Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China.
Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Br J Radiol. 2021 Feb 1;94(1118):20200089. doi: 10.1259/bjr.20200089. Epub 2020 Dec 22.
To investigate the effect of reducing pixel size on the consistency of radiomic features and the diagnostic performance of the downstream radiomic signatures for the invasiveness for pulmonary ground-glass nodules (GGNs) on CTs.
We retrospectively collected the clinical data of 182 patients with GGNs on high resolution CT (HRCT). The CT images of different pixel sizes (0.8mm, 0.4mm, 0.18 mm) were obtained by reconstructing the single HRCT scan using three combinations of field of view and matrix size. For each pixel size setting, radiomic features were extracted for all GGNs and radiomic signatures for the invasiveness of GGNs were built through two modeling pipelines for comparison.
The study finally extracted 788 radiomic features. 87% radiomic features demonstrated inter pixel size variation. By either modeling pipeline, the radiomic signature under small pixel size performed significantly better than those under middle or large pixel sizes in predicting the invasiveness of GGNs ('s value <0.05 by Delong test). With the independent modeling pipeline, the three pixel size bounded radiomic signatures shared almost no common features.
Reducing pixel size could cause inconsistency in most radiomic features and improve the diagnostic performance of the downstream radiomic signatures. Particularly, super HRCTs with small pixel size resulted in more accurate radiomic signatures for the invasiveness of GGNs.
The dependence of radiomic features on pixel size will affect the performance of the downstream radiomic signatures. The future radiomic studies should consider this effect of pixel size.
探究降低像素尺寸对 CT 上肺磨玻璃结节(GGN)侵袭性的放射组学特征一致性和下游放射组学特征诊断性能的影响。
我们回顾性收集了 182 例高分辨率 CT(HRCT)上 GGN 的临床资料。通过三种视野和矩阵大小的组合,对单次 HRCT 扫描进行重建,获得不同像素尺寸(0.8mm、0.4mm、0.18mm)的 CT 图像。对于每个像素尺寸设置,从所有 GGN 中提取放射组学特征,并通过两种建模管道构建 GGN 侵袭性的放射组学特征进行比较。
本研究最终提取了 788 个放射组学特征。87%的放射组学特征表现出像素尺寸间的差异。通过两种建模管道,小像素尺寸下的放射组学特征在预测 GGN 的侵袭性方面均显著优于中或大像素尺寸下的特征(DeLong 检验's 值<0.05)。通过独立建模管道,三种像素尺寸限定的放射组学特征几乎没有共享的特征。
降低像素尺寸会导致大多数放射组学特征不一致,并提高下游放射组学特征的诊断性能。特别是,小像素尺寸的超高分辨率 CT 可获得更准确的 GGN 侵袭性放射组学特征。
放射组学特征对像素尺寸的依赖性会影响下游放射组学特征的性能。未来的放射组学研究应考虑像素尺寸的这种影响。