Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer.

IF 2 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of Applied Clinical Medical Physics Pub Date : 2024-12-20 DOI:10.1002/acm2.14614

Amin Talebi, Ahmad Bitarafan-Rajabi, Azin Alizadeh-Asl, Parisa Seilani, Benyamin Khajetash, Ghasem Hajianfar, Meysam Tavakoli

{"title":"Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer.","authors":"Amin Talebi, Ahmad Bitarafan-Rajabi, Azin Alizadeh-Asl, Parisa Seilani, Benyamin Khajetash, Ghasem Hajianfar, Meysam Tavakoli","doi":"10.1002/acm2.14614","DOIUrl":null,"url":null,"abstract":"Purpose: Cardiotoxicity is one of the major concerns in breast cancer treatment, significantly affecting patient outcomes. To improve the likelihood of favorable outcomes for breast cancer survivors, it is essential to carefully balance the potential advantages of treatment methods with the risks of harm to healthy tissues, including the heart. There is currently a lack of comprehensive, data-driven evidence on effective risk stratification strategies. The aim of this study is to investigate the prediction of cardiotoxicity using machine learning methods combined with radiomics, clinical, and dosimetric features.Materials and methods: A cohort of 83 left-sided breast cancer patients without a history of cardiac disease was examined. Two- and three-dimensional echocardiography were performed before and after 6 months of treatment to evaluate cardiotoxicity. Cardiac dose-volume histograms, demographic data, echocardiographic parameters, and ultrasound imaging radiomics features were collected for all patients. Toxicity modeling was developed with three feature selection methods and five classifiers in four separate groups (Dosimetric, Dosimetric + Demographic, Dosimetric + Demographic + Clinical, and Dosimetric + Demographic + Clinical + Imaging). The prediction performance of the models was validated using five-fold cross-validation and evaluated by AUCs.Results: 58% of patients showed cardiotoxicity 6 months after treatment. Mean left ventricular ejection fraction and Global longitudinal strain decreased significantly compared to pre-treatment (p-value < 0.001). After feature selection and prediction modeling, the Dosimetric, Dosimetric + Demographic, Dosimetric + Demographic + Clinical, Dosimetric + Demographic + Clinical + Imaging models showed prediction performance (AUC) up to 73%, 75%, 85%, and 97%, respectively.Conclusion: Incorporating clinical and imaging features along with dose descriptors are beneficial for predicting cardiotoxicity after radiotherapy.","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14614"},"PeriodicalIF":2.0000,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Clinical Medical Physics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/acm2.14614","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose: Cardiotoxicity is one of the major concerns in breast cancer treatment, significantly affecting patient outcomes. To improve the likelihood of favorable outcomes for breast cancer survivors, it is essential to carefully balance the potential advantages of treatment methods with the risks of harm to healthy tissues, including the heart. There is currently a lack of comprehensive, data-driven evidence on effective risk stratification strategies. The aim of this study is to investigate the prediction of cardiotoxicity using machine learning methods combined with radiomics, clinical, and dosimetric features.

Materials and methods: A cohort of 83 left-sided breast cancer patients without a history of cardiac disease was examined. Two- and three-dimensional echocardiography were performed before and after 6 months of treatment to evaluate cardiotoxicity. Cardiac dose-volume histograms, demographic data, echocardiographic parameters, and ultrasound imaging radiomics features were collected for all patients. Toxicity modeling was developed with three feature selection methods and five classifiers in four separate groups (Dosimetric, Dosimetric + Demographic, Dosimetric + Demographic + Clinical, and Dosimetric + Demographic + Clinical + Imaging). The prediction performance of the models was validated using five-fold cross-validation and evaluated by AUCs.

Results: 58% of patients showed cardiotoxicity 6 months after treatment. Mean left ventricular ejection fraction and Global longitudinal strain decreased significantly compared to pre-treatment (p-value < 0.001). After feature selection and prediction modeling, the Dosimetric, Dosimetric + Demographic, Dosimetric + Demographic + Clinical, Dosimetric + Demographic + Clinical + Imaging models showed prediction performance (AUC) up to 73%, 75%, 85%, and 97%, respectively.

Conclusion: Incorporating clinical and imaging features along with dose descriptors are beneficial for predicting cardiotoxicity after radiotherapy.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Applied Clinical Medical Physics 医学-核医学

CiteScore

3.60

自引率

19.00%

发文量

331

审稿时长

3 months

期刊介绍： Journal of Applied Clinical Medical Physics is an international Open Access publication dedicated to clinical medical physics. JACMP welcomes original contributions dealing with all aspects of medical physics from scientists working in the clinical medical physics around the world. JACMP accepts only online submission. JACMP will publish: -Original Contributions: Peer-reviewed, investigations that represent new and significant contributions to the field. Recommended word count: up to 7500. -Review Articles: Reviews of major areas or sub-areas in the field of clinical medical physics. These articles may be of any length and are peer reviewed. -Technical Notes: These should be no longer than 3000 words, including key references. -Letters to the Editor: Comments on papers published in JACMP or on any other matters of interest to clinical medical physics. These should not be more than 1250 (including the literature) and their publication is only based on the decision of the editor, who occasionally asks experts on the merit of the contents. -Book Reviews: The editorial office solicits Book Reviews. -Announcements of Forthcoming Meetings: The Editor may provide notice of forthcoming meetings, course offerings, and other events relevant to clinical medical physics. -Parallel Opposed Editorial: We welcome topics relevant to clinical practice and medical physics profession. The contents can be controversial debate or opposed aspects of an issue. One author argues for the position and the other against. Each side of the debate contains an opening statement up to 800 words, followed by a rebuttal up to 500 words. Readers interested in participating in this series should contact the moderator with a proposed title and a short description of the topic