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基于肺部 CT 图像的放射组学分析在乳腺癌患者转移早期检测中的应用:一项回顾性队列研究的初步发现。

Radiomics analysis of lung CT image for the early detection of metastases in patients with breast cancer: preliminary findings from a retrospective cohort study.

机构信息

Cancer Therapy and Research Center, Shandong Provincial Hospital affiliated to Shandong University, Shandong University, Jinan, People's Republic of China.

School of Information Science and Engineering, Shandong University, Jinan, People's Republic of China.

出版信息

Eur Radiol. 2020 Aug;30(8):4545-4556. doi: 10.1007/s00330-020-06745-5. Epub 2020 Mar 12.

Abstract

OBJECTIVES

To investigate whether subtle changes in radiomics features are present in lung CT images prior to the development of CT-detectable lung metastases in patients with breast cancer.

METHODS

Thirty-three radiomics features were measured in the metastasis region (MR) and in matched contralateral tissues (non-metastasis region, NMR) of 29 breast cancer patients at the last CT scan, as well as in the corresponding regions of the patients' pre-metastasis scan (pre-MR and pre-NMR). We also compared them with normal lung tissues (control group, CG) from 29 healthy volunteers. Then, 8 patients from the 29 patients with lung metastases and 8 patients who did not develop lung metastases were chosen for further study of the correlation between radiomics parameters and tumor growth.

RESULTS

In the MR vs. NMR and MR vs. CG groups, almost all radiomics features were significantly different. Twenty-six parameters showed significant differences between the pre-MRs and pre-NMRs. Linear fitting demonstrated a significant correlation between 5 features and tumor growth in the metastasis group, but not in the non-metastasis group. Among them, run percentage was the most representative feature. The calculated area under curves (AUCs), based on run percentage for the classification of metastasis and pre-metastasis, were 0.954 and 0.852, respectively.

CONCLUSIONS

Radiomics features may allow early detection of lung metastases before they become visually detectable, and the feature run percentage may be a promising image surrogate marker for the microinvasion of tumor cells into the lung tissue.

KEY POINTS

• The significant differences in radiomics features between pre-MR and pre-NMR are critical for the early detection of lung metastases. • Five radiomics features show a correlation with tumor growth. • The radiomics feature run percentage may be a potential imaging biomarker for the early detection of lung metastases.

摘要

目的

探究乳腺癌患者在 CT 可检测到肺转移前,其肺部 CT 图像的放射组学特征是否发生细微改变。

方法

在 29 例乳腺癌患者的最后一次 CT 扫描中,对转移区域(MR)和配对的对侧组织(非转移区域,NMR)进行了 33 个放射组学特征的测量,同时也对这些患者的转移前扫描的相应区域(前转移区,前 MR 和前非转移区,前 NMR)进行了测量。我们还将这些数据与 29 例健康志愿者的正常肺部组织(对照组,CG)进行了比较。然后,在 29 例有肺转移的患者中,选择了 8 例患者和未发生肺转移的 8 例患者,进一步研究放射组学参数与肿瘤生长之间的相关性。

结果

在 MR 与 NMR 组和 MR 与 CG 组中,几乎所有放射组学特征均有显著差异。在 26 个参数中,前 MR 与前 NMR 之间有显著差异。线性拟合显示,在转移组中,有 5 个特征与肿瘤生长有显著相关性,但在非转移组中没有。其中,游程百分比是最具代表性的特征。基于游程百分比对转移和前转移进行分类的计算曲线下面积(AUCs)分别为 0.954 和 0.852。

结论

放射组学特征可能有助于在肺转移肉眼可见之前,对其进行早期检测,并且特征游程百分比可能是肿瘤细胞向肺部组织微浸润的有前途的影像替代标志物。

关键点

  1. 前 MR 与前 NMR 之间放射组学特征的显著差异对于肺转移的早期检测至关重要。

  2. 有 5 个放射组学特征与肿瘤生长相关。

  3. 放射组学特征游程百分比可能是早期检测肺转移的潜在影像生物标志物。

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