胆囊癌淋巴结状态精准评估策略

淋巴结转移是胆囊癌（GBC）最常见的转移方式，准确的淋巴结状态评估对于GBC患者预后评估、治疗方案选择及手术切除范围等均具有重要的指导意义。目前对于GBC淋巴结状态评估仍存在较多问题与争议。本文从术前、术中及术后三个方面，结合近年来快速发展的人工智能及影像组学技术对GBC淋巴结状态评估现状及存在问题作一述评     

基于灰阶超声联合剪切波弹性成像的影像组学模型诊断乳腺癌腋窝淋巴结转移的临床价值

目的 基于灰阶超声（US）、剪切波弹性成像（SWE）图像特征及临床、病理指标构建双模态影像组学模型，探讨其对乳腺癌腋窝淋巴结转移的诊断价值。方法 选取于我院行乳腺癌手术治疗患者306例，按照7∶3比例随机分为训练集（214例）和验证集（92例），基于术前US和SWE图像分别进行感兴趣区分割和特征提取。应用最小绝对收缩和选择算子（LASSO）算法筛选关键特征并分别构建US、SWE影像组学标记物（US-RIS、SWE-RIS）。采用单因素和多因素Logistic回归在临床、病理指标和RIS中筛选变量并构建单模态US、SWE影像组学模型及双模态影像组学模型；绘制受试者工作特征（ROC）曲线分析并比较各影像组学模型、超声医师对乳腺癌腋窝淋巴结转移的诊断效能；绘制决策曲线评估各影像组学模型的临床实用价值；绘制校准曲线分析双模态影像组学模型预测结果与实际结果的一致性。结果 基于LASSO算法筛选出13个关键US图像特征和17个关键SWE图像特征，分别构建US-RIS和SWE-RIS。单因素和多因素Logistic回归分析显示，BI-RADS分类、肿瘤分类、US-RIS、SWE-RIS均为乳腺癌腋窝...     

Current status and future perspectives of radiomics in hepatocellular carcinoma

Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.     

基于影像组学鉴定头颈部鳞状细胞癌患者HPV状态：诊断性meta分析

目的： 采用诊断性meta方法,评价影像组学特征,构建模型,鉴定头颈部鳞状细胞癌患者人乳头瘤病毒（HPV）状态的价值。方法： 检索PubMed、Embase、Web of Science、中国知网、维普数据库中有关影像组学鉴定HPV状态的相关文献。检索时间为建库至2022年5月28日。采用Revman5.4等统计软件,评估预测模型的诊断效能。结果： 共纳入9篇文献,包括3 312例患者。合并结果显示,基于影像组学方法诊断患者HPV状态的敏感度为0.82[95%CI(0.74,0.87)],特异度为0.66[95%CI(0.54,0.75)], 阳性似然比为2.4[95%CI(1.8,3.1)],阴性似然比为0.28[95%CI(0.21,0.36)],诊断比值比为9[95%CI(6,12)],综合受试者工作特征曲线下面积(AUC)为0.82[95%CI(0.78,0.85)]。通过影像组学方法鉴定HPV状态先验概率为25%,可提升至44%(后验概率)。结论： 采用影像组学方法鉴定头颈部鳞状细胞癌患者HPV状态有较高的实用价值。     

Feasibility of radiomic feature harmonization for pooling of [18F]FET or [18F]GE-180 PET images of gliomas

IntroductionLarge datasets are required to ensure reliable non-invasive glioma assessment with radiomics-based machine learning methods. This can often only be achieved by pooling images from different centers. Moreover, trained models should perform with high accuracy when applied to data from different centers. In this study, the impact of reconstruction settings and segmentation methods on radiomic features derived from amino acid and TSPO PET images of glioma patients was examined. Additionally, the ability to model and thus reduce feature differences was investigated.Methods[¹⁸F]FET and [¹⁸F]GE-180 PET data were acquired from 19 glioma patients. For each acquisition, 10 reconstruction settings and 9 segmentation methods were included to emulate multicentric data. Statistical robustness measures were calculated before and after ComBat harmonization. Differences between features due to setting variations were assessed using Friedman test, coefficient of variation (CV) and inter-rater reliability measures, including intraclass and Spearman’s rank correlation coefficients and Fleiss’ Kappa.ResultsAccording to Friedman analyses, most features (>60%) showed significant differences. Yet, CV and inter-rater reliability measures indicated higher robustness. ComBat resulted in almost complete harmonization (>87%) according to Friedman test and little to no improvement according to CV and inter-rater reliability measures. [¹⁸F]GE-180 features were more sensitive to reconstruction settings than [¹⁸F]FET features.ConclusionsAccording to Friedman test, feature distributions could be successfully aligned using ComBat. However, depending on settings, changes in patient ranks were observed for some features and could not be eliminated by harmonization. Thus, for clinical utilization it is recommended to exclude affected features.     

Clinical-radiomics nomogram for predicting esophagogastric variceal bleeding risk noninvasively in patients with cirrhosis

BACKGROUND Esophagogastric variceal bleeding(EGVB) is a serious complication of patients with decompensated cirrhosis and is associated with high mortality and morbidity. Early diagnosis and screening of cirrhotic patients at risk for EGVB is crucial. Currently, there is a lack of noninvasive predictive models widely available in clinical practice.AIM To develop a nomogram based on clinical variables and radiomics to facilitate the noninvasive prediction of EGVB in cirrhotic patients.METHODS A t...