Scholarlink

<p>Low-dose computed tomography attenuation correction (CTAC) scans are used in hybrid myocardial perfusion imaging (MPI) for attenuation correction and coronary calcium scoring, and contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI) approach. A multi-structure model segmented 33 structures and quantified 15 radiomics features in each organ in 10,480 patients from 4 sites. Coronary calcium and epicardial fat measures were obtained from separate AI models. The area under the receiver-operating characteristic curves (AUC) for all-cause mortality prediction of the model utilizing MPI, CT, stress test, and clinical features was 0.80 (95% confidence interval [0.74–0.87]), which was higher than for coronary calcium (0.64 [0.57–0.71]) or perfusion (0.62 [0.55–0.70]), with <i>p</i> &lt; 0.001 for both. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing hybrid MPI.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月13日

查看全文
  • Anna M. Marcinkiewicz,
  • Wenhao Zhang,
  • Aakash Shanbhag,
  • Robert J. H. Miller,
  • Mark Lemley,
  • Giselle Ramirez,
  • Mikolaj Buchwald,
  • Aditya Killekar,
  • Paul B. Kavanagh,
  • Attila Feher,
  • Edward J. Miller,
  • Andrew J. Einstein,
  • Terrence D. Ruddy,
  • Joanna X. Liang,
  • Valerie Builoff,
  • David Ouyang,
  • Daniel S. Berman,
  • Damini Dey,
  • Piotr J. Slomka

摘要

Low-dose computed tomography attenuation correction (CTAC) scans are used in hybrid myocardial perfusion imaging (MPI) for attenuation correction and coronary calcium scoring, and contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI) approach. A multi-structure model segmented 33 structures and quantified 15 radiomics features in each organ in 10,480 patients from 4 sites. Coronary calcium and epicardial fat measures were obtained from separate AI models. The area under the receiver-operating characteristic curves (AUC) for all-cause mortality prediction of the model utilizing MPI, CT, stress test, and clinical features was 0.80 (95% confidence interval [0.74–0.87]), which was higher than for coronary calcium (0.64 [0.57–0.71]) or perfusion (0.62 [0.55–0.70]), with p < 0.001 for both. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing hybrid MPI.