Abstract 43P
Background
Lenvatinib with PD-1 inhibitors showed promising efficacy in treating unresectable hepatocellular carcinoma (uHCC) but predicting treatment response (TR) remains challenging due to tumor heterogeneity. We aimed to develop models based on clinical and radiomics features using machine learning (ML) algorithms to predict the efficacy of lenvatinib (LEN) combined with tislelizumab (TIS) based therapy in uHCC patients (pts).
Methods
110 uHCC pts treated with LEN combined with TIS based therapy between 2019 and 2024 were recruited. Among them, 30 pts received LEN plus TIS, while 80 received the combination of LEN, TIS, and TACE. Clinical data and contrast-enhanced CT images were collected. TR was assessed by mRECIST. Five machine learning algorithms, including logistic regression, random forest (RF), neural networks, support vector machines and naive Bayes, were applied to develop prediction models based on clinical and radiomic features. The predictive performance of these models was evaluated using ROC curves, accuracy (ACC), calibration curves (CC), and decision curve analysis (DCA).
Results
Of 110 eligible pts, ORR was 33.6% with all partial response, and DCR was 75.9%. The cohort was randomly divided into a training set of 88 pts and a validation set of 22 pts. A total of 240 radiomic features were extracted, and the top 15 significant features were selected using the mRMR algorithm for further analysis. Prediction models based on 20 clinical variables and 15 radiomic features were developed using five different machine learning algorithms. The RF model showed the best predictive performance, with average area under the curve (AUC) of 0.99 and 0.72, and ACC values of 0.99 and 0.66 for the training and validation sets, respectively. CC and DCA further demonstrated the robust performance of the RF model in predicting treatment response.
Conclusions
Machine learning models based on clinical and radiomics features were successfully constructed to predict the efficacy of LEN and TIS based therapy in uHCC pts. These models offer potential guidance for personalized treatment strategies.
Clinical trial identification
NCT05543304.
Legal entity responsible for the study
The First Affiliated Hospital of Wenzhou Medical University.
Funding
Zhejiang Provincial Health Commission (WKJ-ZJ-2438).
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
185P - Characterizing the metabolic reprogramming of mutant ß-catenin in hepatocellular carcinoma
Presenter: Nathalie Wong
Session: Poster Display session
Resources:
Abstract
186P - Unveil and overcome PD(L)1 antibody resistance via functional genomics and clinical derived biopsies
Presenter: BIN XIE
Session: Poster Display session
Resources:
Abstract
187P - Single-cell RNA sequencing elucidates the role of intercommunication between epithelial cells, immune cells, and fibroblasts in high-grade serous ovarian cancer
Presenter: Xiaoting Zhao
Session: Poster Display session
Resources:
Abstract
188P - Single-cell and spatial transcriptomics delineate the microstructure and immune landscape of intrahepatic cholangiocarcinoma in the leading-edge area
Presenter: Zuyin li
Session: Poster Display session
Resources:
Abstract
189P - Image-guided labeling of live cells in tertiary lymphoid structures for single-cell transcriptomics
Presenter: Mengrui Cao
Session: Poster Display session
Resources:
Abstract
190P - Ex vivo modeling of precision immuno-oncology responses in lung cancer
Presenter: Bassel Alsaed
Session: Poster Display session
Resources:
Abstract
191P - Patient-derived 3D-bioprinted tumours as a relevant and innovative tool for immunotherapeutic testing.
Presenter: Kamila Pawlicka
Session: Poster Display session
Resources:
Abstract
192P - A new neuroendocrine model and in vitro effect of melatonin and serotonin on the PD-L1 ligand and its immunomodulatory action
Presenter: Dulce Caraveo Gutiérrez
Session: Poster Display session
Resources:
Abstract
193P - Humanized mice applying CD47; Prkcd; IL2rg triple KO mice exhibit enhanced human immune cell engraftment and reduced GvHD symptoms
Presenter: Seung-Ho Heo
Session: Poster Display session
Resources:
Abstract
194P - The impact of BTK inhibitors on ?d T cell fitness: Lesson for immunotherapy
Presenter: Adam Michalski
Session: Poster Display session
Resources:
Abstract