Evidence map›Paper›PMID 42258140›Full record

ReviewClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2026

Radiomic and dosiomic machine learning models for predicting radiation-induced oral mucositis in head and neck cancer: a systematic review.

Lukas Mendes de Abreu, Raquel Molina Sanches, Alana Crescencio de Oliveira, Michele Di Benedetto, Izabel Regina Fischer Rubira-Bullen, Kellen Cristine Tjioe, Cássia Maria Fischer Rubira

Abstract readReview
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In one paragraph

Review in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Lukas Mendes de AbreuDepartment of Surgery, Stomatology, Pathology, and Radiology, Bauru School of Dentistry, University of São Paulo, Alameda Octávio Pinheiro Brisolla, 9-75, Bauru, SP, 17012-901, Brazil.ORCID http://orcid.org/0000-0003-2791-3603
Raquel Molina SanchesDepartment of Surgery, Stomatology, Pathology, and Radiology, Bauru School of Dentistry, University of São Paulo, Alameda Octávio Pinheiro Brisolla, 9-75, Bauru, SP, 17012-901, Brazil.ORCID http://orcid.org/0000-0002-9560-9526
Alana Crescencio de OliveiraDepartment of Surgery, Stomatology, Pathology, and Radiology, Bauru School of Dentistry, University of São Paulo, Alameda Octávio Pinheiro Brisolla, 9-75, Bauru, SP, 17012-901, Brazil.ORCID http://orcid.org/0009-0005-8145-5916
Michele Di BenedettoDepartment of Surgery, Stomatology, Pathology, and Radiology, Bauru School of Dentistry, University of São Paulo, Alameda Octávio Pinheiro Brisolla, 9-75, Bauru, SP, 17012-901, Brazil.ORCID http://orcid.org/0000-0003-1532-0245
Izabel Regina Fischer Rubira-BullenDepartment of Surgery, Stomatology, Pathology, and Radiology, Bauru School of Dentistry, University of São Paulo, Alameda Octávio Pinheiro Brisolla, 9-75, Bauru, SP, 17012-901, Brazil. izrubira@fob.usp.br.ORCID http://orcid.org/0000-0002-5069-9433
Kellen Cristine TjioeGeorgia Cancer Center, Medical College of Georgia, Augusta University, Augusta, GA, USA.ORCID http://orcid.org/0000-0003-4145-0684
Cássia Maria Fischer RubiraDepartment of Surgery, Stomatology, Pathology, and Radiology, Bauru School of Dentistry, University of São Paulo, Alameda Octávio Pinheiro Brisolla, 9-75, Bauru, SP, 17012-901, Brazil.ORCID http://orcid.org/0000-0003-2119-1144

Funding

Coordination for the Improvement of Higher Education Personnel - Brazil 001
6 · The paper itself

Abstract

objectiveTo critically evaluate machine learning (ML) models developed for predicting radiation-induced oral mucositis (OM) in head and neck cancer (HNC) patients, emphasizing the contribution of advanced imaging biomarkers such as radiomic and dosiomic features. MATERIALS AND

methodsThis systematic review followed the PRISMA 2020 guidelines and was registered in PROSPERO (CRD420251075245). The PICOS framework guided the review question and inclusion criteria. Studies developing or validating ML models for OM prediction using radiomic and/or dosiomic data were included. Electronic databases (PubMed, EMBASE, Web of Science, Scopus) were searched up to July 2025. The risk of bias was assessed using the PROBAST tool, and models were compared based on predictor types.

resultsSeven studies involving 2,311 patients were included. All models incorporated dosimetric predictors, while some used additional radiomic (n = 3), dosiomic (n = 2), contouromic (n = 1), or oral health indices (n = 1). Reported discrimination (AUC) ranged from 0.70 to 0.96, but external validations showed reduced performance (AUC ~ 0.65). High risk of bias was mainly observed in analysis domains due to limited sample sizes and incomplete calibration.

conclusionML models for OM prediction remain exploratory and are not yet ready for routine clinical implementation due to poor generalizability and calibration issues.

Indexed as

DosiomicsHead and neck cancerMachine learningOral mucositisPredictive modelingRadiomics

Identifiers

PMID42258140

What OpenQuestion holds

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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.