Suppr超能文献

动态弥散光学断层扫描监测乳腺癌新辅助化疗患者的疗效。

Dynamic Diffuse Optical Tomography for Monitoring Neoadjuvant Chemotherapy in Patients with Breast Cancer.

机构信息

From the Departments of Biomedical Engineering (J.E.G., M.F., M.A., A.H.H.) and Electrical Engineering (A.H.H.), Columbia University, 500 W 120th St, Mudd Bldg, ET351, MC 8904, New York, NY 10027; Department of Medicine, Division of Hematology/Oncology (E.A.L., J.A.C., K.D.C., K.K., D.L.H.), Department of Radiology (H.K.K., A.H.H.), Department of Pathology and Cell Biology (H.H.), and Department of Epidemiology (K.D.C., D.L.H.), Columbia University Medical Center, New York, NY.

出版信息

Radiology. 2018 Jun;287(3):778-786. doi: 10.1148/radiol.2018161041. Epub 2018 Feb 12.

Abstract

Purpose To identify dynamic optical imaging features that associate with the degree of pathologic response in patients with breast cancer during neoadjuvant chemotherapy (NAC). Materials and Methods Of 40 patients with breast cancer who participated in a longitudinal study between June 2011 and March 2016, 34 completed the study. There were 13 patients who obtained a pathologic complete response (pCR) and 21 patients who did not obtain a pCR. Imaging data from six subjects were excluded from the study because either the patients dropped out of the study before it was finished or there was an instrumentation malfunction. Two weeks into the treatment regimen, three-dimensional images of both breasts during a breath hold were acquired by using dynamic diffuse optical tomography. Features from the breath-hold traces were used to distinguish between response groups. Receiver operating characteristic (ROC) curves and sensitivity analysis were used to determine the degree of association with 5-month treatment outcome. Results An ROC curve analysis showed that this method could identify patients with a pCR with a positive predictive value of 70.6% (12 of 17), a negative predictive value of 94.1% (16 of 17), a sensitivity of 92.3% (12 of 13), a specificity of 76.2% (16 of 21), and an area under the ROC curve of 0.85. Conclusion Several dynamic optical imaging features obtained within 2 weeks of NAC initiation were identified that showed statistically significant differences between patients with pCR and patients without pCR as determined 5 months after treatment initiation. If confirmed in a larger cohort prospective study, these dynamic imaging features may be used to predict treatment outcome as early as 2 weeks after treatment initiation. RSNA, 2018 Online supplemental material is available for this article.

摘要

目的 确定与接受新辅助化疗(NAC)的乳腺癌患者病理反应程度相关的动态光学成像特征。

材料与方法 纳入 2011 年 6 月至 2016 年 3 月进行的一项纵向研究中的 40 例乳腺癌患者,其中 34 例完成了研究。13 例患者获得了病理完全缓解(pCR),21 例患者未获得 pCR。由于患者在研究完成前退出研究或仪器出现故障,有 6 名患者的影像数据被排除在研究之外。在治疗方案开始后的第 2 周,通过动态漫射光学断层扫描获取屏气时双侧乳房的三维图像。使用呼吸暂停轨迹中的特征来区分反应组。使用受试者工作特征(ROC)曲线和敏感性分析来确定与 5 个月治疗结果的关联程度。

结果 ROC 曲线分析表明,该方法可以识别出 pCR 患者,其阳性预测值为 70.6%(17 例中的 12 例),阴性预测值为 94.1%(17 例中的 16 例),敏感性为 92.3%(13 例中的 12 例),特异性为 76.2%(21 例中的 16 例),ROC 曲线下面积为 0.85。

结论 在 NAC 开始后 2 周内获得的几个动态光学成像特征,在治疗开始后 5 个月确定的 pCR 患者与无 pCR 患者之间显示出统计学显著差异。如果在更大的前瞻性队列研究中得到证实,这些动态成像特征可能早在治疗开始后 2 周就可用于预测治疗结果。

相似文献

1
Dynamic Diffuse Optical Tomography for Monitoring Neoadjuvant Chemotherapy in Patients with Breast Cancer.
Radiology. 2018 Jun;287(3):778-786. doi: 10.1148/radiol.2018161041. Epub 2018 Feb 12.
3
Texture Analysis with 3.0-T MRI for Association of Response to Neoadjuvant Chemotherapy in Breast Cancer.
Radiology. 2020 Jan;294(1):31-41. doi: 10.1148/radiol.2019182718. Epub 2019 Nov 26.
5
Predicting breast cancer response to neoadjuvant chemotherapy using pretreatment diffuse optical spectroscopic texture analysis.
Br J Cancer. 2017 May 9;116(10):1329-1339. doi: 10.1038/bjc.2017.97. Epub 2017 Apr 18.
10
The feasibility of dedicated breast PET for the assessment of residual tumor after neoadjuvant chemotherapy.
Jpn J Radiol. 2019 Jan;37(1):81-87. doi: 10.1007/s11604-018-0785-5. Epub 2018 Nov 3.

引用本文的文献

1
Detection of breast cancer using machine learning on time-series diffuse optical transillumination data.
J Biomed Opt. 2024 Nov;29(11):115001. doi: 10.1117/1.JBO.29.11.115001. Epub 2024 Nov 11.
2
Monte Carlo simulation of spatial frequency domain imaging for breast tumors during compression.
J Biomed Opt. 2024 Sep;29(9):096001. doi: 10.1117/1.JBO.29.9.096001. Epub 2024 Sep 14.
6
Mammography with deep learning for breast cancer detection.
Front Oncol. 2024 Feb 12;14:1281922. doi: 10.3389/fonc.2024.1281922. eCollection 2024.

本文引用的文献

1
Diagnosis of pathological complete response to neoadjuvant chemotherapy in breast cancer by minimal invasive biopsy techniques.
Br J Cancer. 2015 Dec 1;113(11):1565-70. doi: 10.1038/bjc.2015.381. Epub 2015 Nov 10.
5
Predicting breast tumor response to neoadjuvant chemotherapy with diffuse optical spectroscopic tomography prior to treatment.
Clin Cancer Res. 2014 Dec 1;20(23):6006-15. doi: 10.1158/1078-0432.CCR-14-1415. Epub 2014 Oct 7.
8
Selecting the neoadjuvant treatment by molecular subtype: how to maximize the benefit?
Breast. 2013 Aug;22 Suppl 2:S149-51. doi: 10.1016/j.breast.2013.07.028.
9
Optical biomarkers for breast cancer derived from dynamic diffuse optical tomography.
J Biomed Opt. 2013 Sep;18(9):096012. doi: 10.1117/1.JBO.18.9.096012.
10
Response-guided neoadjuvant chemotherapy for breast cancer.
J Clin Oncol. 2013 Oct 10;31(29):3623-30. doi: 10.1200/JCO.2012.45.0940. Epub 2013 Sep 3.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验