Synthetic MRI improves radiomics-based glioblastoma survival prediction

Glioblastoma is an aggressive and fast-growing brain tumor with poor prognosis. Predicting the expected survival of patients with glioblastoma is a key task for efficient treatment and surgery planning. Survival predictions could be enhanced by means of a radiomic system. However, these systems demand high numbers of multicontrast images, the acquisitions of which are time consuming, giving rise to patient discomfort and low healthcare system efficiency. Synthetic MRI could favor deployment of radiomic systems in the clinic by allowing practitioners not only to reduce acquisition time, but also to retrospectively complete databases or to replace artifacted images. In this work we analyze the replacement of an actually acquired MR weighted image by a synthesized version to predict survival of glioblastoma patients with a radiomic system. Each synthesized version was realistically generated from two acquired images with a deep learning synthetic MRI approach based on a convolutional neural network. Specifically, two weighted images were considered for the replacement one at a time, a T2w and a FLAIR, which were synthesized from the pairs T1w and FLAIR, and T1w and T2w, respectively. Furthermore, a radiomic system for survival prediction, which can classify patients into two groups (survival >480 days and $\le$ 480 days), was built. Results show that the radiomic system fed with the synthesized image achieves similar performance compared with using the acquired one, and better performance than a model that does not include this image. Hence, our results confirm that synthetic MRI does add to glioblastoma survival prediction within a radiomics-based approach.     

结直肠癌肝转移预测及预后的影像组学综述

结直肠癌在国内外都是高发病率和高死亡率的肿瘤疾病,肝脏是结直肠癌血行转移最主要的靶器官,尽早准确地预测结直肠癌肝转移的发生以及监测患者治疗的预后反应,对患者的诊断和治疗尤为关键。新兴的影像组学为精准预测结直肠癌肝转移以及评估预后提供新的可能。对现有的结直肠癌肝转移影像组学研究进行综述,首先介绍结直肠癌肝转移研究的临床意义以及现有研究存在的缺陷,然后阐述结直肠癌肝转移影像组学的分析流程,接着对结直肠癌肝转移影像组学的4个研究方向展开综述,包括肝转移病理生长模式预测、隐匿性肝转移预测、肝转移疗效预后评估、肝转移患者生存期预后评估;阐明各个方向的最新研究进展及存在的缺陷性;最后对结直肠癌肝转移影像组学的未来发展趋势进行展望。     

基于CT影像组学模型鉴别诊断小细胞肺癌与非小细胞肺癌

目的观察基于CT影像组学模型鉴别诊断小细胞肺癌(SCLC)与非小细胞肺癌(NSCLC)的效能。方法回顾性分析1 524例经手术病理确诊的肺癌患者,其中526例SCLC (SCLC组), 998例NSCLC(NSCLC组)。采用特征提取软件MaZda(Version 4.6)提取CT图像中病灶最大层面的纹理特征参数,以Correlation相关性分析和最小绝对收缩和选择算子(LASSO)算法对数据进行降维,筛选组间差异明显的影像组学特征,构建影像组学模型。以7∶3比例将全部患者分为训练集和验证集,采用7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)、卷积神经网络(CNN)、邻近算法(KNN)和支持向量机(SVM)模型对数据集进行处理,根据其在验证集的准确率选择最佳分类器模型,采用受试者工作特征(ROC)曲线分析该分类器模型对SCLC与NSCLC的鉴别诊断效能。结果针对每个病灶提取306个纹理特征参数,最终筛选出20个组间差异明显的影像组学特征,并以之构建预测模型。模型训练结果显示,KNN模型鉴别诊断SCLC与NSCLC的准确率最高,其在训练集的AUC为0.88、准确率81.34%、特异度97.00%、敏感度51.63%,在验证集的AUC为0.82、准确率78.82%、特异度95.00%、敏感度48.10%。结论基于CT影像组学结合机器学习算法建立的诊断模型可用于鉴别SCLC与NSCLC,以KNN模型的效能更优。     

基于术前MRI影像组学特征预测肝癌经肝动脉化疗栓塞治疗的反应性

目的 探索术前MRI影像组学特征预测肝细胞癌（hepatocellular carcinoma,HCC）对经肝动脉化疗栓塞（transcatheter arterial chemoembolization,TACE）治疗反应性的可行性。方法 回顾性收集2016年3月至2018年4月经临床或病理确诊、并在温州医科大学附属第五医院接受TACE治疗的中晚期HCC患者86例,采用随机数字表法分为训练组（n=59）及验证组（n=27）,基于ITK-SNAP软件在术前T2WI图像勾画肿瘤感兴趣区,使用GE AI-Kit软件提取影像特征;经最小绝对收缩与选择算子（LASSO）回归筛选最优的特征子集,并构建TACE反应性预测模型;利用受试者工作特征（ROC）曲线、校准曲线及决策曲线分析评价模型的诊断效能和临床应用价值。结果 共筛选出8个影像学特征与TACE反应性相关,并成功构建预测模型。训练组中,该模型的ROC曲线下面积（AUC）为0.838（95%CI 0.737～0.939）,灵敏度为74.1%,特异度为84.4%;在验证组中,AUC为0.794（95%CI 0.604～0.985）,灵敏度为75.0%,特异度为80.0%。校准曲线显示该模型在训练组和验证组的预测概率与实际概率拟合较好;决策曲线分析显示该模型在0.1～1.0的阈值范围内具有较高的净收益。结论 术前MRI T2WI图像影像组学特征可用于预测HCC的TACE 反应性,基于MRI T2WI图像影像特征的预测模型特异度和灵敏度较高。     

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。     

基于MR T2WI影像组学诊断早期糖尿病肾病

目的 观察基于MR T2WI影像组学诊断早期糖尿病肾病（DN）的价值。方法 收集59例估算肾小球滤过率（eGFR）为60~120 ml/（min·1.73 m²）且尿白蛋白/肌酐比值（ACR）>30 mg/g的早期DN患者作为试验组,另以46名无肾脏疾病、心脏疾病、高血压及痛风且eGFR为80~120 ml/（min·1.73 m²）的健康志愿者为对照组。按照7∶3比例将全部受试者分为训练集（n=73）和测试集（n=32）,训练集包括试验组40例、对照组33名,测试集包括试验组19例、对照组13名受试者。提取训练集右侧肾脏T2WI影像组学特征,并进行筛选,构建影像组学模型,观察其诊断早期DN的价值。结果 试验组与对照组性别及年龄差异均无统计学意义（P均>0.05）,而试验组尿素及肌酐均大于对照组（P均<0.05）。影像组学模型诊断训练集及测试集早期DN的敏感度、特异度及曲线下面积分别为95.20%、77.41%及0.892和88.23%、53.33%及0.765。结论 基于MR T2WI影像组学诊断早期DN具有一定价值。     

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...     

Strengths,Weaknesses, Opportunities,and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology

Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning (ML), has become a reality in clinical practice. Since the end of the last century, several ML algorithms have been introduced for a wide range of common imaging tasks, not only for diagnostic purposes but also for image acquisition and postprocessing. AI is now recognized to be a driving initiative in every aspect of radiology. There is growing evidence of the advantages of AI in radiology creating seamless imaging workflows for radiologists or even replacing radiologists. Most of the current AI methods have some internal and external disadvantages that are impeding their ultimate implementation in the clinical arena. As such, AI can be considered a portion of a business trying to be introduced in the health care market. For this reason, this review analyzes the current status of AI, and specifically ML, applied to radiology from the scope of strengths, weaknesses, opportunities, and threats (SWOT) analysis.