Intelligent Oncology continues to deliver high-impact research in lung cancer, spanning radiomics to clinical decision-making. We spotlight the Lung Cancer category with five papers:
AI for Therapy Optimization
NSCLC Immunotherapy Efficacy Prediction (Vol 1, Iss 3) — A narrative review exploring how machine learning and CT imaging enable early prediction of immunotherapy responses in NSCLC.
https://doi.org/10.1016/j.intonc.2025.05.001
AI in Clinical Trials (Vol 1, Iss 1) — An in-depth analysis of AI's transformative role in lung cancer drug discovery and trial design.
https://doi.org/10.1016/j.intonc.2024.11.003
LLMs in Clinical Decision-Making
LLMs vs. Human Physicians (Vol 2, Iss 1) — A real-world case-based study objectively evaluating the decision-making performance of large language models in challenging lung cancer cases.
https://doi.org/10.1016/j.intonc.2026.100039
Intelligent Imaging & Precision Quantification
Brain Metastasis Segmentation (Vol 2, Iss 2) — A deep learning-based nnU-Net model achieving high-precision segmentation of small-volume brain metastases in lung cancer patients.
https://doi.org/10.1016/j.intonc.2026.100049
Multimodal Imaging for Pulmonary Function (Vol 2, Iss 2) — Integrating radiomics and deep learning to predict pulmonary function, bridging the gap from morphology to function.
https://doi.org/10.1016/j.intonc.2026.100051
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