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拉曼光谱和机器学习揭示浸润性乳腺癌中的生物分子改变。

Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer.

机构信息

Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada.

Centre de recherche du Centre hospitalier de l'Université de Montréal, Montreal, Quebec, Canada.

出版信息

J Biomed Opt. 2023 Mar;28(3):036009. doi: 10.1117/1.JBO.28.3.036009. Epub 2023 Mar 30.

DOI:10.1117/1.JBO.28.3.036009
PMID:37009577
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10062385/
Abstract

SIGNIFICANCE

As many as 60% of patients with early stage breast cancer undergo breast-conserving surgery. Of those, 20% to 35% need a second surgery because of incomplete resection of the lesions. A technology allowing detection of cancer could reduce re-excision procedure rates and improve patient survival.

AIM

Raman spectroscopy was used to measure the spectral fingerprint of normal breast and cancer tissue . The aim was to build a machine learning model and to identify the biomolecular bands that allow one to detect invasive breast cancer.

APPROACH

The system was used to interrogate specimens from 20 patients undergoing lumpectomy, mastectomy, or breast reduction surgery. This resulted in 238 measurements spatially registered with standard histology classifying tissue as cancer, normal, or fat. A technique based on support vector machines led to the development of predictive models, and their performance was quantified using a receiver-operating-characteristic analysis.

RESULTS

Raman spectroscopy combined with machine learning detected normal breast from ductal or lobular invasive cancer with a sensitivity of 93% and a specificity of 95%. This was achieved using a model based on only two spectral bands, including the peaks associated with C-C stretching of proteins around and the symmetric ring breathing at associated with phenylalanine.

CONCLUSIONS

Detection of cancer on the margins of surgically resected breast specimen is feasible with Raman spectroscopy.

摘要

意义

多达 60%的早期乳腺癌患者接受保乳手术。其中,20%至 35%的患者因病变切除不完全需要进行第二次手术。一种能够检测癌症的技术可以降低再次切除手术的比率,并提高患者的生存率。

目的

拉曼光谱用于测量正常乳房和癌症组织的光谱指纹。目的是构建一个机器学习模型,并确定允许检测浸润性乳腺癌的生物分子带。

方法

该系统用于询问 20 名接受保乳术、乳房切除术或乳房缩小术的患者的标本。这导致了 238 次测量,这些测量在空间上与标准组织学进行了注册,将组织分类为癌症、正常或脂肪。基于支持向量机的技术导致了预测模型的发展,并使用接收者操作特征分析来量化它们的性能。

结果

拉曼光谱结合机器学习技术可以检测出导管或小叶浸润性癌的正常乳房,其灵敏度为 93%,特异性为 95%。这是通过使用仅基于两个光谱带的模型实现的,包括与蛋白质 C-C 拉伸相关的峰值和与苯丙氨酸相关的对称环呼吸峰 。

结论

拉曼光谱技术可以在手术切除的乳房标本边缘检测到癌症。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/86e954c55962/JBO-028-036009-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/9f0d67deed92/JBO-028-036009-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/a49f16c6bf4d/JBO-028-036009-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/0c6dddd4ae4d/JBO-028-036009-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/631fe926edf1/JBO-028-036009-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/351fa36eae11/JBO-028-036009-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/5ce345679f04/JBO-028-036009-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/d7163c80e78b/JBO-028-036009-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/86e954c55962/JBO-028-036009-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/9f0d67deed92/JBO-028-036009-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/a49f16c6bf4d/JBO-028-036009-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/0c6dddd4ae4d/JBO-028-036009-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/631fe926edf1/JBO-028-036009-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/351fa36eae11/JBO-028-036009-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/5ce345679f04/JBO-028-036009-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/d7163c80e78b/JBO-028-036009-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed91/10062385/86e954c55962/JBO-028-036009-g008.jpg

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