Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Duke University 2424 Erwin Rd, Suite 302, Durham, NC 27705.
Clinical Imaging Physics Group, Carl E. Ravin Advanced Imaging, Laboratories Medical Physics Graduate Program, Durham, North Carolina.
Acad Radiol. 2021 Nov;28(11):1570-1581. doi: 10.1016/j.acra.2020.07.029. Epub 2020 Aug 20.
The 3-fold purpose of this study was to (1) develop a method to relate measured differences in radiomics features in different computed tomography (CT) scans to one another and to true feature differences; (2) quantify minimum detectable change in radiomics features based on measured radiomics features from pairs of synthesized CT images acquired under variable CT scan settings, and (3) ascertain and inform the recommendations of the Quantitative Imaging Biomarkers Alliance (QIBA) for nodule volumetry.
Images of anthropomorphic lung nodule models were simulated using resolution and noise properties for 297 unique imaging conditions. Nineteen morphology features were calculated from both the segmentation masks derived from the imaged nodules and from ground truth nodules. Analysis was performed to calculate minimum detectable difference of radiomics features as a function of imaging protocols in comparison to QIBA guidelines.
The minimum detectable differences ranged from 1% to 175% depending on the specific feature and set of imaging protocols. The results showed that QIBA protocol recommendations result in improved minimum detectable difference as compared to the range of possible protocols. The results showed that the minimum detectable differences may be improved from QIBA's current recommendation by further restricting the slice thickness requirement to be between 0.5 mm and 1 mm.
Minimum detectable differences of radiomics features were quantified for lung nodules across a wide range of possible protocols. The results can be used prospectively to inform decision-making about imaging protocols to provide superior quantification of radiomics features.
本研究有三重目的：（1）开发一种方法，将不同 CT 扫描中测量的放射组学特征差异与真实特征差异联系起来；（2）基于在不同 CT 扫描设置下获取的合成 CT 图像对测量的放射组学特征，量化放射组学特征的最小可检测变化；（3）确定并告知定量成像生物标志物联盟（QIBA）对结节体积测量的建议。
使用 297 种独特成像条件的分辨率和噪声特性模拟人体肺部结节模型的图像。从成像结节和真实结节导出的分割掩模中计算了 19 种形态特征。进行了分析，以根据成像方案计算放射组学特征的最小可检测差异，并与 QIBA 指南进行比较。
最小可检测差异范围从特定特征和成像方案的 1%到 175%不等。结果表明，与可能的方案范围相比，QIBA 方案建议可改善最小可检测差异。结果表明，通过进一步将切片厚度要求限制在 0.5 毫米至 1 毫米之间，最小可检测差异可以从 QIBA 的当前建议中得到改善。
对肺结节的放射组学特征进行了最小可检测差异的量化，涵盖了广泛的可能方案。结果可用于前瞻性地告知成像方案的决策，以提供放射组学特征的优越量化。
