Advances in Normal Tissue Complication Probability (NTCP) Modelling with Multi-Modal Data: From Dosimetry to Omics

Normal Tissue Complication Probability (NTCP) modelling remains a cornerstone of modern radiation oncology, guiding clinicians in balancing tumor control with the preservation of normal tissue function. Historically, most NTCP models have relied on clinical and dosimetric parameters; however, these models can only partly capture the complex biological and patient-specific factors underpinning radiation-induced toxicities.

In recent years, technological innovations have enabled the collection of a rich array of new data modalities. Quantitative imaging biomarkers, particularly from modalities such as MRI, PET, and CT, offer spatially resolved, tissue-specific insights into subclinical changes that precede clinically apparent toxicity. Meanwhile, advances in genomics and transcriptomics are unveiling the genetic and molecular bases of individual susceptibility to radiation effects and complications.

There is rapidly increasing interest and emerging evidence that combining multi-modal data, spanning dosimetry, imaging biomarkers, and omics, can provide a more comprehensive and personalized framework for predicting radiation-induced toxicities. Integrative approaches promise not only earlier detection but also improved stratification and risk modeling, paving the way for precision radiation therapy tailored to the unique biology and risk profiles of individual patients.

This Research Topic aims to bring together original research, reviews, and perspectives on the latest advances in NTCP modelling that leverage multi-modal data sources. We welcome submissions that address, but are not limited to:
• Development and validation of NTCP models incorporating multi-modal data (clinical, dosimetric, imaging, omics).
• Novel imaging biomarkers and radiomics signatures related to early detection, monitoring and prediction of radiation-induced toxicities.
• Integration of genomics, transcriptomics, or other molecular data into radiation-induced toxicity risk models.
• Machine learning, artificial intelligence, and statistical methods for multi-modal data integration in the context of normal tissue radiation-induced toxicities.
• Early detection and spatial mapping of radiotherapy-induced toxicities.
• Benchmarking, validation, and comparative studies of NTCP models across data modalities.
• Translational studies and anticipated clinical impact of multi-modal NTCP modelling.

By showcasing innovative strategies and new multi-modal datasets, this Topic will help advance the field towards more accurate, biologically informed, and patient-centric toxicity prediction. Researchers in radiation oncology, imaging science, medical physics, bioinformatics, and related disciplines are encouraged to contribute to this focused discussion at the intersection of dosimetry, imaging, and omics.

Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.

Topic Editor Eva Onjukka has received research funding and honoraria from Varian Medical Systems (a Siemens Healthineers company), and their institution maintains a strategic partnership with Varian. The other Topic Editors declare no competing interests with regard to the Research Topic subject.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

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:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: NTCP Modelling, Multi-Modal Data Integration, Dosimetry, Radiomics, Quantitative Imaging Biomarkers, Genomics, Transcriptomics, Radiation-Induced Toxicity, Machine Learning In Radiotherapy, Precision Radiation Oncology

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.