Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA.
Department of Radiology, Stanford School of Medicine, Stanford, CA, 94305, USA.
Med Phys. 2017 Jul;44(7):3483-3490. doi: 10.1002/mp.12277. Epub 2017 May 23.
To explore the characteristics that impact lung nodule detection by peripheral vision when searching for lung nodules on chest CT-scans.
This study was approved by the local IRB and is HIPAA compliant. A simulated primary (1°) target mass (2 × 2 × 5 cm) was embedded into 5 cm thick subvolumes (SV) extracted from three unenhanced lung MDCT scans (64 row, 1.25 mm thickness, 0.7 mm increment). One of 30 solid, secondary nodules with either 3-4 mm and 5-8 mm diameters were embedded into 192 of 207 SVs. The secondary nodule was placed at a random depth within each SV, a transverse distance of 2.5, 5, 7.5, or 10 mm, and along one of eight rays cast every 45° from the center of the 1° mass. Video recordings of transverse paging in cranio-caudal direction were created for each SV (frame rate three sections/sec). Six radiologists observed each cine-loop once while gaze-tracking hardware assured that gaze was centered on the 1° mass. Each radiologist assigned a confidence rating (0-5) to the detection of a secondary nodule and indicated its location. Detection sensitivity was analyzed relative to secondary nodule size, transverse distance, radial orientation, and lung complexity. Lung complexity was characterized by the number of particles (connected pixels) and the sum of the area of all particles above a -500 HU threshold within regions of interest around the 1° mass and secondary nodule.
Using a proportional odds logistic regression model and eliminating redundant predictors, models fit individually to each reader resulted in the following decreasing order of association based on greatest reduction in Akaike Information Criterion: secondary nodule diameter (6/6 readers, P < 0.001), distance from central mass (6/6 readers, P < 0.001), lung complexity particle count (5/6 readers, P = 0.05), and lung complexity particle area (3/6 readers, P = 0.03). Substantial inter-reader differences in sensitivity to decreasing nodule diameter, distance, and complexity characteristics were observed.
Of the investigated parameters, secondary nodule size, distance from the gaze center and lung complexity (particle number and area) significantly impact nodule detection with peripheral vision.
探索在胸部 CT 扫描中通过周边视觉寻找肺结节时影响肺结节检测的特征。
本研究得到了当地 IRB 的批准，并符合 HIPAA 规定。一个模拟的原发性（1°）靶质量（2×2×5cm）被嵌入到三个未增强的肺 MDCT 扫描的 5cm 厚子容积（SV）中（64 排，1.25mm 厚度，0.7mm 增量）。30 个实性次级结节中的一个，直径为 3-4mm 和 5-8mm，被嵌入到 207 个 SV 中的 192 个中。次级结节随机放置在每个 SV 内的任意深度，横向距离为 2.5、5、7.5 或 10mm，并沿着从 1°质量中心引出的八个射线中的一个。为每个 SV 创建了一个沿头尾方向的横向分页的视频记录（帧率为每三部分/秒）。每位放射科医生观察每个电影循环一次，同时注视跟踪硬件确保注视集中在 1°质量上。每位放射科医生都对检测到的次级结节的置信度进行了（0-5）评级，并指出了其位置。根据次级结节的大小、横向距离、放射方向和肺复杂性，分析了检测灵敏度。肺复杂性通过感兴趣区域内的粒子数量（连接像素）和所有粒子的面积总和来描述，这些粒子的面积总和高于 1°质量和次级结节周围的-500HU 阈值。
使用比例优势逻辑回归模型并消除冗余预测因子，单独拟合到每个读者的模型导致以下关联的降序排列，基于最大减少 Akaike 信息准则：次级结节直径（6/6 名读者，P<0.001）、距中心质量的距离（6/6 名读者，P<0.001）、肺复杂性粒子计数（5/6 名读者，P=0.05）和肺复杂性粒子面积（3/6 名读者，P=0.03）。观察到读者之间对逐渐减小的结节直径、距离和复杂性特征的敏感性存在显著差异。
在所研究的参数中，次级结节的大小、与注视中心的距离和肺复杂性（粒子数量和面积）显著影响周边视觉下的结节检测。
