Virtual biopsies for breast cancer using MCR-ALS perfusion-based biomarkers and double cross-validation PLS-DA

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-05-28 DOI:10.1016/j.chemolab.2024.105152

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

{"title":"Virtual biopsies for breast cancer using MCR-ALS perfusion-based biomarkers and double cross-validation PLS-DA","authors":"E. Aguado-Sarrió , J.M. Prats-Montalbán , J. Camps-Herrero , A. Ferrer","doi":"10.1016/j.chemolab.2024.105152","DOIUrl":null,"url":null,"abstract":"<div><p>Functional MRI is, currently, the most sensitive technique in breast cancer for detecting early tumors, and perfusion (DCE-MRI) has become the most important sequence to depict and characterize angiogenesis and neovascularization. In this work, we propose the use of new biomarkers that are related to clear physiological phenomena, obtained from MCR-ALS as an alternative to curve-based pseudo-biomarkers and pharmacokinetics models. In order to provide a discrimination and prediction model between healthy tissue and cancer, we propose using PLS-DA with double cross-validation (2CV) and variable selection, repeated several times and obtaining excellent average results for the performance indexes (f-score: 0.9149, MCC: 0.8538, AUROC: 0.8794). After selecting the optimal prediction model, a unique probabilistic map called “virtual biopsy” that shows in different colors the probability that each pixel of the image has a tumor behavior is obtained, helping the specialist with the identification and characterization of breast tumors with only one easy-to-interpret biomarker map.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"250 ","pages":"Article 105152"},"PeriodicalIF":3.7000,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169743924000923/pdfft?md5=a737eedaef6f346a82a2ab4bc9a92f6d&pid=1-s2.0-S0169743924000923-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/S0169743924000923","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

Functional MRI is, currently, the most sensitive technique in breast cancer for detecting early tumors, and perfusion (DCE-MRI) has become the most important sequence to depict and characterize angiogenesis and neovascularization. In this work, we propose the use of new biomarkers that are related to clear physiological phenomena, obtained from MCR-ALS as an alternative to curve-based pseudo-biomarkers and pharmacokinetics models. In order to provide a discrimination and prediction model between healthy tissue and cancer, we propose using PLS-DA with double cross-validation (2CV) and variable selection, repeated several times and obtaining excellent average results for the performance indexes (f-score: 0.9149, MCC: 0.8538, AUROC: 0.8794). After selecting the optimal prediction model, a unique probabilistic map called “virtual biopsy” that shows in different colors the probability that each pixel of the image has a tumor behavior is obtained, helping the specialist with the identification and characterization of breast tumors with only one easy-to-interpret biomarker map.

查看原文本刊更多论文

利用基于 MCR-ALS 灌注的生物标记和双交叉验证 PLS-DA 对乳腺癌进行虚拟活检

功能磁共振成像（Functional MRI）是目前乳腺癌检测早期肿瘤最灵敏的技术，而灌注（DCE-MRI）已成为描述血管生成和新生血管特征的最重要序列。在这项工作中，我们建议使用从 MCR-ALS 中获得的与明确生理现象相关的新生物标记物，以替代基于曲线的伪生物标记物和药代动力学模型。为了提供健康组织和癌症之间的鉴别和预测模型，我们建议使用双交叉验证（2CV）和变量选择的 PLS-DA，重复多次后，性能指标的平均结果非常好（f-score：0.9149，MCC：0.8538，AUROC：0.8794）。在选择出最佳预测模型后，就得到了一个被称为 "虚拟活检 "的独特概率图，该图以不同颜色显示图像中每个像素具有肿瘤行为的概率，只需一张易于理解的生物标志物图就能帮助专家识别乳腺肿瘤并确定其特征。

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.