Evidence map›Paper›PMID 39708187›Full record

ArticleClinical oral investigations2024

Automated classification of pathological differentiation in head and neck squamous cell carcinoma using combined radiomics models from CET1WI and T2WI.

Yang Li, Wen Li, Haotian Xiao, Weizhong Chen, Jie Lu, Nengwen Huang, Qingling Li, Kangwei Zhou, Ikuho Kojima, Yiming Liu and 1 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Clinical oral investigations, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
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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

11 authors.

Yang Li *Institute of Stomatology & Research Center of Dental and Craniofacial Implants, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China.
Wen Li *School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China.
Haotian XiaoDepartment of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Weizhong ChenDepartment of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Jie LuSchool and Hospital of Stomatology, Fujian Medical University, Fuzhou, China.
Nengwen HuangSchool and Hospital of Stomatology, Fujian Medical University, Fuzhou, China.
Qingling LiDepartment of Periodontology and Endodontology, Tohoku University Graduate School of Dentistry, Sendai, Japan.
Kangwei ZhouDepartment of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Ikuho KojimaDepartment of Oral Diagnosis, Tohoku University Graduate School of Dentistry, Sendai, Japan.
Yiming LiuDepartment of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yanjing OuInstitute of Stomatology & Research Center of Dental and Craniofacial Implants, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China. ouyanjing_FJMU@163.com.

Funding

the National Natural Science Foundation of China U190415the Natural Science Foundation of Fujian Province 2022J01267the Natural Science Foundation of Henan Province No. 232300420225
6 · The paper itself

Abstract

objectivesThis study aims to develop an automated radiomics-based model to grade the pathological differentiation of head and neck squamous cell carcinoma (HNSCC) and to assess the influence of various magnetic resonance imaging (MRI) sequences on the model's performance. MATERIALS AND

methodsWe retrospectively analyzed MRI data from 256 patients across two medical centers, including both contrast-enhanced T1-weighted images (CET1WI) and T2-weighted images (T2WI). Regions of interest were delineated for radiomics feature extraction, followed by dimensionality reduction. An XGBoost classifier was then employed to build the predictive model, with its classification efficiency assessed using receiver operating characteristic curves and the area under the curve (AUC).

resultsIn validation cohort, the AUC (macro/micro) values for models utilizing CET1WI, T2WI, and the combination of CET1WI and T2WI were 0.801/0.814, 0.741/0.798, and 0.885/0.895, respectively. The AUC for the three differentiations, ranging from well-differentiated to poorly differentiated, were 0.867, 0.909, and 0.837, respectively. The macro/micro precision, recall, and F1 scores of 0.688/0.736, 0.744/0.828, and 0.685/0.779 for the CET1WI + T2WI model.

conclusionThis study demonstrates that constructing a radiomics model based on CET1WI and T2WI sequences can be used to predict the pathological differentiation grading of HNSCC patients. CLINICAL RELEVANCE: This study suggests that a radiomics model integrating CET1WI and T2WI MRI sequences can effectively predict the pathological differentiation of HNSCC, providing an alternative diagnostic approach through non-invasive preoperative methods.

Indexed as

Head and Neck NeoplasmsMagnetic Resonance ImagingSquamous Cell Carcinoma of Head and NeckAdultAgedContrast MediaFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedNeoplasm GradingRadiomicsRetrospective StudiesContrast MediaHead and neck squamous cell carcinomaPathological differentiationRadiomicsWavelet transformationsXGBoost classifier

Identifiers

PMID39708187

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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.