论著摘要 |【Radiomics-CT】影像组学表型特征预测非小细胞肺癌的病理反应(双语版)

2018-06-12 12:01:08 admin
标签:   影像组学 非小细胞肺癌 CT 病理反应 肺癌

Radiomic phenotype features predict pathological response in non-small cell lung cancer.

发表日期: 2016.06.01   来源:Radiother Oncol. 2016 Jun;119(3):480-6.

作者:

Coroller TP1, Agrawal V22, Narayan V22, Hou Y22, Grossmann P22, Lee SW22, Mak RH2,2 Aerts HJ33.

作者介绍:

1. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA. Electronic address: tcoroller@lroc.harvard.edu.

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

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

摘要

Abstact

背景与目标

影像组学通过应用先进的成像特征算法可以非侵入性地量化肿瘤表型特征。在这项研究中,我们评价了治疗前影像组学数据是否能预测局部晚期非小细胞肺癌(NSCLC)患者新辅助放化疗后的病理反应。

Background and purpose

Radiomics can quantify tumor phenotype characteristics non-invasively by applying advanced imaging feature algorithms. In this study we assessed if pre-treatment radiomics data are able to predict pathological response after neoadjuvant chemoradiation in patients with locally advanced non-small cell lung cancer (NSCLC).

材料与方法

本研究纳入了127例非小细胞肺癌患者。基于稳定性和方差,选择出15个影像组学特征用以评估其预测病理反应的能力。预测能力使用曲线下面积(AUC)评估,并使用常规成像特征(肿瘤体积和直径)进行比较。

Materials and Methods

127 NSCLC patients were included in this study. Fifteen radiomic features selected based on stability and variance were evaluated for its power to predict pathological response. Predictive power was evaluated using area under the curve (AUC). Conventional imaging features (tumor volume and diameter) were used for comparison.

结果

7个特征可预测对总体病理残留病变(AUC > 0.6,p值 < 0.05),以及1个用于病理完全反应(AUC = 0.63,p值 = 0.01)。常规成像特征没有预测性(范围AUC = 0.51-0.59,p值 > 0.05)。对新辅助放化疗没有很好响应的肿瘤更可能呈现更圆的形状(不成比例球形,AUC = 0.63,p值 = 0.009)和非均匀结构(LOG 5毫米3D - GLCM熵,AUC = 0.61,p值 = 0.03)。

Results

Seven features were predictive for pathologic gross residual disease (AUC > 0.6, p-value < 0.05), and one for pathologic complete response (AUC = 0.63, p-value = 0.01). No conventional imaging features were predictive (range AUC = 0.51-0.59, p-value > 0.05). Tumors that did not respond well to neoadjuvant chemoradiation were more likely to present a rounder shape (spherical disproportionality, AUC = 0.63, p-value = 0.009) and heterogeneous texture (LoG 5mm 3D - GLCM entropy, AUC = 0.61, p-value = 0.03).

结论

我们确定了能预测病理反应的影像组学特征,而常规特征没有显著预测性。这项研究表明,影像组学可以提供有价值的临床信息,并且比传统成像特征表现更好。

Conclusions

We identified predictive radiomic features for pathological response, although no conventional features were significantly predictive. This study demonstrates that radiomics can provide valuable clinical information, and performed better than conventional imaging features.

关键词:

生物标记;非小细胞肺癌;病例反应;定量成像;影像组学

Keywords:

Biomarkers; NSCLC; Pathological response; Quantitative imaging; Radiomics

阅读原文:PMID: 27085484  PMCID: PMC4930885  DOI: 10.1016/j.radonc.2016.04.004


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