论著摘要 |【CT】基于双能CT图像提取的影像组学特征对可手术的肺腺癌的病理学分层(双语版)

2017-10-10 10:15:09 admin 2

Pathologic Stratification of Operable Lung Adenocarcinoma using Radiomics Features Extracted from Dual Energy CT Images.

发表日期: 2017.01.03   来源:Oncotarget, 2016, 8(1): 523–535.

作者

Jung Min Bae,#1 Ji Yun Jeong,#2 Ho Yun Lee,1 Insuk Sohn,3 Hye Seung Kim,3 Ji Ye Son,1 O Jung Kwon,4 Joon Young Choi,5 Kyung Soo Lee,1 and Young Mog Shim6

作者介绍:

1. Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 135-710, Korea

2. Department of Pathology, Kyungpook National University Medical Center, Kyungpook National University School of Medicine, Daegu 702-210, Korea

3. Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 135-710, Korea

4. Division of Respiratory and Critical Medicine of the Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 135-710, Korea

5. Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 135-710, Korea

6. Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul 135-710, Korea

#Contributed equally.

Correspondence to: Ho Yun Lee, Email: moc.liamg@69eelnuyoh

Young Mog Shim, Email: moc.gnusmas@mihs.gomgnuoy

摘要

Abstract

目的

使用双能计算机断层扫描(DECT)的影像组学数据,评估替代生物标志物作为组织病理学肿瘤等级和侵袭性的预测因子的有用性,最终实现早期肺腺癌分层治疗的最佳治疗方法。

Purpose

To evaluate the usefulness of surrogate biomarkers as predictors of histopathologic tumor grade and aggressiveness using radiomics data from dual-energy computed tomography (DECT), with the ultimate goal of accomplishing stratification of early-stage lung adenocarcinoma for optimal treatment.

结果

病理分级分为1级,2级和3级。多元逻辑回归分析显示i均匀性和第97.5百分位数CT衰减值作为独立的重要因素,从1级分级为2级或3级。用从留一交叉验证程序计算得到的AUC值区分等级1,23AUC值分别为0.930795CI0.8514-1),0.861095CI0.7547-0.9672)和0.839495CI0.7045-0.9743 )。

Results

Pathologic grade was divided into grades 1, 2, and 3. Multinomial logistic regression analysis revealed i-uniformity and 97.5th percentile CT attenuation value as independent significant factors to stratify grade 2 or 3 from grade 1. The AUC value calculated from leave-one-out cross-validation procedure for discriminating grades 1, 2, and 3 was 0.9307 (95% CI: 0.8514–1), 0.8610 (95% CI: 0.7547–0.9672), and 0.8394 (95% CI: 0.7045–0.9743), respectively.

材料与方法

共有80位患者, 91个临床和放射学诊断为I期或II期的肺腺癌进行研究。所有患者均接受DECTF-18-氟脱氧葡萄糖(FDG)正电子发射断层扫描(PET/ CT,随后进行手术。使用影像组学方法评估定量CTPET成像特征。提取肿瘤侵袭性预测模型的重要特征来计算其预测所有病理分级的诊断性能。

Materials and Methods

A total of 80 patients with 91 clinically and radiologically suspected stage I or II lung adenocarcinoma were prospectively enrolled. All patients underwent DECT and F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT, followed by surgery. Quantitative CT and PET imaging characteristics were evaluated using a radiomics approach. Significant features for a tumor aggressiveness prediction model were extracted and used to calculate diagnostic performance for predicting all pathologic grades.

结论

DECT成像指标的定量影像组学值可以帮助预测肺腺癌的病理侵袭性。

Conclusions

Quantitative radiomics values from DECT imaging metrics can help predict pathologic aggressiveness of lung adenocarcinoma.

 

阅读原文:doi:10.18632/oncotarget.13476


慧影医疗科技(北京)有限公司

地点:北京市海淀区中关村东升科技园B2-C103

电话:400-890-9020

邮箱:radcloud@huiyihuiying.com

关闭
图片
图片
  • 深度学习科研云平台