Cancer imaging is moving from a purely diagnostic tool to a source of measurable, reproducible data that can guide treatment decisions and trial outcomes. Artificial intelligence now allows researchers to extract quantitative information directly from medical images, turning scans into structured, analyzable data rather than static pictures for visual review.
Automated image quantitation, including tumor segmentation, radiomic feature extraction, and functional imaging metrics from PET, DCE-MRI, and DWI, offers a non-invasive way to measure tumor burden, track treatment response, and predict outcomes over time. These tools have the potential to replace or supplement manual, observer-dependent assessments like RECIST with faster, more consistent, and more sensitive measurements, and to serve directly as endpoints in clinical trials.
Yet moving automated quantitation from research settings into clinical response assessment and trial use remains difficult. Variability across scanners and institutions, a lack of standardized acquisition and analysis protocols, limited prospective validation, and unclear regulatory pathways all slow adoption. This Research Topic explores how AI-driven quantitation of medical images can be developed, validated, and applied as a reliable measure of cancer response and a trial endpoint.
Our goal is to show how automated image quantitation can move from algorithm development to accepted use as a response assessment tool and a clinical trial endpoint. We aim to address the challenges that stand in the way of this transition, including reproducibility across sites and scanners, interpretability of automated measures, and the evidence needed for regulatory qualification.
By bringing together medical physicists, radiologists, oncologists, and AI researchers, we connect computational innovation with the practical needs of response assessment in the clinic and in trials. We focus on how automated quantitation of tumor size, shape, texture, and function can improve response evaluation, patient stratification, and therapeutic decision-making.
This Research Topic welcomes submissions on the development, validation, and clinical or trial application of automated image quantitation in oncology. We accept experimental, computational, and translational studies across all cancer imaging modalities.
We welcome Original Research articles, Reviews, and Methodological papers. Topics of interest include:
o Automated tumor segmentation and volumetric quantitation
o Radiomic and texture-based feature extraction for treatment response
o Quantitative functional and molecular imaging (PET, DCE-MRI, DWI) for response monitoring
o Automated alternatives or complements to RECIST and other response criteria
o Use of automated image quantitation as clinical trial endpoints or stratification tools
o Reproducibility, standardization, and multi-site/multi-scanner generalizability of quantitation methods
o Regulatory and qualification pathways for automated imaging endpoints
Through these papers, we aim to build a resource that helps move automated image quantitation from algorithm to accepted clinical and trial practice.
Please note that manuscripts consisting solely of bioinformatics or computational analysis of public omics databases that are not supplemented by relevant functional validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this Research Topic.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Case Report
Clinical Trial
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
