Imaging and Radiomics in Lung Cancer: AI‑Driven Clinical Trials
Advances in imaging, especially radiomics and the emergence of AI and machine learning for downstream analysis are transforming lung cancer detection and management including in clinical trials. Imaging remains essential for early, often asymptomatic, lung cancer diagnosis and for accurate staging using chest X-ray, CT, PET, and MRI. By improving nodule detection, characterization, and staging accuracy through standardized imaging and radiomic extraction, clinical trials can achieve better eligibility, stratification, and treatment planning that improve patient outcomes.
Lung cancer includes small‑cell lung cancer (SCLC, ~15%) and non‑small‑cell lung cancer (NSCLC, ~85%), which comprises adenocarcinoma, squamous‑cell, and large‑cell subtypes. Early disease is often silent and depends on imaging for detection; precise imaging‑based staging (tumor size, nodal status, metastases), and it guides therapy and trial enrollment. Industry trends emphasize low‑dose CT screening, multimodality imaging (PET‑CT, MRI), quantitative imaging biomarkers, and wider adoption of radiomics. While AI/ML promise enhanced predictive analytics, their reliable deployment requires high‑quality, standardized imaging, curated datasets, and robust validation, areas where radiomics and study design are critical.
Imaging Endpoints (IE) leads by delivering rigorous radiomics‑driven imaging requirements and assessment criteria tailored to lung cancer clinical trials. IE focuses on producing high‑quality, standardized imaging acquisition, harmonized site procedures, and validated radiomic feature extraction that form the essential foundation for downstream AI/ML analyses. IE develops research frameworks that enable AI/ML use for radiomics analysis, correlation with clinical and molecular data, and predictive modeling. This approach ensures datasets are analysis‑ready and regulatory aligned. IE guides sponsors on AI implementation, offering protocol design, data curation, and governance structures to support model development and validation. Investing in imaging consistency and metadata capture (scanner parameters, reconstruction kernels, timing) dramatically increases the utility of radiomic datasets for AI/ML work, yet may be missed in routine trial setup.
Imaging Endpoints is the global leader in designing imaging requirements for oncology clinical trial protocols, including lung cancer. With a strong track record of improving trial outcomes through the right strategy, optimized imaging charters, and robust assessment criteria, IE is also building radiomics‑ready workflows and an AI‑compatible research framework to support advanced downstream analyses and regulatory submissions.
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