Archive | 2021

CT Radiomics Nomogram for the Preoperative Prediction of Severe Post-Hepatectomy Liver Failure in Patients with Huge (≥10 cm) Hepatocellular Carcinoma

 
 
 
 
 

Abstract


\n Background To establish a radiomics-based nomogram for predicting severe (grade B or C) post-hepatectomy liver failure (PHLF) in patients with huge (≥10 cm) hepatocellular carcinoma (HCC).Methods 186 patients with huge HCC (n = 131 for training dataset and n = 55 for test dataset) who underwent curative hepatic resection were included. The least absolute shrinkage and selection operator approach was applied to develop the radiomics signature for grade B or C PHLF prediction in the training dataset. A multivariable logistic regression model was used by incorporating radiomics signature and other clinical predictors to establish a radiomics nomogram. A decision tree was created to stratify the risk for severe PHLF.Results The radiomics signature consisting of nine features predicted severe PHLF with an AUC of 0.766 and 0.745 in the training and test datasets, respectively. The radiomics nomogram was generated by integrating the radiomics signature, the extent of resection and model for end-stage liver disease (MELD) score. The nomogram exhibited satisfactory discrimination and calibration, with an AUC of 0.842 and 0.863 in the training and test datasets, respectively. Decision tree split patients into 3 risk classes: low-risk patients with radiomics score < -0.247 and MELD score < 10 or,radiomics score ≥ -0.247 and underwent partial resections; intermediate-risk patients with radiomics score < -0.247 but MELD score ≥10; high-risk patients with radiomics score ≥ -0.247 and underwent extended resections.Conclusions The radiomics nomogram was able to predict severe PHLF in huge HCC patients. Decision tree may be useful in surgical decision-making for huge HCC hepatectomy.

Volume None
Pages None
DOI 10.21203/rs.3.rs-732725/v1
Language English
Journal None

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