基于术前脾脏CT影像组学列线图预测胃癌的浆膜浸润

目的：联合临床检验指标及影像学特征构建一种能够术前识别胃癌浆膜浸润的模型。方法：选取2015年1月至2019年12月温州医科大学附属第一医院经病理证实的656例胃癌患者,采用随机数字表法分为建模组（394例）和验证组（262例）。收集建模组患者的脾脏影像学资料,对收集的数据进行套索回归并选取差异有统计学意义的特征来构建浆膜浸润预测模型。在最大约登指数下取肿瘤浸润风险评分截断值将患者分为高危组（238例）和低危组（418例）,然后与其他浸润相关因素如BMI、年龄、性别、高血压、糖尿病等进行单变量和多变量Logistic回归分析,结合显著的独立影响因素共同建立可视化的浆膜浸润预测列线图。结果：将患者以肿瘤浸润评分≤-0.335分为低危组,＞-0.335为高危组,经验证组验证,建模组和验证组的诊断准确性较为一致（P＜0.001）。经浸润影响因素的单变量和多变量Logistic回归分析发现,影像组学肿瘤浸润评分（OR=2.9,95%CI=2.1～4.2,P＜0.001）、术前低白蛋白（OR=1.3,95%CI=1.2～3.1,P=0.003）、血小板与淋巴细胞比值（OR=1.8,95%CI=1.2～2.7,P=0.004）、肿瘤分化程度（OR=2.6,95%CI= 1.8～3.7,P＜0.001）是浆膜浸润的独立影响因素。基于这4个指标建立的预测模型能够较为准确地预测浆膜浸润风险,其AUC值为0.733。结论：基于脾脏影像的肿瘤浸润评分联合其他临床因素可准确预测胃癌浆膜浸润与否,提高诊断精度。     

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.     

基于影像组学分类器术前预测直肠非黏液性腺癌淋巴结转移

目的：探讨影像组学方法在术前预测直肠非黏液性腺癌淋巴结转移中的价值。方法：回顾性分析91例手术病理切片证实为直肠非黏液性腺癌患者的影像学资料,其中61例为训练样本,30例为验证样本。基于全瘤体积,从每个原发病灶术前高分辨T2加权成像(T2-weighted imaging,T2WI)图像中提取影像组学特征1 301个。基于训练样本,利用最小绝对收缩和选择算子(the least absolute shrinkage and selection operator,LASSO)逻辑回归方法筛选关键特征并构建影像组学分类器。采用受试者工作特征(receiver operating characteristic,ROC)曲线评价影像组学分类器的辨别效能,并将其与形态学标准进行比较。在验证样本中验证影像组学分类器的价值。结果：由5个影像组学特征构建的分类器与淋巴结转移状态有关(P<0.001)。在训练样本和验证样本中,影像组学分类器诊断淋巴结转移的曲线下面积分别为0.874(95% CI：0.787~0.960)和0.878(95% CI：0.727~1.000),形态学标准诊断淋巴结转移的曲线下面积分别为0.619(95% CI：0.487~0.752)和0.556(95% CI：0.355~0.756)。无论是训练样本还是验证样本,影像组学分类器的诊断效能均高于形态学标准(均P<0.05)。结论：影像组学分类器可术前个体化预测直肠非黏液性腺癌淋巴结转移,而且其诊断效能高于形态学标准。     

影像组学及其在肝癌中的进展

肝癌(hepatocellular carcinoma,HCC)是全球癌症相关死亡的第二大原因,因此早期发现和治疗反应的预测对HCC患者有很大益处。 目前,穿刺活检和常规医学成像在HCC患者的管理中发挥重要且基础的作用,而这两种方法分别存在样本误差和操作者依赖性的不足。 影像组学是一种新兴的非侵入性技术,可以突破时空限制来获取肿瘤的综合信息,用以反映肿瘤的情况,从而弥补上述方法的不足。影像组学的基本步骤包括图像的获取,感兴趣区的划分与重建,特征的提取、划分与分类,模型的建立与效能评价。影像组学在HCC的诊断、治疗和评价方面取得了一定的进展,具有应用前景。     

人工智能技术在宫颈癌筛查和精准临床诊疗中的研究进展

宫颈癌的发病率和死亡率均高居女性恶性肿瘤的第4位,且呈年轻化趋势。由于不同地区资源分布不均,患者防治效果存在较大差异,因此需探索适宜不同资源地区的新型诊疗手段,加快推进宫颈癌防治工作。人工智能（artificial intelligence,AI）是一门研究开发计算机程序来模拟、延伸和拓展人行为的科学,近年来在图像分析方面表现优异,在癌症精准筛查、诊断及指导治疗方面展现了巨大潜能,但是目前仍然存在较大的问题及挑战,不可能完全代替医生诊断。本文通过对AI技术在宫颈癌早期筛查和精准临床诊疗方面的研究进展进行总结,以期为患者个性化治疗提供参考,提高患者临床疗效。     

瘤内与瘤周CT影像组学模型结合临床及常规CT特征鉴别肺原位腺癌与微浸润性腺癌

目的 观察基于平扫CT瘤内及瘤周影像组学模型联合临床及常规CT特征鉴别肺原位腺癌(AIS)与微浸润性腺癌(MIA)的价值。方法 回顾性分析180例孤立性AIS及180例孤立性MIA肺结节患者,随机将其中各160例纳入训练集(n=320)、各20例纳入测试集(n=40)。以训练集AIS与MIA间差异有统计学意义的临床及常规CT特征构建临床模型;勾画瘤内(CTi)及包含瘤周2 mm(CTi+p2mm)、4 mm(CTi+p4mm)ROI,提取并筛选其影像组学特征,分别以之构建CTi模型、CTi+p2mm模型及CTi+p4mm模型;绘制受试者工作特征(ROC)曲线,计算曲线下面积(AUC),评价各模型效能,遴选预测测试集MIA效能最佳者,联合临床及常规CT特征构建联合模型,观察临床模型、最佳影像组学模型及联合模型的AUC、校准度及净收益。结果 训练集内,相比AIS,MIA结节直径较大、密度不均匀、伴血管穿行结节占比较高(P均＜0.05)。CTi+p2mm模型鉴别测试集MIA与AIS效能最高(AUC=0.838,P＜0.05);以之结合临床及常规CT特征构建的联合模型鉴别诊断效能更佳(AUC=0.867,P＜0.05)。联合模型的校准度及0.60~0.90阈值概率区间的临床净收益较高。结论 基于平扫CT构建的瘤内和瘤周2 mm ROI影像组学模型能有效鉴别肺MIA与AIS,联合临床及常规CT特征可进一步提高模型鉴别效能。     

MRI结合人工智能技术预测弥漫性胶质瘤分子病理研究进展

分子病理指标对于诊断和治疗弥漫性胶质瘤(DG)具有关键作用。迅速发展的人工智能(AI)技术为预测DG分子病理和评估预后提供了客观、无创的工具。本文围绕MRI结合AI技术预测DG重要分子病理研究进展进行述评。     

Prognostic and incremental value of computed tomography-based radiomics from tumor and nodal regions in esophageal squamous cell carcinoma

Objective: This study aimed to evaluate the prognostic value of preoperative radiomics and establish an integrated model for esophageal squamous cell cancer(ESCC).Methods: A total of 931 patients were retrospectively enrolled in this study(training cohort, n=624; validation cohort, n=307). Radiomics features were obtained by contrast-enhanced computed tomography(CT) before esophagectomy. A radiomics index was set based on features of tumor and reginal lymph nodes by using the least absolute shri...     

Texture Analysis in Brain Tumor MR Imaging

Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram- or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors.However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples.To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts.     

机器学习结合CT影像组学预测2型糖尿病患者椎体脆性骨折

目的：探讨机器学习结合CT影像组学特征构建模型预测2型糖尿病（type 2 diabetes mellitus,T2DM）患者椎体脆性骨折的准确性。方法：回顾性收集140例（新发椎体脆性骨折的T2DM患者70例,对照组70例）患者CT图像和临床资料。另收集18例（椎体脆性骨折的T2DM患者16例,对照组2例）患者的前次CT图像和临床资料作为外部验证集。应用单因素分析、Pearson相关分析、最小冗余度最大相关度算法、二元logistic回归分析和最小绝对值收缩和选择算子模型筛选出最佳特征。基于支持向量机、多层感知器、极端梯度提升（eXtreme Gradient Boosting,XGBoost）构建预测模型。应用受试者工作特征曲线下面积（area under the curve,AUC）对模型效能进行评估。结果：从每例患者的CT图像中提取了1 037个影像组学特征,然后精简为14个影像组学特征。17个临床特征中性别、年龄、体质指数是预测结果的独立因素。其中XGBoost分类器表现最好,训练集中XGBoost模型的AUC分别为1.000、0.929、1.000;测试集中分别为0.954...