Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China.
Clinical trials Unit, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Radiol Med. 2020 Aug;125(8):697-705. doi: 10.1007/s11547-020-01174-2. Epub 2020 Mar 21.
To test the technical reproducibility of acquisition and scanners of CT image-based radiomics model for early recurrent hepatocellular carcinoma (HCC).
We included primary HCC patient undergone curative therapies, using early recurrence as endpoint. Four datasets were constructed: 109 images from hospital #1 for training (set 1: 1-mm image slice thickness), 47 images from hospital #1 for internal validation (sets 2 and 3: 1-mm and 10-mm image slice thicknesses, respectively), and 47 images from hospital #2 for external validation (set 4: vastly different from training dataset). A radiomics model was constructed. Radiomics technical reproducibility was measured by overfitting and calibration deviation in external validation dataset. The influence of slice thickness on reproducibility was evaluated in two internal validation datasets.
Compared with set 1, the model in set 2 indicated favorable prediction efficiency (the area under the curve 0.79 vs. 0.80, P = 0.47) and good calibration (unreliability statistic U: P = 0.33). However, in set 4, significant overfitting (0.63 vs. 0.80, P < 0.01) and calibration deviation (U: P < 0.01) were observed. Similar poor performance was also observed in set 3 (0.56 vs. 0.80, P = 0.02; U: P < 0.01).
CT-based radiomics has poor reproducibility between centers. Image heterogeneity, such as slice thickness, can be a significant influencing factor.
测试基于 CT 图像的放射组学模型采集和扫描仪在早期复发性肝细胞癌（HCC）中的技术可重复性。
我们纳入了接受根治性治疗的原发性 HCC 患者，以早期复发为终点。构建了四个数据集：来自医院 #1 的 109 幅图像用于训练（数据集 1：1mm 图像层厚），来自医院 #1 的 47 幅图像用于内部验证（数据集 2 和 3：分别为 1mm 和 10mm 图像层厚），来自医院 #2 的 47 幅图像用于外部验证（数据集 4：与训练数据集差异很大）。构建了放射组学模型。通过外部验证数据集的过拟合和校准偏差来测量放射组学技术的可重复性。在两个内部验证数据集中评估了层厚对可重复性的影响。
与数据集 1 相比，数据集 2 中的模型显示出良好的预测效率（曲线下面积 0.79 对 0.80，P=0.47）和良好的校准（不可靠性统计量 U：P=0.33）。然而，在数据集 4 中，观察到明显的过拟合（0.63 对 0.80，P<0.01）和校准偏差（U：P<0.01）。数据集 3 也观察到类似的不良性能（0.56 对 0.80，P=0.02；U：P<0.01）。
基于 CT 的放射组学在中心之间具有较差的可重复性。图像异质性，如层厚，可能是一个重要的影响因素。
