Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

基于CT平扫纹理分析的影像组学预测肾透明细胞癌WHO/ISUP分级的初步研究

目的:探讨基于肾脏CT平扫图像纹理分析的影像组学模型在预测肾透明细胞癌（ccRCC）WHO/国际泌尿病理学会（ISUP）病理分级中的价值。方法:回顾性分析2016年12月至2019年5月中国科学院大学附属肿瘤医院经手术病理证实且有明确病理分级的90例ccRCC患者,按照7∶3的比例将所有患者随机分为训练组（63例）及测...     

医学影像纹理分析在软组织肿瘤中的研究进展

近年来凭借大数据及深度学习使得以纹理分析(Texture Analysis)为代表的影像组学迅猛发展。随着纹理分析方法及影像新技术的不断研发应用,近年来纹理分析被越来越多的应用于临床研究,为疾病、尤其是肿瘤的诊断、分级与疗效预测评估提供有价值的医学影像依据。本文总结了纹理分析的常用方法、流程,并对软组织肿瘤的研究现况进行调查,作一综述。     

CBCT影像组学联合构建Nomogram模型预测食管癌放疗患者放射性肺炎

目的:通过对放疗疗程中不同时段CBCT图像的影像组学分析,寻找早期定量预测食管癌放疗放射性肺炎(RP)的参数,结合临床特征和肺剂量体积参数建立联合Nomogram模型并探讨这一模型对食管癌RP的预测价值。方法:回顾分析2017—2019年间临床资料、剂量学参数、CBCT图像资料完整的96例胸中段食管鳞癌调强放疗患者资料...     

120例胶质瘤及瘤周水肿MRI影像组学在评估肿瘤复发中的价值

目的 探讨胶质瘤及瘤周水肿(PTE)MRI影像组学在评估肿瘤复发中的价值。 方法 选取山东大学齐鲁医院2013年1月至2020年12月经术后病理证实的胶质瘤患者120例,包括55例复发和65例无复发患者,根据术前T₂WI和T₁WI增强图像对肿瘤和PTE进行三维容积感兴趣区勾画,并按照8∶2的比例分为训练组和验证组,分析两者及联合的组学特征与肿瘤复发的关系。使用受试者工作特征(ROC)曲线下面积(AUC)与准确性矩阵,比较和评价不同影像组学模型的训练结果。 结果 对于PTE,K临近法(KNN)分类器预测效能最好:训练组AUC值、敏感度、特异度分别为0.910、0.84、0.88,验证组分别为0.916、0.82、0.93。对于肿瘤,逻辑回归(LR)分类器预测效能最好:训练组AUC值、敏感度和特异度分别为0.777、0.69、0.67,验证组分别为0.758、0.82、0.92。当肿瘤+PTE联合时,逻辑回归(LR)分类器预测效能最好:训练组AUC值、敏感度、特异度为0.977、0.88、0.89,验证组则为0.841、0.73、0.83。 结论 胶质瘤PTE和肿瘤影像组学特征在预测胶质瘤术后复发方面具有一定的价值,其中PTE的KNN组学模型效能最佳。     

基于MRI影像组学鉴别卵巢卵泡膜细胞瘤与阔韧带肌瘤

目的 探讨基于MRI影像组学对卵巢卵泡膜细胞瘤（OTCA）与阔韧带肌瘤（BLM）的鉴别诊断价值。资料与方法 回顾性分析安阳市肿瘤医院2016年1月—2021年3月经病理证实的76例OTCA和58例BLM的MRI图像,比较两组疾病的MRI特征。于肿瘤最大层面勾画感兴趣区提取T2WI脂肪抑制序列图像纹理特征,采用分层抽样方式按照7∶3分为训练组104例和测试组30例,根据病理结果分为OTCA亚组和BLM亚组。基于训练组,使用最小绝对收缩和选择算子回归分析筛选关键特征,根据回归模型中变量的回归系数,建立线性方程计算影像组学标签评分。采用受试者工作特征（ROC）曲线评价基于MRI图像特征、影像组学及其组合区分两种疾病的能力。结果 共4个MRI特征为鉴别两组疾病的独立特征,包括同侧卵巢可见性（χ2=5.503,P<0.05）、外周囊性区（χ2=7.693,P<0.05）、动脉期强化程度（P<0.05）及表观扩散系数（t=3.310,P<0.05）;训练组和测试组OTCA、BLM亚组的影像组学标签评分比较,差异均有统计学意义（P<0.05）。联合MRI图像特征和影像组...     

基于动态增强MRI影像组学评分和激素受体的列线图预测乳腺癌新辅助化疗不敏感的价值

目的 探讨基于动态增强MRI（DCE-MRI）影像组学评分（Radscore）和激素受体状态的列线图预测乳腺癌新辅助化疗（NAC）疗效不敏感的价值。 方法 回顾性收集128例行乳腺癌NAC治疗的女性病人,平均年龄（49.2±10.0）岁。128例病人按照7∶3比例随机分为训练集90例（疗效敏感者47例,疗效不敏感者43例）和测试集38例（疗效敏感者15例,疗效不敏感者23例）。基于DCE-MRI影像提取并筛选影像组学特征,采用多因素逻辑回归构建影像组学模型并计算模型的Radscore。采用t检验、χ²检验或Fisher确切概率检验比较训练集和测试集中临床病理指标[年龄、雌激素受体（ER）、孕激素受体（PR）、人表皮生长因子受体-2（HER-2）和肿瘤增殖细胞核抗原-67（Ki-67）],将差异有统计学意义的临床病理指标和Radscore纳入多因素逻辑回归,建立联合模型和列线图。应用受试者操作特征（ROC）曲线下面积（AUC）评价影像组学模型和联合模型的预测效能。应用决策曲线评估影像组学模型和联合模型的临床应用价值。 结果 在训练集中,ER和PR在疗效敏感与不敏感组间的差异均有统计学意义（均P<0.05）,但未得到测试集的验证（均P>0.05）。在训练集中,联合模型预测NAC不敏感的AUC值和准确度分别高于影像组学模型约3.8%和3.1%。在测试集中,联合模型预测NAC不敏感的AUC值高于影像组学模型,其较后者提高了约2.3%,但两者的准确度相同。在基于ER、PR和Radscore构建的联合模型列线图中,Radscore得分最高,其次是ER和PR。决策曲线分析显示联合模型的临床获益高于影像组学模型。 结论 基于DCE-MRI的Radscore和ER、PR构建的联合模型列线图能够较好地预测NAC疗效不敏感。     

Changes in ADC and T2-weighted MRI-derived radiomic features in patients treated with focal salvage HDR prostate brachytherapy for local recurrence after previous external-beam radiotherapy

PurposeTo explore the changes in T2-weighted (T2w) and apparent diffusion coefficient (ADC) magnetic resonance imaging -derived radiomic features of the gross tumor volume (GTV) from focal salvage high-dose-rate prostate brachytherapy (HDRB) and to correlate with clinical parameters.Materials and MethodsEligible patients included those with biopsy-confirmed local recurrence that correlated with MRI (T2w, ADC). Patients received 27 Gy in 2 fractions separated by 1 week to a quadrant consisting of the GTV. The MRI was repeated 1 year after HDRB. GTVs, planning target volumes, and normal prostate tissue control volumes were identified on the pre- and post-HDRB MRIs. Radiomic features from each GTV were extracted, and principle component analysis identified features with the highest variance.ResultsPre- and post-HDRB MRIs were obtained from 14 trial patients. Principle component analysis showed that 18 and 17 features contributed to 93% and 86% of the variance observed in the T2w and ADC data, respectively. Sixteen T2w features and 1 ADC GTV feature were different from the control volumes in the pre-HDRB images (p < 0.05). Ten T2w and 7 ADC GTV post-HDRB features were different from those of pre-HDRB (p < 0.05).ConclusionsExploratory analysis reveals several radiomic features in the T2w and ADC image GTVs that distinguish the GTV from healthy prostate tissue and change significantly after salvage HDRB.