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。     

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.     

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...     

脑转移瘤影像组学研究进展

脑转移瘤是成人最常见的颅内肿瘤,发病率呈上升趋势。影像组学可对医学影像进行定量分析处理来指导临床实践。近年来,基于CT、MRI的影像组学逐渐应用于脑转移瘤的精准诊疗,如肿瘤精准检测和分割、与其他脑肿瘤的鉴别诊断、原发肿瘤的判别、疗效评价及预后预测等。本文就脑转移瘤影像组学研究现状予以综述。     

基于CT平扫影像组学模型预测食管癌淋巴结转移

目的 评估术前基于胸部CT平扫影像组学模型预测食管癌患者淋巴结转移的价值。方法 回顾性分析368例经术前内镜活检及术后病理确诊的食管癌患者,其中100例淋巴结转移、268例无淋巴结转移,按比例3 ∶ 1将其分为训练组（包括201例无淋巴结转移和75例淋巴结转移）和验证组（67例无淋巴结转移和25例淋巴结转移）。自胸部CT中提取食管癌病灶的影像组学特征,并以最小绝对收缩和选择算子（LASSO）回归进行降维,筛选与食管癌淋巴结转移关联度高的特征;采用支持向量机构建预测淋巴结转移的影像组学模型,并以受试者工作特征（ROC）曲线分析模型的诊断效能。结果 共提取1 046个组学特征参数,经LASSO降维筛选出11个特征参数用于建立预测淋巴结转移模型。影像组学模型预测训练组淋巴结转移的曲线下面积（AUC）为0.84,敏感度为84.00%,特异度为75.12%,准确率为77.54%;于验证组的AUC为0.82,敏感度为80.00%,特异度为77.61%,准确率为78.26%。结论 术前基于胸部CT平扫影像组学模型预测食管癌患者淋巴结转移具有较高价值。     

乳腺癌MRI影像组学的研究进展

乳腺癌是全世界女性最常见的恶性肿瘤之一,也是患癌女性的主要死因。提高诊断准确性、早期评估乳腺癌预后及治疗效果是临床实践中的关键问题。MRI因其较高的软组织分辨率,目前仍是诊断乳腺癌的常用成像方式。相对于乳房X线摄影和超声,MRI可以提供更全面的诊断信息。影像组学是一个高通量提取和定量分析影像特征的新研究领域,近年来受到广泛关注并应用于肿瘤领域。作者就乳腺癌MRI影像组学方面的研究进展进行综述。     

影像基因组学在结直肠癌肝转移诊疗中的应用进展

结直肠癌在消化系统恶性肿瘤中比较常见,经门静脉系统转移至肝脏是其常见的血行转移途径,尽早发现肝转移灶并治疗是改善患者预后的关键。影像组学及影像基因组学通过深度挖掘影像图像的纹理特征,并量化肿瘤的异质性,用无创的方法评估肿瘤的生物学性质,在结直肠癌肝转移的发现、指导治疗、预后预测等已广泛应用。现拟基于肝脏的影像学图像,对结直肠癌肝转移瘤的影像组学及影像基因组学的应用现状进行综述。