Intricacies of human-AI interaction in dynamic decision-making for precision oncology.

AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progression for effective personalized care. However, AI's imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human-AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma.

Embracing the Future of Clinical Trials in Radiotherapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions.

This white paper examines the potential of pioneering technologies and artificial intelligence (AI)-driven solutions in advancing clinical trials involving radiotherapy. As the field of radiotherapy evolves, the integration of cutting-edge approaches such as radiopharmaceutical dosimetry, FLASH radiotherapy, image-guided radiation therapy (IGRT), and AI promises to improve treatment planning, patient care, and outcomes. Additionally, recent advancements in quantum science, linear energy transfer/relative biological effect (LET/RBE), and the combination of radiotherapy and immunotherapy create new avenues for innovation in clinical trials.

Primary Results of NRG-RTOG1106/ECOG-ACRIN 6697: A Randomized Phase II Trial of Individualized Adaptive (chemo)Radiotherapy Using Midtreatment F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Stage III Non-Small Cell Lung Cancer.

Purpose: NRG-RTOG0617 demonstrated a detrimental effect of uniform high-dose radiation in stage III non-small cell lung cancer. NRG-RTOG1106/ECOG-ACRIN6697 (ClinicalTrials.gov identifier: NCT01507428), a randomized phase II trial, studied whether midtreatment F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) can guide individualized/adaptive dose-intensified radiotherapy (RT) to improve and predict outcomes in patients with this disease.

Integrating local and distant radiation-induced lung injury: Development and validation of a predictive model for ventilation loss.

Background: Investigations on radiation-induced lung injury (RILI) have predominantly focused on local effects, primarily those associated with radiation damage to lung parenchyma. However, recent studies from our group and others have revealed that radiation-induced damage to branching serial structures such as airways and vessels may also have a substantial impact on post-radiotherapy (RT) lung function. Furthermore, recent results from multiple functional lung avoidance RT trials, although promising, have demonstrated only modest toxicity reduction, likely because they were primarily focused on dose avoidance to lung parenchyma.