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Computer-aided detection improves detection of pulmonary nodules in chest radiographs beyond the support by bone-suppressed images

机译:计算机辅助检测可改善胸部X线片中肺结节的检测,超出了骨骼抑制图像的支持范围

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Purpose: To evaluate the added value of computer-aided detection (CAD) for lung nodules on chest radiographs when radiologists have bone-suppressed images (BSIs) available. Materials and Methods: Written informed consent was waived by the institutional review board. Selection of study images and study setup was reviewed and approved by the institutional review boards. Three hundred posteroanterior (PA) and lateral chest radiographs (189 radiographs with negative findings and 111 radiographs with a solitary nodule) in 300 subjects were selected from image archives at four institutions. PA images were processed by using a commercially available CAD, and PA BSIs were generated. Five radiologists and three residents evaluated the radiographs with BSIs available, first, without CAD and, second, after inspection of the CAD marks. Readers marked locations suspicious for a nodule and provided a confidence score for that location to be a nodule. Location-based receiver operating characteristic analysis was performed by using jackknife alternative free-response receiver operating characteristic analysis. Area under the curve (AUC) functioned as figure of merit, and P values were computed with the Dorfman-Berbaum-Metz method. Results: Average nodule size was 16.2 mm. Stand-alone CAD reached a sensitivity of 74% at 1.0 false-positive mark per image. Without CAD, average AUC for observers was 0.812. With CAD, performance significantly improved to an AUC of 0.841 (P = .0001). CAD detected 127 of 239 nodules that were missed after evaluation of the radiographs together with BSIs pooled over all observers. Only 57 of these detections were eventually marked by the observers after review of CAD candidates. Conclusion: CAD improved radiologists' performance for the detection of lung nodules on chest radiographs, even when baseline performance was optimized by providing lateral radiographs and BSIs. Still, most of the true-positive CAD candidates are dismissed by observers.
机译:目的:当放射线医师有可用的骨抑制图像(BSI)时,评估胸片上肺结节的计算机辅助检测(CAD)的附加值。资料和方法:机构审查委员会放弃书面知情同意书。研究图像的选择和研究设置由机构审查委员会审查和批准。从四家机构的图像档案库中选出了300名受试者的300副后前(PA)和侧位胸部X光片(189幅阴性结果的X光片和111颗孤立性结节的X光片)。通过使用市售CAD处理PA图像,并生成PA BSI。五名放射科医生和三名住院医师用可用的BSI对放射线进行了评估,首先是没有CAD,其次是在检查了CAD标记之后。读者将可疑结节的位置标记为可疑结节,并提供该地点为结节的置信度得分。基于位置的接收器工作特性分析是通过使用折刀替代自由响应接收器工作特性分析来进行的。曲线下面积(AUC)用作品质因数,并使用Dorfman-Berbaum-Metz方法计算P值。结果:平均结节大小为16.2毫米。独立的CAD在每个图像1.0假阳性标记处达到74%的灵敏度。没有CAD,观察者的平均AUC为0.812。使用CAD时,性能显着提高到AUC为0.841(P = .0001)。 CAD评估了X光片并汇总了所有观察者的BSI后,发现了239个结节中的127个。在对CAD候选者进行审查后,观察者最终仅对其中的57个检测结果进行了标记。结论:即使通过提供侧位X线照片和BSI来优化基线性能,CAD仍可以提高放射科医生在胸部X光片上检测肺结节的性能。尽管如此,大多数真正正面的CAD候选人还是被观察员解雇了。

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