CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners

Purpose To test the technical reproducibility of acquisition and scanners of CT image-based radiomics model for early recurrent hepatocellular carcinoma (HCC). Methods We included primary HCC patient undergone curative therapies, using early recurrence as endpoint. Four datasets were constructed: 10...

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Bibliographic Details
Published in:Radiologia medica 2020-08, Vol.125 (8), p.697-705
Main Authors: Hu, Hang-tong, Shan, Quan-yuan, Chen, Shu-ling, Li, Bin, Feng, Shi-ting, Xu, Er-jiao, Li, Xin, Long, Jian-yan, Xie, Xiao-yan, Lu, Ming-de, Kuang, Ming, Shen, Jing-xian, Wang, Wei
Format: Article
Language:English
Subjects:
Abdominal Radiology
Calibration
Computed tomography
Datasets
Deviation
Diagnostic Radiology
Heterogeneity
Image acquisition
Imaging
Interventional Radiology
Liver cancer
Medicine
Medicine & Public Health
Neuroradiology
Radiology
Radiomics
Reproducibility
Scanners
Thickness
Training
Ultrasound
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Summary:Purpose To test the technical reproducibility of acquisition and scanners of CT image-based radiomics model for early recurrent hepatocellular carcinoma (HCC). Methods 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. Results 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 
ISSN:0033-8362
1826-6983
DOI:10.1007/s11547-020-01174-2