ReviewCancers2026
Personalized Treatment of Head and Neck Cancers: Role of Functional Imaging and AI.
Review in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.