二分法VASARI特征与胶质母细胞瘤患者总生存期的关系:单机构倾向评分匹配分析。

IF 3.5 2区 医学 Q2 ONCOLOGY Cancer Imaging Pub Date : 2024-08-18 DOI:10.1186/s40644-024-00754-z
Yu Han, Yu-Yao Wang, Yang Yang, Shu-Qi Qiao, Zhi-Cheng Liu, Guang-Bin Cui, Lin-Feng Yan
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引用次数: 0

摘要

研究目的本研究旨在调查视觉可及伦勃朗图像(VASARI)特征集在二分法化前后观察者内部和观察者之间的一致性,以及二分法VASARI特征与胶质母细胞瘤(GBM)患者总生存期(OS)之间的关联:这项回顾性研究纳入了2016年1月至2022年6月间351例经病理证实的IDH1野生型GBM患者。首先,由四位经验不同的放射科医生在二分法前后对 VASARI 特征进行评估。计算科恩卡帕系数(κ)来衡量观察者内部和观察者之间的一致性。然后,在使用倾向评分匹配法调整混杂因素后,使用 Kaplan-Meier 曲线比较每个二分法 VASARI 特征的 OS 差异。接下来,按 3:2 的比例将患者随机分层为训练集(n = 211)和测试集(n = 140)。在训练集的基础上,采用Cox比例危险回归分析建立预测OS的综合临床模型,并通过测试集评估模型的性能:结果:κ值为0.61-0.8的11个VASARI特征在二分法后显示出几乎完美的一致性,所有读者的κ值范围为0.874-1.000。七个 VASARI 特征与 GBM 患者的 OS 相关。就OS预测而言,在训练集(C-index, 0.762 vs. 0.723)和测试集(C-index, 0.812 vs. 0.702)中,组合模型的表现均优于临床模型:结论:二分法 VASARI 特征在观察者之间和观察者内部具有极好的一致性。结论:VASARI的二分法特征在观察者之间和观察者内部具有极好的一致性,在预测OS方面,组合模型优于临床模型。
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Association between dichotomized VASARI feature and overall survival in glioblastoma patients: a single-institution propensity score matching analysis.

Objectives: This study aimed to investigate the intra- and inter-observer consistency of the Visually Accessible Rembrandt Images (VASARI) feature set before and after dichotomization, and the association between dichotomous VASARI features and the overall survival (OS) in glioblastoma (GBM) patients.

Methods: This retrospective study included 351 patients with pathologically confirmed IDH1 wild-type GBM between January 2016 and June 2022. Firstly, VASARI features were assessed by four radiologists with varying levels of experience before and after dichotomization. Cohen's kappa coefficient (κ) was calculated to measure the intra- and inter-observer consistency. Then, after adjustment for confounders using propensity score matching, Kaplan-Meier curves were used to compare OS differences for each dichotomous VASARI feature. Next, patients were randomly stratified into a training set (n = 211) and a test set (n = 140) in a 3:2 ratio. Based on the training set, Cox proportional hazards regression analysis was adopted to develop combined and clinical models to predict OS, and the performance of the models was evaluated with the test set.

Results: Eleven VASARI features with κ value of 0.61-0.8 demonstrated almost perfect agreement after dichotomization, with the range of κ values across all readers being 0.874-1.000. Seven VASARI features were correlated with GBM patient OS. For OS prediction, the combined model outperformed the clinical model in both training set (C-index, 0.762 vs. 0.723) and test set (C-index, 0.812 vs. 0.702).

Conclusion: The dichotomous VASARI features exhibited excellent inter- and intra-observer consistency. The combined model outperformed the clinical model for OS prediction.

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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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