Deep learning-based aggressive progression prediction from CT images of hepatocellular carcinoma
| Autor: | Sirui Fu, Xiaoqun Li, Hui Zhang, Jie Zhang, Wei Mu, Ligong Lu, Zhenchao Tang, Jie Tian, Meiqing Pan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Oncology
medicine.medical_specialty Training set business.industry Deep learning medicine.medical_treatment Disease progression Aggressive disease medicine.disease Internal medicine Hepatocellular carcinoma Extrahepatic metastasis Medicine Artificial intelligence Correlation test Hepatectomy business |
| Zdroj: | Medical Imaging 2021: Computer-Aided Diagnosis. |
| DOI: | 10.1117/12.2581057 |
| Popis: | Repeat liver resection or transarterial chemoembolization (TACE) can be used for disease progression (PD) of hepatocellular carcinoma (HCC), but when patients developed extrahepatic metastasis or macrovascular invasion which was aggressive disease progression (aggressive-PD), the treatments became a challenge. Therefore, it was meaningful to predict aggressive-PD as early as possible considering the current prediction method in clinical was unreliable. In this study, a deep learning model was conducted to predict aggressive-PD. 333 patients receiving hepatectomy or TACE were enrolled from five hospitals. For each patient, deep learning score was calculated from a convolutional neural network model constructed based on resnet block. The model showed excellent performance for individualized, non-invasive prediction of the progression of Hepatocellular carcinoma (training set: ACC=75.61%, AUC=0.81, validation set: ACC=87.36%, AUC=0.82). Pearson correlation analysis showed albumin concentration were significantly correlated with deep learning score. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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