Spectral precision imaging for early diagnosis of colorectal lesions with CT colonography

CT结肠成像光谱精密成像用于结直肠病变的早期诊断

基本信息

  • 批准号:
    10054168
  • 负责人:
  • 金额:
    $ 12.06万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-12-01 至 2022-11-30
  • 项目状态:
    已结题

项目摘要

Abstract Colon cancer is the second leading cause of cancer deaths for men and women in the United States, even though it could be prevented by early detection and removal of its precursor lesions. Computed tomographic colonography (CTC) could substantially increase the capacity, safety, and patient compliance of colorectal examinations. However, the current standard of cathartic bowel preparation for CTC and optical colonoscopy (OC) is poorly tolerated by patients and has been recognized as a major barrier to colorectal examinations. Our advanced non-cathartic multi-center computer-assisted CTC trial showed that non-cathartic CTC is easily tolerated by patients and that radiologists who use computer-aided detection (CADe) can detect large polyps in size in non-cathartic CTC with high sensitivity, comparable to that of OC. However, SF6-lesions (serrated lesions, flat lesions <3 mm in height, and polyps 6 – 9 mm in size) were a significant source of false negatives in the trial. The challenges of detection and visualization of these SF6-lesions in non-cathartic CTC are caused largely by the inability of the current single-energy CTC technique to differentiate between soft tissues, fecal tagging, and their partial volumes with lumen air. We propose to employ multi-spectral CTC precision imaging and artificial intelligence (AI) to overcome these inherent limitations of non-cathartic CTC. Our goal in this project is to develop a novel deep-learning AI (DEEP-AI) scheme for multi-spectral multi-material (MUSMA) precision imaging, which will use deep super-learning of high-quality spectral CTC (spCTC) precision images to boost the diagnostic performance of non-cathartic CTC. We hypothesize that (1) high-quality MUSMA precision images can be reconstructed from ultra-low-dose (<1 mSv) spCTC scans, (2) DEEP-AI will yield a detection sensitivity for ≥6 mm SF6-lesions comparable to that of OC, and that (3) the use of DEEP-AI as first reader will significantly improve radiologists’ detection performance for SF6-lesions and reduce interpretation time compared with unaided reading, and that it will yield a detection accuracy comparable to that of OC. Our specific aims are (1) to establish a non-cathartic spCTC and MUSMA precision image database, (2) to develop a DEEP-AI Interpretation System for visualization and detection of SF6-lesions, and (3) to evaluate the clinical benefit of the DEEP-AI Interpretation System with non-cathartic spCTC cases. Successful development of the proposed DEEP-AI Interpretation System will substantially improve human readers’ performance in the detection of SF6-lesions from non-cathartic CTC examinations that address the problem of patient adherence to colorectal screening guidelines. Such a scheme will make non-cathartic CTC a highly accurate and acceptable screening option for large populations, leading to an increased colorectal screening rate, promoting early diagnosis of colon cancer, and ultimately reducing mortality due to colon cancer.
摘要 结肠癌是美国男性和女性癌症死亡的第二大原因,甚至 但可通过早期发现和清除其前驱病变来预防。计算机断层扫描 结肠造影术(CTC)可以显著提高结直肠的容量、安全性和患者的依从性 考试。然而,目前用于CTC和光学结肠镜检查的泻剂肠道准备标准 (OC)患者耐受性差,已被认为是结直肠检查的主要障碍。 我们先进的非泻药多中心计算机辅助CTC试验表明,非泻药CTC很容易 患者可以耐受,使用计算机辅助检测(CADE)的放射科医生可以在 在非泻剂CTC中大小具有很高的灵敏度,与OC相当。然而,SF6-损害(锯齿状 病变、高度3毫米的扁平病变和6-9毫米大小的息肉)是假阴性的重要来源 在审判中。在非泻剂CTC中,这些SF6病变的检测和可视化带来了挑战 很大程度上是由于目前的单能量CTC技术无法区分软组织、粪便 标记,以及使用流明空气的部分体积。我们建议采用多光谱CTC精密成像 以及人工智能(AI),以克服非泻剂CTC的这些固有限制。我们在这方面的目标 该项目是开发一种新的多光谱多材料深度学习人工智能(Depth-AI)方案(MUSMA) 精确成像,它将使用高质量光谱CTC(SpCTC)精度图像的深度超级学习 目的:提高非泻剂CTC的诊断性能。我们假设(1)高质量的MUSMA 可以通过超低剂量(1 MSv)spCTC扫描重建精确的图像,(2)深度人工智能将产生 对≥6 mm SF6病变的检测灵敏度与OC相当,并且(3)首次使用Deep-AI Reader将显著提高放射科医生对SF6病变的检测性能,并减少解释 时间与非辅助阅读相比,它将产生与OC相当的检测准确率。我们的 具体目标是(1)建立非泻剂SPCTC和MUSMA精密图像数据库,(2)开发 用于SF6病变可视化和检测的深度人工智能解释系统,以及(3)临床评估 深层人工智能解释系统对非宣泄性SPCTC病例的好处成功地开发了 提出的深度人工智能口译系统将大幅提高人类读者在 从解决患者依从性问题的非泻剂CTC检查中检测SF6病变 结直肠癌筛查指南。这样的方案将使非泻剂CTC成为一种高度准确和 为大量人群提供可接受的筛查选择,从而提高结直肠筛查率,促进 早期诊断结肠癌,并最终降低因结肠癌而导致的死亡率。

项目成果

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HIROYUKI YOSHIDA其他文献

HIROYUKI YOSHIDA的其他文献

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{{ truncateString('HIROYUKI YOSHIDA', 18)}}的其他基金

Survival prediction in patients with progressive fibrosing interstitial lung disease
进行性纤维化间质性肺病患者的生存预测
  • 批准号:
    10644030
  • 财政年份:
    2022
  • 资助金额:
    $ 12.06万
  • 项目类别:
Survival prediction in patients with progressive fibrosing interstitial lung disease
进行性纤维化间质性肺病患者的生存预测
  • 批准号:
    10503417
  • 财政年份:
    2022
  • 资助金额:
    $ 12.06万
  • 项目类别:
Deep radiomic decision support system for colorectal cancer
结直肠癌深度放射组学决策支持系统
  • 批准号:
    9764151
  • 财政年份:
    2017
  • 资助金额:
    $ 12.06万
  • 项目类别:
Spectral precision imaging for early diagnosis of colorectal lesions with CT colonography
CT结肠成像光谱精密成像用于结直肠病变的早期诊断
  • 批准号:
    10308462
  • 财政年份:
    2017
  • 资助金额:
    $ 12.06万
  • 项目类别:
Deep radiomic decision support system for colorectal cancer
结直肠癌深度放射组学决策支持系统
  • 批准号:
    9288493
  • 财政年份:
    2017
  • 资助金额:
    $ 12.06万
  • 项目类别:
Deep radiomic decision support system for colorectal cancer
结直肠癌深度放射组学决策支持系统
  • 批准号:
    9566185
  • 财政年份:
    2017
  • 资助金额:
    $ 12.06万
  • 项目类别:
Dynamic-CT-based biomarker for predicting clinical outcome in CRC
基于动态 CT 的生物标志物用于预测 CRC 的临床结果
  • 批准号:
    8893927
  • 财政年份:
    2014
  • 资助金额:
    $ 12.06万
  • 项目类别:
Dynamic-CT-based biomarker for predicting clinical outcome in CRC
基于动态 CT 的生物标志物用于预测 CRC 的临床结果
  • 批准号:
    8757781
  • 财政年份:
    2014
  • 资助金额:
    $ 12.06万
  • 项目类别:
Cloud-computer-aided diagnostic imaging decision support system
云计算机辅助影像诊断决策支持系统
  • 批准号:
    8848046
  • 财政年份:
    2012
  • 资助金额:
    $ 12.06万
  • 项目类别:
Cloud-computer-aided diagnostic imaging decision support system
云计算机辅助影像诊断决策支持系统
  • 批准号:
    8276007
  • 财政年份:
    2012
  • 资助金额:
    $ 12.06万
  • 项目类别:

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