ReviewCurrent oncology reports2025
Beyond Standard Radiotherapy: an AI-Driven Framework for Personalized Radiotherapy with a Four-Step Classification in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients.
Review in Current oncology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
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
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewRadiotherapy (RT) is a mainstay treatment strategy for patients with radically treatable head and neck cancer (HNC). Efforts to personalize RT have spanned decades, yielding variable results across different treatment stages. The purpose of this review was to assess the potential of AI-driven models to bridge personalized radiotherapy strategies. RECENT
findingsRadiomics, an emerging imaging analytics approach, provides significant quantitative features that can predict survival outcomes, treatment responses, and radiation-induced toxicity. Radiomics-based models in the studies demonstrate promising predictive efficacy with a high C-index or area under the curve (AUC) exceeding 0.70. AI-driven multimodal and multitemporal imaging models can stratify patients and guide risk-adapted radiotherapy. A four-step AI-driven RT framework may provide a template for future randomized controlled trials, supporting more trustworthy evidence.
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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.