PICTURE
AI-driven prediction of pathological complete response after neoadjuvant therapies in non-small cell lung cancer through multimodal data fusion.
Clinical Application
PICTURE
AI-driven prediction of pathological complete response after neoadjuvant therapies in non-small cell lung cancer through multimodal data fusion.
Overview
PICTURE addresses one of the central questions in contemporary lung oncology: whether pathological complete response after neoadjuvant treatment can be predicted before surgical resection. The project assumes that a multimodal view of the patient offers a more faithful representation of treatment response than any single data source taken in isolation.
The project develops AI systems that integrate radiology imaging, histology, cytology, molecular data, and electronic health records to support pre-surgical decision-making in NSCLC. Alongside predictive performance, PICTURE emphasizes robustness, explainability, and the possibility of transferring learned models toward chemoimmunotherapy scenarios.
Research Directions
- Fusion of radiology, histology, cytology, molecular data, and electronic health records.
- Prediction of pathological complete response before surgery in NSCLC.
- Robust and explainable multimodal deep learning pipelines, including transfer toward chemoimmunotherapy settings.
Related Publications
- A Systematic Benchmark of GAN Architectures for MRI-to-CT SynthesisarXiv preprint arXiv:2603.13520, 2026
- SPARSE data, rich results: Few-shot semi-supervised learning via class-conditioned image translation2026oncology generative AI clinical prediction
- Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLCarXiv preprint arXiv:2603.15100, 2026
- Pathologic Complete Response Prediction with Machine Learning Using Hierarchical Attention Feature Extraction2025radiomics oncology clinical prediction
- MARIA: A multimodal transformer model for incomplete healthcare data2025multimodal learning foundation models clinical prediction
- Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer2025radiomics oncology multimodal learning
- Doctor-in-the-Loop: An explainable, multi-view deep learning framework for predicting pathological response in non-small cell lung cancer2025explainability oncology medical imaging
- Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging2025foundation models medical imaging
- Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET2025radiomics oncology multimodal learning
- Enhancing NSCLC Histological Subtype Classification: A Federated Learning Approach Using Triplet Loss2025radiomics oncology medical imaging
- Toward a Multimodal Deep Learning Approach for Histological Subtype Classification in NSCLC2024oncology multimodal learning medical imaging