Radiomics-driven multimodal integration in precision radiotherapy for nasopharyngeal carcinoma— current evidence, clinical barriers, and future directions

Nasopharyngeal carcinoma (NPC), a head and neck malignancy, is highly sensitive to radiation. Accordingly, radiotherapy has long been the primary curative modality for patients with non-metastatic NPC. Although intensity-modulated radiotherapy has significantly improved target conformality and normal tissue protection, local recurrence, distant metastasis, and late toxicity remain important issues in long-term management. To achieve a balance between tumor control and normal tissue protection, the continuous integration of multi-source information throughout the treatment workflow is critical. Radiomics has the potential to quantify spatial heterogeneity that may not be fully captured by conventional visual assessment. In this review, radiomics-driven multimodal integration refers to the task-specific combination of imaging-derived phenotypes with complementary clinical variables, Epstein–Barr virus DNA (EBV-DNA), hematological biomarkers, dose distributions, pathological features, molecular data, longitudinal imaging, or treatment-delivery information for defined radiotherapy decisions. This review focuses on the clinical timeline of precision radiotherapy for NPC and provides an overview of radiomics-driven multimodal integration studies. It discusses the current applications and limitations of this framework in pretreatment risk stratification and treatment selection, target volume and organ-at-risk definition during radiotherapy planning, on-treatment monitoring and adaptive radiotherapy, toxicity prediction and normal-tissue protection, and post-treatment recurrence assessment and re-irradiation decision-making. This synthesis of current evidence, clinical barriers, and methodological considerations may inform future validation and implementation of radiomics-driven multimodal integration in NPC precision radiotherapy.

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Publication Details

Journal
Frontiers in Oncology
Published
2026-11-04
DOI
https://doi.org/10.3389/fonc.2026.1902266
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Radiomics-driven multimodal integration in precision radiotherapy for nasopharyngeal carcinoma— current evidence, clinical barriers, and future directions

Nianyong Chen, Qitao Gou, Yuxin Xie, Qiheng Gou et al.
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
article

Radiomics-driven multimodal integration in precision radiotherapy for nasopharyngeal carcinoma— current evidence, clinical barriers, and future directions

Nianyong Chen, Qitao Gou, Yuxin Xie, Qiheng Gou, Yuhan Ma
article en

Abstract

Nasopharyngeal carcinoma (NPC), a head and neck malignancy, is highly sensitive to radiation. Accordingly, radiotherapy has long been the primary curative modality for patients with non-metastatic NPC. Although intensity-modulated radiotherapy has significantly improved target conformality and normal tissue protection, local recurrence, distant metastasis, and late toxicity remain important issues in long-term management. To achieve a balance between tumor control and normal tissue protection, the continuous integration of multi-source information throughout the treatment workflow is critical. Radiomics has the potential to quantify spatial heterogeneity that may not be fully captured by conventional visual assessment. In this review, radiomics-driven multimodal integration refers to the task-specific combination of imaging-derived phenotypes with complementary clinical variables, Epstein–Barr virus DNA (EBV-DNA), hematological biomarkers, dose distributions, pathological features, molecular data, longitudinal imaging, or treatment-delivery information for defined radiotherapy decisions. This review focuses on the clinical timeline of precision radiotherapy for NPC and provides an overview of radiomics-driven multimodal integration studies. It discusses the current applications and limitations of this framework in pretreatment risk stratification and treatment selection, target volume and organ-at-risk definition during radiotherapy planning, on-treatment monitoring and adaptive radiotherapy, toxicity prediction and normal-tissue protection, and post-treatment recurrence assessment and re-irradiation decision-making. This synthesis of current evidence, clinical barriers, and methodological considerations may inform future validation and implementation of radiomics-driven multimodal integration in NPC precision radiotherapy.

Frontiers in OncologyVol. 16
Sichuan University (CN), West China Hospital of Sichuan University (CN)
Openalex Percentile: Top 14%
Radiomics and Machine Learning in Medical Imaging
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