A multi-task masked autoencoder with GAN-based augmentation for PD-L1 prediction from chest CT images

Area of Science:

• Radiology
• Artificial Intelligence
• Oncology

Background:

• Immune checkpoint inhibitors targeting PD-1/PD-L1 are standard for advanced non-small cell lung cancer (NSCLC).
• Patient selection for these therapies is difficult due to tumor heterogeneity and variable PD-L1 staining.
• Accurate prediction of PD-L1 expression is crucial for effective treatment stratification.

Purpose of the Study:

• To develop a non-invasive, model-driven computer-aided diagnosis (CADx) framework.
• To predict PD-L1 expression directly from CT images in NSCLC patients.
• To address challenges posed by limited labeled data and improve treatment decision-making.

Main Methods:

• A Multi-task Masked Autoencoder (MTMAE) framework was developed using CT images from 188 NSCLC patients.
• The MTMAE incorporates self-supervised masked image modeling for data efficiency.
• Integrated segmentation and Gabor-based generative adversarial network (GAN) augmentation enhance feature learning and generalization.

Main Results:

• The proposed MTMAE model achieved an Area Under the Curve (AUC) of 0.735 and an accuracy of 0.724.
• This performance surpassed traditional supervised pretraining (AUC 0.695) and single-task Masked Autoencoder (MAE) (AUC 0.712).
• The model demonstrated effective prediction of PD-L1 status from CT images.

Conclusions:

• Combining self-supervised learning, multi-task learning, and GAN augmentation provides a robust method for PD-L1 prediction.
• This model-based approach offers a reproducible and standardized non-invasive tool for predicting PD-L1 status.
• The framework serves as a valuable complementary tool for guiding NSCLC treatment decisions.