Evidence map›Paper›PMID 41146071›Full record

SynthesisBMC cancer2025

Application of artificial intelligence in head and neck tumor segmentation: a comparative systematic review and meta-analysis between PET and PET/CT modalities.

Hamed Hajimokhtari, Tina Soleymanpourshamsi, Leila Rostamian, Ailar Yousefbeigi, Soheil Jafari, Asal Rezaeiyazdi, Mohammadjavad Askari, Maryam Khalilian, Parsa Vafaei, Mahla Esfahaniani and 3 more

Abstract readSystematic ReviewMeta-AnalysisComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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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

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Hamed HajimokhtariIslamic Azad University of Isfahan, Isfahan, Iran.
Tina SoleymanpourshamsiTehran University of Medical Sciences, Tehran, Iran.
Leila RostamianTehran University of Medical Sciences, Tehran, Iran.
Ailar YousefbeigiUniversity of California, Los Angeles, Los Angeles, USA.
Soheil JafariTehran University of Medical Sciences, Tehran, Iran.
Asal RezaeiyazdiIsfahan University of Medical Sciences, Isfahan, Iran.
Mohammadjavad AskariIsfahan University of Medical Sciences, Isfahan, Iran.
Maryam KhalilianIslamic Azad University of Isfahan, Isfahan, Iran.
Parsa VafaeiIslamic Azad University of Isfahan, Isfahan, Iran. itsparsavafaei@gmail.com.
Mahla EsfahanianiGolestan University of Medical Sciences, Gorgan, Iran.
Gianrico SpagnuoloUniversity of Naples Federico II, Naples, Italy.
Shirin ShahnaseriMeharry Medical College, Nashville, USA.
Parisa SoltaniUniversity of Naples Federico II, Naples, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFor the effective treatment planning of head and neck cancers, precise tumor segmentation is vital. The combination of artificial intelligence (AI) technology with imaging systems like positron emission tomography (PET) and PET/ computed tomography (PET/CT) has made attempts to automate these processes. Despite these attempts, the usefulness of AI segmentation with PET imaging compared to PET/CT still lacks clarity.

methodsA comprehensive search was performed on Scopus, Embase, PubMed, Cochrane, Web of Science, and Google Scholar for studies published before Dec 2024, with an update in March 2025. Included studies utilized AI algorithms to segment head and neck tumors via PET or PET/CT and provided quantitative performance measures. Pooled estimates of Dice Similarity Coefficient (DSC) sensitivity, precision, and Hausdorff Distance (HD95) were calculated using a random-effects model. Also, sensitivity analyses were performed to find the potential source of heterogeneity. Additionally, subgroup analyses were conducted for overall and primary tumor segmentation. Publication bias was assessed using weighted Egger's test, followed by presentation of funnel plots for different metrics. Risk of bias (RoB) was evaluated using the QUADAS-C tool. Also, CLAIM was used to assess methodological quality and robustness of the included studies.

resultsEleven studies were included. All included studies were rated as having a low risk of bias. Also, CLAIM scores showed a high methodological quality in the studies. There was a significant difference between PET/CT and PET-only modalities. Pooled effectiveness metrics showed improvement in their respective DSC of 0.05 (95% CI 0.03-0.07), sensitivity by 0.04, and precision by 0.05, and HD95 decreased by approximately 3 mm. There was low heterogeneity for most metrics except HD95, which showed a high heterogeneity (I

conclusionsThe performance of AI-assisted segmentation using PET/CT is greater than that of PET-only in neck and head tumors. These results justify the clinical use of AI-based PET/CT imaging beyond contouring due to its automation potential and highlight the importance of unified datasets alongside distributed learning systems that improve the applicability and consistency of clinical workflows. TRIAL REGISTRATION NUMBER: The study protocol was registered at PROSPERO [CRD42024614436].

Indexed as

Artificial IntelligenceHead and Neck NeoplasmsPositron-Emission TomographyPositron Emission Tomography Computed TomographyHumansArtificial intelligenceDeep learningHead and neck cancerPET/CTRadiomicsTumor segmentation

Identifiers

PMID41146071
PMCPMC12560352

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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.