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用超声信号统计和背向散射积分监测新辅助化疗的乳腺癌反应。

Monitoring breast cancer response to neoadjuvant chemotherapy with ultrasound signal statistics and integrated backscatter.

机构信息

Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland.

Radiology Department, Cancer Center and Institute of Oncology, M. Skłodowska-Curie Memorial, Warsaw, Poland.

出版信息

PLoS One. 2019 Mar 14;14(3):e0213749. doi: 10.1371/journal.pone.0213749. eCollection 2019.

DOI:10.1371/journal.pone.0213749
PMID:30870478
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6417657/
Abstract

BACKGROUND

Neoadjuvant chemotherapy (NAC) is used in patients with breast cancer to reduce tumor focus, metastatic risk, and patient mortality. Monitoring NAC effects is necessary to capture resistant patients and stop or change treatment. The existing methods for evaluating NAC results have some limitations. The aim of this study was to assess the tumor response at an early stage, after the first doses of the NAC, based on the variability of the backscattered ultrasound energy, and backscatter statistics. The backscatter statistics has not previously been used to monitor NAC effects.

METHODS

The B-mode ultrasound images and raw radio frequency data from breast tumors were obtained using an ultrasound scanner before chemotherapy and 1 week after each NAC cycle. The study included twenty-four malignant breast cancers diagnosed in sixteen patients and qualified for neoadjuvant treatment before surgery. The shape parameter of the homodyned K distribution and integrated backscatter, along with the tumor size in the longest dimension, were determined based on ultrasound data and used as markers for NAC response. Cancer tumors were assigned to responding and non-responding groups, according to histopathological evaluation, which was a reference in assessing the utility of markers. Statistical analysis was performed to rate the ability of markers to predict the final NAC response based on data obtained after subsequent therapeutic doses.

RESULTS

Statistically significant differences (p<0.05) between groups were obtained after 2, 3, 4, and 5 doses of NAC for quantitative ultrasound markers and after 5 doses for the assessment based on maximum tumor dimension. Statistical analysis showed that, after the second and third NAC courses the classification based on integrated backscatter marker was characterized by an AUC of 0.69 and 0.82, respectively. The introduction of the second quantitative marker describing the statistical properties of scattering increased the corresponding AUC values to 0.82 and 0.91.

CONCLUSIONS

Quantitative ultrasound information can characterize the tumor's pathological response better and at an earlier stage of therapy than the assessment of the reduction of its dimensions. The introduction of statistical parameters of ultrasonic backscatter to monitor the effects of chemotherapy can increase the effectiveness of monitoring and contribute to a better personalization of NAC therapy.

摘要

背景

新辅助化疗(NAC)用于乳腺癌患者,以缩小肿瘤焦点、降低转移风险和患者死亡率。监测 NAC 效果对于捕捉耐药患者并停止或改变治疗非常必要。目前评估 NAC 结果的方法存在一些局限性。本研究旨在评估 NAC 第一疗程后早期的肿瘤反应,基于背散射超声能量的变化和背散射统计。背散射统计以前没有用于监测 NAC 效果。

方法

在化疗前和每个 NAC 周期后 1 周,使用超声扫描仪获得乳腺癌的 B 型超声图像和原始射频数据。该研究包括 16 名患者的 24 例恶性乳腺癌,这些患者在手术前符合新辅助治疗的条件。根据超声数据确定同态 K 分布的形状参数和积分背散射,以及最长维度上的肿瘤大小,用作 NAC 反应的标志物。根据组织病理学评估将癌症肿瘤分为反应组和非反应组,该评估是评估标志物效用的参考。进行统计分析,以根据后续治疗剂量获得的数据评估标志物预测最终 NAC 反应的能力。

结果

在接受 2、3、4 和 5 剂 NAC 后,定量超声标志物的组间差异具有统计学意义(p<0.05),而基于最大肿瘤尺寸的评估则在接受 5 剂 NAC 后具有统计学意义。统计分析表明,在第二和第三 NAC 疗程后,基于积分背散射标志物的分类特征 AUC 分别为 0.69 和 0.82。引入描述散射统计特性的第二个定量标志物将相应的 AUC 值分别提高到 0.82 和 0.91。

结论

与评估其尺寸的减小相比,定量超声信息可以更好地和更早地描述肿瘤的病理反应。引入超声背散射的统计参数来监测化疗效果可以提高监测的有效性,并有助于更好地实现 NAC 治疗的个体化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fe6/6417657/38f2cc400576/pone.0213749.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fe6/6417657/12dd8b56c71e/pone.0213749.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fe6/6417657/a62db818ed2c/pone.0213749.g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fe6/6417657/38f2cc400576/pone.0213749.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fe6/6417657/12dd8b56c71e/pone.0213749.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fe6/6417657/a62db818ed2c/pone.0213749.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fe6/6417657/6e663fab30c7/pone.0213749.g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fe6/6417657/38f2cc400576/pone.0213749.g006.jpg

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