Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.

IF 2.2 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Tomography Pub Date : 2024-09-02 DOI:10.3390/tomography10090107

Jesutofunmi Ayo Fajemisin, Glebys Gonzalez, Stephen A Rosenberg, Ghanim Ullah, Gage Redler, Kujtim Latifi, Eduardo G Moros, Issam El Naqa

{"title":"Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.","authors":"Jesutofunmi Ayo Fajemisin, Glebys Gonzalez, Stephen A Rosenberg, Ghanim Ullah, Gage Redler, Kujtim Latifi, Eduardo G Moros, Issam El Naqa","doi":"10.3390/tomography10090107","DOIUrl":null,"url":null,"abstract":"<p><p>Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly impacted the imaging and treatment of cancers. Radiomics is the process of extracting high-dimensional features from medical images. Several studies have shown that these extracted features may be used to build machine-learning models for the prediction of treatment outcomes of cancer patients. Various feature selection techniques and machine models interrogate the relevant radiomics features for predicting cancer treatment outcomes. This study aims to provide an overview of MRI radiomics features used in predicting clinical treatment outcomes with machine learning techniques. The review includes examples from different disease sites. It will also discuss the impact of magnetic field strength, sample size, and other characteristics on outcome prediction performance.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 9","pages":"1439-1454"},"PeriodicalIF":2.2000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11435563/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tomography","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/tomography10090107","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly impacted the imaging and treatment of cancers. Radiomics is the process of extracting high-dimensional features from medical images. Several studies have shown that these extracted features may be used to build machine-learning models for the prediction of treatment outcomes of cancer patients. Various feature selection techniques and machine models interrogate the relevant radiomics features for predicting cancer treatment outcomes. This study aims to provide an overview of MRI radiomics features used in predicting clinical treatment outcomes with machine learning techniques. The review includes examples from different disease sites. It will also discuss the impact of magnetic field strength, sample size, and other characteristics on outcome prediction performance.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

磁共振引导的癌症治疗放射组学和响应预测的机器学习模型。

磁共振成像（MRI）以准确划分肿瘤和正常组织的软组织而闻名。这一发展对癌症的成像和治疗产生了重大影响。放射组学是从医学图像中提取高维特征的过程。多项研究表明，这些提取的特征可用于建立机器学习模型，以预测癌症患者的治疗效果。各种特征选择技术和机器模型都会询问用于预测癌症治疗结果的相关放射组学特征。本研究旨在概述利用机器学习技术预测临床治疗效果的 MRI 放射组学特征。综述包括不同疾病部位的实例。它还将讨论磁场强度、样本大小和其他特征对结果预测性能的影响。

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分钟内获得全文去求助

来源期刊

Tomography Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

2.70

自引率

10.50%

发文量

222

期刊介绍： TomographyTM publishes basic (technical and pre-clinical) and clinical scientific articles which involve the advancement of imaging technologies. Tomography encompasses studies that use single or multiple imaging modalities including for example CT, US, PET, SPECT, MR and hyperpolarization technologies, as well as optical modalities (i.e. bioluminescence, photoacoustic, endomicroscopy, fiber optic imaging and optical computed tomography) in basic sciences, engineering, preclinical and clinical medicine. Tomography also welcomes studies involving exploration and refinement of contrast mechanisms and image-derived metrics within and across modalities toward the development of novel imaging probes for image-based feedback and intervention. The use of imaging in biology and medicine provides unparalleled opportunities to noninvasively interrogate tissues to obtain real-time dynamic and quantitative information required for diagnosis and response to interventions and to follow evolving pathological conditions. As multi-modal studies and the complexities of imaging technologies themselves are ever increasing to provide advanced information to scientists and clinicians. Tomography provides a unique publication venue allowing investigators the opportunity to more precisely communicate integrated findings related to the diverse and heterogeneous features associated with underlying anatomical, physiological, functional, metabolic and molecular genetic activities of normal and diseased tissue. Thus Tomography publishes peer-reviewed articles which involve the broad use of imaging of any tissue and disease type including both preclinical and clinical investigations. In addition, hardware/software along with chemical and molecular probe advances are welcome as they are deemed to significantly contribute towards the long-term goal of improving the overall impact of imaging on scientific and clinical discovery.