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肝脏实质的 CT 纹理特征预测结直肠癌患者转移疾病和总生存的发展。

CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer.

机构信息

Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI, 53792, USA.

Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI, 53792, USA.

出版信息

Eur Radiol. 2018 Apr;28(4):1520-1528. doi: 10.1007/s00330-017-5111-6. Epub 2017 Nov 21.

Abstract

OBJECTIVES

To determine if identifiable hepatic textural features are present at abdominal CT in patients with colorectal cancer (CRC) prior to the development of CT-detectable hepatic metastases.

METHODS

Four filtration-histogram texture features (standard deviation, skewness, entropy and kurtosis) were extracted from the liver parenchyma on portal venous phase CT images at staging and post-treatment surveillance. Surveillance scans corresponded to the last scan prior to the development of CT-detectable CRC liver metastases in 29 patients (median time interval, 6 months), and these were compared with interval-matched surveillance scans in 60 CRC patients who did not develop liver metastases. Predictive models of liver metastasis-free survival and overall survival were built using regularised Cox proportional hazards regression.

RESULTS

Texture features did not significantly differ between cases and controls. For Cox models using all features as predictors, all coefficients were shrunk to zero, suggesting no association between any CT texture features and outcomes. Prognostic indices derived from entropy features at surveillance CT incorrectly classified patients into risk groups for future liver metastases (p < 0.001).

CONCLUSIONS

On surveillance CT scans immediately prior to the development of CRC liver metastases, we found no evidence suggesting that changes in identifiable hepatic texture features were predictive of their development.

KEY POINTS

• No correlation between liver texture features and metastasis-free survival was observed. • Liver texture features incorrectly classified patients into risk groups for liver metastases. • Standardised texture analysis workflows need to be developed to improve research reproducibility.

摘要

目的

在结直肠癌(CRC)患者发生 CT 可检测的肝转移之前,确定腹部 CT 中是否存在可识别的肝纹理特征。

方法

在分期和治疗后监测的门静脉期 CT 图像上,从肝实质中提取了 4 个滤波直方图纹理特征(标准差、偏度、熵和峰度)。29 例患者(中位时间间隔 6 个月)的监测扫描对应于最后一次 CT 检测到 CRC 肝转移前的扫描,将这些扫描与 60 例未发生肝转移的 CRC 患者的间隔匹配监测扫描进行比较。使用正则化 Cox 比例风险回归建立无肝转移生存和总体生存的预测模型。

结果

纹理特征在病例组和对照组之间没有显著差异。对于使用所有特征作为预测因子的 Cox 模型,所有系数都收缩为零,表明任何 CT 纹理特征与结果之间均无关联。在监测 CT 上使用熵特征得出的预后指数错误地将患者分类为未来肝转移的风险组(p < 0.001)。

结论

在 CRC 肝转移发生前的监测 CT 扫描上,我们没有发现任何证据表明可识别的肝纹理特征的变化可预测其发展。

关键点

• 未观察到肝纹理特征与无转移生存之间存在相关性。• 肝纹理特征错误地将患者分类为肝转移的风险组。• 需要开发标准化的纹理分析工作流程,以提高研究的可重复性。

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本文引用的文献

1
Characterization of PET/CT images using texture analysis: the past, the present… any future?
Eur J Nucl Med Mol Imaging. 2017 Jan;44(1):151-165. doi: 10.1007/s00259-016-3427-0. Epub 2016 Jun 6.
2
Radiomics: Images Are More than Pictures, They Are Data.
Radiology. 2016 Feb;278(2):563-77. doi: 10.1148/radiol.2015151169. Epub 2015 Nov 18.
3
Measuring Computed Tomography Scanner Variability of Radiomics Features.
Invest Radiol. 2015 Nov;50(11):757-65. doi: 10.1097/RLI.0000000000000180.
4
False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review.
PLoS One. 2015 May 4;10(5):e0124165. doi: 10.1371/journal.pone.0124165. eCollection 2015.
8
External validation of a Cox prognostic model: principles and methods.
BMC Med Res Methodol. 2013 Mar 6;13:33. doi: 10.1186/1471-2288-13-33.
9
Radiomics: the process and the challenges.
Magn Reson Imaging. 2012 Nov;30(9):1234-48. doi: 10.1016/j.mri.2012.06.010. Epub 2012 Aug 13.

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