论著摘要 |【Radiomics-CT】应用定量CT图像特征分析预测卵巢癌患者化疗反应(双语版)

2018-01-15 18:05:29 admin 0
标签:   影像组学 CT 卵巢癌 化疗 预测

Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy.

发表日期: 2017.05.26   来源:Acad Radiol. 2017 Oct;24(10):1233-1239.

作者:

Danala G1, Thai T2, Gunderson CC2, Moxley KM2 , Moore K2, Mannel RS2, Liu H1, Zheng B1, Qiu Y3.

作者介绍:

1. School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK 73019.

2. Health Science Center of University of Oklahoma, Oklahoma City, Oklahoma.

3. School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK 73019. Electronic address: qiuyuchen@ou.edu.

摘要

Abstact

原理和目标

该研究旨在探讨在计算机断层扫描(CT)图像中计算的定量图像特征用于临床试验中使用化疗治疗卵巢癌患者的肿瘤反应的早期预测的作用。

Rationale and Objectives

The study aimed to investigate the role of applying quantitative image features computed from computed tomography (CT) images for early prediction of tumor response to chemotherapy in the clinical trials for treating ovarian cancer patients.

材料和方法

回顾性分析了91例患者的数据集。每位患者有两组治疗前和治疗后CT图像。计算机辅助检测方案被应用于分割放射科医师以前在CT图像和计算图像特征上跟踪的转移性肿瘤。使用从治疗前CT图像计算的图像特征和从治疗前和治疗后图像计算的图像特征差异构建了两个初始特征池。应用特征选择方法选择最佳特征,采用平均加权融合方法从每个池中产生新的定量成像标记,以预测6个月的无进展生存期。还比较了定量成像标记标准与实体肿瘤(RECIST)中的响应评估标准之间的预测准确性。

Materials and Methods

A dataset involving 91 patients was retrospectively assembled. Each patient had two sets of pre- and post-therapy CT images. A computer-aided detection scheme was applied to segment metastatic tumors previously tracked by radiologists on CT images and computed image features. Two initial feature pools were built using image features computed from pre-therapy CT images only and image feature difference computed from both pre- and post-therapy images. A feature selection method was applied to select optimal features, and an equal-weighted fusion method was used to generate a new quantitative imaging marker from each pool to predict 6-month progression-free survival. The prediction accuracy between quantitative imaging markers and the Response Evaluation Criteria in Solid Tumors (RECIST) criteria was also compared.

结果

分别使用从治疗前CT图像计算的单个图像特征和治疗前后CT图像的特征差异,接收器最高工作特征曲线下面积分别为0.684±0.056和0.771±0.050。使用两个相应的融合图像标记,接收器工作特征曲线下的面积分别显著增加到0.810±0.045和0.829±0.043(P<0.05)。使用两个成像标记物和RECIST时,总体预测精度水平分别为71.4%,80.2%和74.7%。

Results

The highest areas under the receiver operating characteristic curve are 0.684 ± 0.056 and 0.771 ± 0.050 when using a single image feature computed from pre-therapy CT images and feature difference computed from pre- and post-therapy CT images, respectively. Using two corresponding fusion-based image markers, the areas under the receiver operating characteristic curve significantly increased to 0.810 ± 0.045 and 0.829 ± 0.043 (P < 0.05), respectively. Overall prediction accuracy levels are 71.4%, 80.2%, and 74.7% when using two imaging markers and RECIST, respectively.

结论

这项研究表明使用从治疗前CT图像计算的定量成像标记物预测患者化疗反应的可行性。然而,使用在治疗前和治疗后CT图像之间计算的图像特征差异产生较高的预测精度。

Conclusions

This study demonstrated the feasibility of predicting patients' response to chemotherapy using quantitative imaging markers computed from pre-therapy CT images. However, using image feature difference computed between pre- and post-therapy CT images yielded higher prediction accuracy.

关键词:

量化图像特征分析;卵巢癌化疗方法;临床试验的预测效果;化疗反应预测;影像组学

Keywords:

Quantitative image feature analysis; chemotherapy of ovarian cancer; prediction efficacy of clinical trials; prediction of tumor response to chemotherapy; radiomics

阅读原文:PMID: 28554551  DOI: 10.1016/j.acra.2017.04.014


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