Abstract 948P
Background
Development of a method to predict the response of HCC to combination therapy with lenvatinib plus anti-PD-1 antibodies before treatment initiation would have great clinical benefit.
Methods
Consecutive patients with uHCC receiving first-line lenvatinib plus an anti-PD-1 antibody between Sep 2018 and Feb 2021, and who had at least one radiological response evaluation, were eligible for this study. Intrahepatic tumor response was assessed every 2 months (± 2 weeks) using modified RECIST; patients with a best intrahepatic tumor response of complete or partial response were defined as radiological responders and those with stable or progressive disease were defined as radiological non-responders. Radiomic features of intrahepatic tumors were extracted from the enhanced arterial and delayed phase of baseline MRI images. A Least Absolute Shrinkage and Selection Operator (LASSO) model was employed for feature selection. A Neural Network was used to develop the prediction model. The optimal cutoff value was determined using a receiver operating characteristic (ROC) curve by maximizing the Youden index.
Results
Of 96 eligible patients, 50% (n = 48) were radiological responders and 50% (n = 48) were non-responders. All patients were randomly divided into training (n = 72) and validation (n = 24) sets. A total of 2,420 radiomic features were extracted and normalized with min-max normalization. Features in the training set with intraclass correlation coefficients ≥ 0.80 were introduced into the LASSO model. Five features in the arterial phase and five in the delayed phase were identified as significant and used to build a neural network. The optimal cutoff value was 0.504. The area under the ROC curve, accuracy, specificity, and sensitivity for predicting objective response were 0.971 (P < 0.001), 97.2%, 97.2% and 97.2% in the training set, respectively; and were 0.778 (P = 0.010), 75.0%, 91.7% and 58.3% in the validation set, respectively.
Conclusions
Radiomics features from baseline MRI may serve as predictors for objective response to lenvatinib plus anti-PD-1 antibodies in uHCC patients before treatment initiation.
Clinical trial identification
NCT04639284.
Editorial acknowledgement
Legal entity responsible for the study
Zhongshan Hospital, Fudan University.
Funding
Clinical Research Special Fund of Zhongshan Hospital, Fudan University.
Disclosure
All authors have declared no conflicts of interest.