论著摘要 |【Radiomics-CT】非小细胞肺癌原发肿瘤和淋巴结中基于影像组学的病理学预测(双语版)

2018-06-25 19:07:14 admin
标签:   影像组学 非小细胞肺癌 淋巴结 CT

Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC.

发表日期: 2017.03.01   来源:J Thorac Oncol. 2017 Mar;12(3):467-476.

作者:

Coroller TP1, Agrawal V1, Huynh E1, Narayan V1, Lee SW1, Mak RH#1, Aerts HJWL#1,2.

作者介绍:

1. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

2. Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

# Contributed equally

摘要

Abstact

引言

捕获全部肿瘤负荷的无创生物标志物可以为精准医疗提供重要的补充信息以帮助临床决策。我们研究了从原发肿瘤和淋巴结治疗前计算机断层扫描图像中提取的影像组学数据,在手术前新辅助放化疗后预测病理反应的价值。

Introduction

Noninvasive biomarkers that capture the total tumor burden could provide important complementary information for precision medicine to aid clinical decision making. We investigated the value of radiomic data extracted from pretreatment computed tomography images of the primary tumor and lymph nodes in predicting pathological response after neoadjuvant chemoradiation before surgery.

方法

本机构审查委员会批准的研究纳入了2003年至2013年期间共85名可局部手术切除的晚期(II-III期)NSCLC患者(中位年龄60.3岁,65%为女性)。对85个原发肿瘤和178个淋巴结进行影像组学分析,以区分病理完全缓解(pCR)和总体残余疾病(GRD)。通过使用曲线下面积(AUC)评估20个非冗余和稳定的特征(来自每个位点的10个特征)(所有p值被校正用于多重假设检验)。通过随机森林和嵌套交叉验证评估每个特征集合的分类性能。

Methods

A total of 85 patients with resectable locally advanced (stage II-III) NSCLC (median age 60.3 years, 65% female) treated from 2003 to 2013 were included in this institutional review board-approved study. Radiomics analysis was performed on 85 primary tumors and 178 lymph nodes to discriminate between pathological complete response (pCR) and gross residual disease (GRD). Twenty nonredundant and stable features (10 from each site) were evaluated by using the area under the curve (AUC) (all p values were corrected for multiple hypothesis testing). Classification performance of each feature set was evaluated by random forest and nested cross validation.

结果

三个基本特征(描述原发性肿瘤球形度和淋巴结同质性)对pCR预测具有相似的显著性表现(所有的AUC = 0.67,p < 0.05)。两个特征(定量淋巴结同质性)预测GRD(AUC范围0.72-0.75,p < 0.05),并且显著优于主要特征(AUC = 0.62)。多变量分析显示,对于pCR,单独设置的影像组学特征具有最好的分类能力(中值AUC = 0.68)。此外,对于GRD分类,影像组学和临床数据的组合显著优于其他所有特征组(中值AUC = 0.73)。

Results

Three radiomic features (describing primary tumor sphericity and lymph node homogeneity) were significantly predictive of pCR with similar performances (all AUC = 0.67, p < 0.05). Two features (quantifying lymph node homogeneity) were predictive of GRD (AUC range 0.72-0.75, p < 0.05) and performed significantly better than the primary features (AUC = 0.62). Multivariate analysis showed that for pCR, the radiomic features set alone had the best-performing classification (median AUC = 0.68). Furthermore, for GRD classification, the combination of radiomic and clinical data significantly outperformed all other feature sets (median AUC = 0.73).

结论

淋巴结表型信息能显著预测病理学反应,并且显示出比从原发性肿瘤获得的影像组学特征更高的分类性能。

Conclusions

Lymph node phenotypic information was significantly predictive for pathological response and showed higher classification performance than radiomic features obtained from the primary tumor.

关键词:

生物统计学;肺结节;非小细胞肺癌;病理反应;定量成像;影像组学

Keywords:

Biostatistics; Lymph nodes; NSCLC; Pathological response; Quantitative imaging; Radiomics

阅读原文:PMID: 27903462  PMCID: PMC5318226  DOI: 10.1016/j.jtho.2016.11.2226


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