Noninvasive Prediction of Ki‐67 Expression in Hepatocellular Carcinoma Using Machine Learning‐Based Ultrasomics
| العنوان: | Noninvasive Prediction of Ki‐67 Expression in Hepatocellular Carcinoma Using Machine Learning‐Based Ultrasomics |
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| المؤلفون: | Linlin Zhang, Shaobo Duan, Qinghua Qi, Qian Li, Shanshan Ren, Shunhua Liu, Bing Mao, Ye Zhang, Simeng Wang, Long Yang, Ruiqing Liu, Luwen Liu, Yaqiong Li, Na Li, Lianzhong Zhang |
| المصدر: | Journal of Ultrasound in Medicine. 42:1113-1122 |
| بيانات النشر: | Wiley, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Radiological and Ultrasound Technology, Radiology, Nuclear Medicine and imaging |
| الوصف: | To investigate the ability of ultrasomics to predict Ki-67 expression in hepatocellular carcinoma (HCC).A total of 244 patients from three hospitals were retrospectively recruited (training dataset, n = 168; test dataset, n = 43; and validation dataset, n = 33). Lesion segmentation of the ultrasound images was performed manually by two radiologists. In total, 1409 ultrasomics features were extracted. Feature selection was conducted using the intra-class correlation coefficient, variance threshold, mutual information, and recursive feature elimination plus eXtreme Gradient Boosting. The support vector machine was combined with the learning curve and grid search parameter tuning to construct the clinical, ultrasomics, and combined models. The predictive performance of the models was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity and accuracy.The ultrasomics model performed well on the training, test, and validation datasets. The AUC (95% confidence interval [CI]) for these datasets were 0.955 (0.912-0.981), 0.861 (0.721-0.947), and 0.665 (0.480-0.819), respectively. The combination of ultrasomics and clinical features significantly improved model performance on all three datasets. The AUC (95% CI), sensitivity, specificity, and accuracy were 0.986 (0.955-0.998), 0.973, 0.840, and 0.869 on the training dataset; 0.871 (0.734-0.954), 0.750, 0.829, and 0.814 on the test dataset; and 0.742 (0.560-0.878), 0.714, 0.808, and 0.788 on the validation dataset, respectively.Ultrasomics was proved to be a potential noninvasive method to predict Ki-67 expression in HCC. |
| تدمد: | 1550-9613 0278-4297 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15b1ada0ed3ff3c9499b6a59273d5888 https://doi.org/10.1002/jum.16126 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....15b1ada0ed3ff3c9499b6a59273d5888 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15509613 02784297 |
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