独立预测因素。将获得的临床信息及Radscore分别建立临床列线图、影像组学列线图及联合列线图。采用受试者操作特征曲线下面积（AUC）分析各列线图对GPC3表达状态的预测能力,采用DeLong检验比较各列线图间的诊断效能,并用决策曲线分析评估列线图的临床价值。使用验证集数据对列线图预测效能进行验证。结果 二元logistic回归显示血清AFP、CA199、Radscore是GPC3阳性的独立危险因素[优势比（OR）分别为8.503、1.090、13 300.044,均P<0.05]。校准曲线显示联合列线图对GPC3阳性表达的预测概率与实际概率一致性良好。训练集中,联合列线图的AUC（0.918）高于影像组学列线图（0.842）和临床列线图（0.787）（均P<0.05）,联合列线图的敏感度最高,而临床列线图的特异度最高;验证集中,联合列线图的AUC（0.896）高于影像组学列线图（0.726）和临床列线图（0.803）（均P<0.05）,联合列线图的敏感度和特异度均最高。决策曲线分析显示当阈值概率处于16%~86%时,联合列线图的临床净获益高于临床列线图和影像组学列线图。结论 基于增强CT的影像组学列线图可以术前鉴别单发HCC GPC3阳性和阴性表达,联合列线图进一步提高了预测效能。     

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...