迈向精准诊断：使用多参数 3 T MRI 识别恶性眼眶肿瘤的机器学习。

IF 7 1区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Investigative Radiology Pub Date : 2024-04-11 DOI:10.1097/rli.0000000000001076

Emma O'Shaughnessy, Lucile Senicourt, Natasha Mambour, Julien Savatovsky, Loïc Duron, Augustin Lecler

{"title":"迈向精准诊断：使用多参数 3 T MRI 识别恶性眼眶肿瘤的机器学习。","authors":"Emma O'Shaughnessy, Lucile Senicourt, Natasha Mambour, Julien Savatovsky, Loïc Duron, Augustin Lecler","doi":"10.1097/rli.0000000000001076","DOIUrl":null,"url":null,"abstract":"Orbital tumors present a diagnostic challenge due to their varied locations and histopathological differences. Although recent advancements in imaging have improved diagnosis, classification remains a challenge. The integration of artificial intelligence in radiology and ophthalmology has demonstrated promising outcomes.","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Toward Precision Diagnosis: Machine Learning in Identifying Malignant Orbital Tumors With Multiparametric 3 T MRI.\",\"authors\":\"Emma O'Shaughnessy, Lucile Senicourt, Natasha Mambour, Julien Savatovsky, Loïc Duron, Augustin Lecler\",\"doi\":\"10.1097/rli.0000000000001076\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Orbital tumors present a diagnostic challenge due to their varied locations and histopathological differences. Although recent advancements in imaging have improved diagnosis, classification remains a challenge. The integration of artificial intelligence in radiology and ophthalmology has demonstrated promising outcomes.\",\"PeriodicalId\":14486,\"journal\":{\"name\":\"Investigative Radiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2024-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Investigative Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/rli.0000000000001076\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Investigative Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/rli.0000000000001076","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

摘要

眼眶肿瘤的位置和组织病理学差异各不相同，给诊断带来了挑战。虽然近期成像技术的进步改善了诊断，但分类仍是一项挑战。人工智能与放射学和眼科学的结合已取得了可喜的成果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Toward Precision Diagnosis: Machine Learning in Identifying Malignant Orbital Tumors With Multiparametric 3 T MRI.

Orbital tumors present a diagnostic challenge due to their varied locations and histopathological differences. Although recent advancements in imaging have improved diagnosis, classification remains a challenge. The integration of artificial intelligence in radiology and ophthalmology has demonstrated promising outcomes.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Investigative Radiology 医学-核医学

CiteScore

15.10

自引率

16.40%

发文量

188

审稿时长

4-8 weeks

期刊介绍： Investigative Radiology publishes original, peer-reviewed reports on clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, and related modalities. Emphasis is on early and timely publication. Primarily research-oriented, the journal also includes a wide variety of features of interest to clinical radiologists.