Personalized Treatment of Head and Neck Cancers: Role of Functional Imaging and AI

Chemoradiotherapy plays an important role in the management of locally advanced head and neck squamous cell carcinoma. Unfortunately, a substantial fraction of patients experience treatment failure, while others suffer from significant treatment-related toxicity caused by intensive chemoradiotherapy regimens. This underscores the need for new biomarkers that can accurately capture the biological tumor heterogeneity and guide personalized therapy. Functional imaging combined with AI-based approaches such as radiomics and deep learning may offer a promising strategy for treatment stratification. However, a substantial number of challenges remain before clinical implementation can be achieved. Therefore, this review proposes a biology-driven framework for AI analysis of functional imaging in head and neck squamous cell carcinoma. In addition, it emphasizes the need for clinically oriented validation strategies to facilitate the translation of stratification models into clinical management.

Authors

Institutions

Publication Details

Journal
Cancers
Published
2026-06-16
DOI
https://doi.org/10.3390/cancers18121954
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Personalized Treatment of Head and Neck Cancers: Role of Functional Imaging and AI

Rüveyda Dok, Sandra Nuyts, Joran Tanghe
Cancers
Radiomics and Machine Learning in Medical Imaging
article

Personalized Treatment of Head and Neck Cancers: Role of Functional Imaging and AI

Rüveyda Dok, Sandra Nuyts, Joran Tanghe
article en

Abstract

Chemoradiotherapy plays an important role in the management of locally advanced head and neck squamous cell carcinoma. Unfortunately, a substantial fraction of patients experience treatment failure, while others suffer from significant treatment-related toxicity caused by intensive chemoradiotherapy regimens. This underscores the need for new biomarkers that can accurately capture the biological tumor heterogeneity and guide personalized therapy. Functional imaging combined with AI-based approaches such as radiomics and deep learning may offer a promising strategy for treatment stratification. However, a substantial number of challenges remain before clinical implementation can be achieved. Therefore, this review proposes a biology-driven framework for AI analysis of functional imaging in head and neck squamous cell carcinoma. In addition, it emphasizes the need for clinically oriented validation strategies to facilitate the translation of stratification models into clinical management.

CancersVol. 18(12)
KU Leuven (BE)
Fonds Wetenschappelijk Onderzoek, Kom op tegen Kanker, Vlaamse regering
Good health and well-being
Openalex Percentile: Top 7%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.