Introduction
The complexity of oncology clinical trials is increasing, driven by discoveries such as targeted therapies, novel immunotherapies, and radiopharmaceutical therapies, which are leading to the adoption of adaptive trial designs with complex biomarker-driven endpoints. Traditional imaging endpoints such as Response Evaluation Criteria in Solid Tumors (RECIST) provide a standardized method for evaluation in solid-tumor research, but can fail to capture early or subtle biological responses or treatment effects. Quantitative Imaging (QI) uses advanced imaging techniques to produce precise measurements of tumor metabolism, perfusion, receptor expression, and other molecular characteristics, providing quantifiable and actionable insights to support oncological drug development decision-making. Methods to quantify imaging features assist in the clinical assessment of many patients, and help oncology drug developers to optimise trial design, stratify patients, and monitor treatment efficacy thoroughly. This article considers how quantitative imaging enhances preclinical oncology imaging and decision-making in oncology drug development, discussing key advantages and considering real-world research insights to highlight clinical utility for oncology research.
What is Quantitative Imaging?
According to Radiology Key, Quantitative Imaging can be defined as the methods used to extract quantifiable features from medical images for the assessment of disease, injury, or chronic conditions. In contrast to standard radiology reads, which are based on the subjective and visual evaluation of a radiologist, quantitative imaging provides an objective numerical measurement which can be combined with other clinical, molecular, or genetic biomarkers. Quantitative Imaging transforms imaging data into precise, measurable metrics which can be consistently reproduced, compared, and integrated into predictive and prognostic models. Examples of commonly used quantitative imaging biomarkers within the field of oncology include tumor volume, apparent diffusion coefficient, and standardized uptake value. Imaging biomarkers must be precise, accurate, and reproducible, requiring standardized imaging protocols, rigorous validation, and robust quality standards. A review of the use of imaging biomarkers in oncology clinical trials highlights the importance of employing quality assurance strategies for technical validation specific to the imaging modality in use.
Imaging modalities commonly used include:
- Positron Emission Tomography (PET)
- Magnetic Resonance Imaging (MRI)
- Computed Tomography (CT)
- Single-Photon Emission Computed Tomography (SPECT)
Why quantitative imaging data matters in oncology drug development
As oncology drug development becomes increasingly complex due to the discovery of novel targeted therapies and precision molecular approaches, it has become essential to develop robust efficacy analysis methods which go beyond traditional subjective, size-based criteria. Imaging is used throughout oncology drug development in a number of ways: to evaluate treatment burden, monitor treatment response, assess disease progression, and provide early indicators of therapeutic efficacy. Quantitative imaging gives a comprehensive overview of all of these factors, enabling the extraction of objective metrics such as tumor volume, metabolic activity, perfusion parameters, and tissue cellularity to give a much more far-reaching preclinical evaluation of a therapeutic intervention when combined with conventional assessments.
Key advantages of using quantitative imaging in oncology trials:
- Detect treatment response before visible tumor shrinkage
- Use imaging biomarkers to guide inclusion and exclusion criteria
- Real-time imaging data informs dose adjustments or cohort expansion
- Objective measurements minimise late-stage trial failure
- Quantitative imaging endpoints are increasingly useful for regulatory submission
Real-world research insights
PET Imaging is used widely in the context of preclinical oncology imaging trials. Fluorodeoxyglucose F18 (FDG)-positron emission tomography (PET), or FDG-PET, uses a radioactive tracer similar to sugar to evaluate the body for areas of high metabolic activity. This quantitative imaging modality is often used in oncology drug development to evaluate early treatment response. As cancer cells consume more glucose than normal cells, the FDG tracer accumulates in the cancer cells, making them easier to see on imaging scans. Integrating the metabolic and anatomical findings of nuclear medicine and radiology in this way supersedes subjective assessment alone. FDG-PET has been used in many influential oncology studies, which have supported intervention approvals and have demonstrated utility in the detection of early biological activity and treatment efficacy in cancers such as esophageal cancer, non-small cell lung cancer, and lymphoma.
Best practice for the integration of QI
- Integrate early, in preclinical and early-phase clinical trials
- Ensure QI standardisation across sites
- Partner with an expert preclinical imaging CRO for enhanced infrastructure and expertise
- Leverage centralised imaging platforms to reduce variability
- Collaborate early with regulators and QI imaging CROs
The future of Quantitative Imaging
Radiomics is a rapidly evolving field of Quantitative Imaging, in which medical images are analysed for a large number of features by means of advanced mathematical analysis. Results are then used to build predictive or prognostic models and combined with other clinical and molecular data to enhance the utility of existing data. This innovative concept has broadly been applied to the field of oncology, allowing the extraction of disease-specific imaging features that are imperceptible to the human eye. Radiomics can be applied across different imaging modalities, with results combined to give a comprehensive overview of available imaging data. Artificial Intelligence (AI) and machine learning are expected to have a huge impact on quantitative imaging, automating features for the extraction and analysis of data, identifying complex patterns or relationships between biomarkers, and enabling predictive modelling of patient outcomes to support real-time decision-making with multimodal imaging data.
Developers who embrace objective, quantitative imaging tools reduce risk, accelerate timelines, and improve clinical trial outcomes. Better yet, developers who partner with an expert preclinical imaging CRO, such as Perceptive Discovery, leverage specialist expertise, complex QI biomarkers, and advanced imaging infrastructure to support early go/no-go decision making in oncology drug development.
Resources
Radiology Key. Quantitative Imaging: Images to Numbers. https://radiologykey.com/27-quantitative-imaging-images-to-numbers/
Tomography. A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation. https://pmc.ncbi.nlm.nih.gov/articles/PMC10610870/
Cancer Imaging. How we read oncologic FDG PET/CT. https://cancerimagingjournal.biomedcentral.com/articles/10.1186/s40644-016-0091-3
