New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-06-26 DOI:10.1016/j.chemolab.2024.105171

C. Ortiz-Abellán , E. Aguado-Sarrió , J.M. Prats-Montalbán , J. Camps-Herrero , A. Ferrer

{"title":"New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models","authors":"C. Ortiz-Abellán , E. Aguado-Sarrió , J.M. Prats-Montalbán , J. Camps-Herrero , A. Ferrer","doi":"10.1016/j.chemolab.2024.105171","DOIUrl":null,"url":null,"abstract":"<div><p>Currently, magnetic resonance imaging is the most sensitive imaging technique for detecting cancerous processes in early stages. As for breast cancer, due to the tubular structure of the tissue, being formed by ducts, anisotropic diffusion should be considered instead of the general isotropic diffusion. Anisotropic diffusion is studied by applying a technique called Diffusion Tensor Imaging (DTI), where the diffusion gradient is applied by changing the magnetic field in several spatial directions.</p><p>To date, the application of Multivariate Curve Resolution (MCR) models in diffusion sequences has demonstrated its ability to develop cancer biomarkers of easy clinical interpretation in the case of isotropic tissues, such as the prostate. But so far, it has never been applied in the case of anisotropic tissues, as the breast.</p><p>Therefore, the main objective of this work is to obtain easy-to-interpret imaging biomarkers useful for early breast cancer diagnosis from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models. A classification model to identify healthy and tumor affected pixels is also proposed.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"251 ","pages":"Article 105171"},"PeriodicalIF":3.7000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169743924001114/pdfft?md5=bfa9e402dd60fbdcd42e8d99cb32d250&pid=1-s2.0-S0169743924001114-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemometrics and Intelligent Laboratory Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169743924001114","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

Currently, magnetic resonance imaging is the most sensitive imaging technique for detecting cancerous processes in early stages. As for breast cancer, due to the tubular structure of the tissue, being formed by ducts, anisotropic diffusion should be considered instead of the general isotropic diffusion. Anisotropic diffusion is studied by applying a technique called Diffusion Tensor Imaging (DTI), where the diffusion gradient is applied by changing the magnetic field in several spatial directions.

To date, the application of Multivariate Curve Resolution (MCR) models in diffusion sequences has demonstrated its ability to develop cancer biomarkers of easy clinical interpretation in the case of isotropic tissues, such as the prostate. But so far, it has never been applied in the case of anisotropic tissues, as the breast.

Therefore, the main objective of this work is to obtain easy-to-interpret imaging biomarkers useful for early breast cancer diagnosis from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models. A classification model to identify healthy and tumor affected pixels is also proposed.

查看原文本刊更多论文

利用多变量曲线分辨率 (MCR) 模型，从基于扩散张量的扩散磁共振成像中提取新的乳腺癌生物标记物

目前，磁共振成像是早期检测癌症过程最灵敏的成像技术。就乳腺癌而言，由于乳腺组织是由导管形成的管状结构，因此应考虑各向异性扩散，而不是一般的各向同性扩散。研究各向异性扩散的方法是应用一种称为扩散张量成像（DTI）的技术，通过改变多个空间方向的磁场来应用扩散梯度。迄今为止，在扩散序列中应用多变量曲线分辨率（MCR）模型已经证明了其在各向同性组织（如前列腺）中开发易于临床解释的癌症生物标志物的能力。因此，这项工作的主要目的是利用多变量曲线分辨率（MCR）模型，从基于扩散张量的扩散磁共振成像中获得易于解读的成像生物标记，用于早期乳腺癌诊断。此外，还提出了一种用于识别健康像素和受肿瘤影响像素的分类模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.