Effect of CT image acquisition parameters on diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes.
Effect of CT image acquisition parameters on diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes.
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DOI：
10.1007/s00330-021-08274-1
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发表时间：
2022-03
影响因子：
5.9
通讯作者：
中科院分区：
文献类型：
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探讨CT图像采集参数对放射组学在肺结节大小分类中良恶性表现的影响。我们回顾性收集2015年3月至2018年3月696例PNs患者的CT图像。按结节直径分组：T1a（直径≤1.0 cm）、T1b （1.0 cm <直径≤2.0 cm）、T1c （2.0 cm <直径≤3.0 cm）。根据切片厚度-卷积-核将CT图像分为四种设置：设置1（切片厚度/重建类型：1.25 mm尖锐），设置2 （5mm尖锐），设置3 （5mm平滑）和随机设置。我们在两种相互作用的条件下创建了12组。对每个PN进行分割，提取1160个放射组学特征。选择训练中预测能力强的非冗余特征，在12个子集下分别建立不同的模型。预测PN恶性肿瘤的auc值分别为：T1a组：0.84、0.64、0.68、0.68；T1b组：0.68、0.74、0.76、0.70；T1c组：设置1、设置2、设置3、随机设置分别为0.66、0.64、0.63、0.70。T1a组放射组学模型设置1的AUC显著高于其他组；T1b组放射组学模型设置3的auc显著高于部分；在T1c组，各模型间无统计学差异。对于小于1 cm的PNs， CT图像采集参数对放射组学预测恶性肿瘤的诊断性能有显著影响，使用薄切片重建图像和锐核算法建立的模型获得了最佳性能。对于大于1cm的PNs， CT重建参数对诊断效果影响不大。
To investigate the effect of CT image acquisition parameters on the performance of radiomics in classifying benign and malignant pulmonary nodules (PNs) with respect to nodule size. We retrospectively collected CT images of 696 patients with PNs from March 2015 to March 2018. PNs were grouped by nodule diameter: T1a (diameter ≤ 1.0 cm), T1b (1.0 cm < diameter ≤ 2.0 cm), and T1c (2.0 cm < diameter ≤ 3.0 cm). CT images were divided into four settings according to slice-thickness-convolution-kernels: setting 1 (slice thickness/reconstruction type: 1.25 mm sharp), setting 2 (5 mm sharp), setting 3 (5 mm smooth), and random setting. We created twelve groups from two interacting conditions. Each PN was segmented and had 1160 radiomics features extracted. Non-redundant features with high predictive ability in training were selected to build a distinct model under each of the twelve subsets. The performance (AUCs) on predicting PN malignancy were as follows: T1a group: 0.84, 0.64, 0.68, and 0.68; T1b group: 0.68, 0.74, 0.76, and 0.70; T1c group: 0.66, 0.64, 0.63, and 0.70, for the setting 1, setting 2, setting 3, and random setting, respectively. In the T1a group, the AUC of radiomics model in setting 1 was statistically significantly higher than all others; In the T1b group, AUCs of radiomics models in setting 3 were statistically significantly higher than some; and in the T1c group, there were no statistically significant differences among models. For PNs less than 1 cm, CT image acquisition parameters have a significant influence on diagnostic performance of radiomics in predicting malignancy, and a model created using images reconstructed with thin section and a sharp kernel algorithm achieved the best performance. For PNs larger than 1 cm, CT reconstruction parameters did not affect diagnostic performance substantially.
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DOI：
10.1016/j.radonc.2018.06.025
发表时间：
2018-11
期刊：
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
影响因子：
--
作者：
Li H;Galperin-Aizenberg M;Pryma D;Simone CB 2nd;Fan Y
通讯作者：
Fan Y
影响因子：
19.7
作者：
Berenguer, Roberto;del Rosario Pastor-Juan, Maria;Sabater, Sebastia
通讯作者：
Sabater, Sebastia
影响因子：
5.5
作者：
Barrett, JF;Keat, N
通讯作者：
Keat, N
影响因子：
3.7
作者：
Chen CH;Chang CK;Tu CY;Liao WC;Wu BR;Chou KT;Chiou YR;Yang SN;Zhang G;Huang TC
通讯作者：
Huang TC
DOI：
10.1016/j.jtho.2016.07.002
发表时间：
2016-12
期刊：
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
影响因子：
--
作者：
Hawkins S;Wang H;Liu Y;Garcia A;Stringfield O;Krewer H;Li Q;Cherezov D;Gatenby RA;Balagurunathan Y;Goldgof D;Schabath MB;Hall L;Gillies RJ
通讯作者：
Gillies RJ