论著摘要 |【AI-MR】三维磁共振成像脑转移的计算机辅助检测:观察者的性能研究(双语版)

2018-02-06 10:29:50 admin 6
标签:   人工智能 MR 计算机辅助检测 CAD 脑转移 人工神经网络 K均值

Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study.

发表日期: 2017.06.08   来源:PLoS One. 2017 Jun 8;12(6):e0178265.

作者:

Sunwoo L1,2, Kim YJ3,4, Choi SH1,5, Kim KG3, Kang JH5 , Kang Y6, Bae YJ1,2, Yoo RE1,5, Kim J1,2, Lee KJ1,2, Lee SH4, Choi BS1,2, Jung C1,2 , Sohn CH1,5, Kim JH1,2.

作者介绍:

1. Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.

2. Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.

3. Department of Biomedical Engineering, Gachon University, Incheon, Korea.

4. Department of Plasma Bio Display, Kwangwoon University, Seoul, Korea.

5. Department of Radiology, Seoul National University Hospital, Seoul, Korea.

6. Department of Radiology, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, Korea.

摘要

Abstact

目的

评估脑转移计算机辅助检测(CAD)对放射科医师诊断性能的影响,以三维脑磁共振(MR)成像为基础,采用随访成像和共识作为参考标准。

Purspose

To assess the effect of computer-aided detection (CAD) of brain metastasis (BM) on radiologists' diagnostic performance in interpreting three-dimensional brain magnetic resonance (MR) imaging using follow-up imaging and consensus as the reference standard.

方法

机构审查委员会批准了这个回顾性研究。 研究队列由连续110例BM患者和30例无BM患者组成。训练数据集包括80名患有450个BM结节的患者的MR图像。测试集包括30名患有134个BM结节的患者和30个没有BM的患者的MR图像。我们开发了一个用于BM检测的CAD系统,使用模板匹配和K均值聚类算法进行候选检测,并使用人工神经网络降低假阳性。四位评价者(两位神经放射科医师和两位放射科医师)在使用CAD之前和之后依次解释测试组图像。 分析了每例病例的灵敏度,假阳性(FP)和阅读时间。使用刀切自由响应受试者操作特性(JAFROC)方法来确定诊断准确度的改善。

Materials and methods

The institutional review board approved this retrospective study. The study cohort consisted of 110 consecutive patients with BM and 30 patients without BM. The training data set included MR images of 80 patients with 450 BM nodules. The test set included MR images of 30 patients with 134 BM nodules and 30 patients without BM. We developed a CAD system for BM detection using template-matching and K-means clustering algorithms for candidate detection and an artificial neural network for false-positive reduction. Four reviewers (two neuroradiologists and two radiology residents) interpreted the test set images before and after the use of CAD in a sequential manner. The sensitivity, false positive (FP) per case, and reading time were analyzed. A jackknife free-response receiver operating characteristic (JAFROC) method was used to determine the improvement in the diagnostic accuracy.

结果

CAD的敏感性为87.3%,每例302.4的FP。根据JAFROC分析(p < 0.01),CAD使四位评价者的诊断性能显着改善,其品质因数(FOM)为0.874(无CAD)与0.898(与CAD)。统计上显着的改善只有经验较少的评论者(FOM无vs. CAD,0.834 vs. 0.877,p < 0.01)。审查CAD结果所需的额外时间约为72秒(占总审阅时间的40%)。

Results

The sensitivity of CAD was 87.3% with an FP per case of 302.4. CAD significantly improved the diagnostic performance of the four reviewers with a figure-of-merit (FOM) of 0.874 (without CAD) vs. 0.898 (with CAD) according to JAFROC analysis (p < 0.01). Statistically significant improvement was noted only for less-experienced reviewers (FOM without vs. with CAD, 0.834 vs. 0.877, p < 0.01). The additional time required to review the CAD results was approximately 72 sec (40% of the total review time).

结论

CAD作为第二个阅读器可以帮助放射科医师改善他们的诊断在磁共振成像检测中的性能,特别是对磁共振成像经验不足的审稿人。

Conclusions

CAD as a second reader helps radiologists improve their diagnostic performance in the detection of BM on MR imaging, particularly for less-experienced reviewers.

阅读原文:PMID: 28594923  PMCID: PMC5464563  DOI: 10.1371/journal.pone.0178265


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