ArticleEuropean journal of radiology open2026
Performance of magnetic-resonance imaging radiomics in prediction of response after neoadjuvant chemotherapy in head and neck squamous cell carcinoma: A systematic review and meta-analysis.
Article in European journal of radiology open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational and AI-Enabled Imaging Biomarkers for Predicting and Assessing Immunotherapy Response in Oral Squamous Cell Carcinoma: A Systematic Review with Functional Meta-Synthesis.Medical sciences (Basel, Switzerland) · 2026Pooled it
- Performance of MRI-based deep learning models in differentiation of triple negative breast cancer from other breast cancer subtypes: A systematic review and meta-analysis.European journal of radiology open · 2026Article
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Authors and funding
7 authors.
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Abstract
Objective: To evaluate performance of MRI-based radiomics for evaluating response to neoadjuvant chemotherapy (NACT) in head and neck squamous cell carcinoma (HNSCC) patients. Method: We performed a comprehensive search across four databases until July, 2025 to include studies evaluating diagnostic performance of MRI radiomics in response assessment. Methodological quality of studies was assessed using METhodological RadiomICs Score (METRICS). Using a random-effects bivariate model diagnostic values of area under the curve (AUC), sensitivity, and specificity were calculated. Meta-regression and subgroup analyses were used to explore the source of heterogeneity. Results: Twenty-one studies were included. The overall predictive performance of MRI-radiomics in the validation set was good, with a summary AUC of 0.84 (95% CI: 0.81-0.87) in differentiation of complete or partial response (CR/PR) and non-responders as those with stable or progressive disease (SD/PD). High heterogeneity was observed, but there was no evidence of publication bias (Deeks' test p = 0.20). Subgroup analysis resolved the heterogeneity and showed similar performance of model across different therapeutic regimens, study designs, study qualities, and MRI magnitudes of strength. For classifying only CR as responders and PR + SD + PD as non-responders, MRI-radiomics achieved an AUC of 0.83 (95% CI: 0.79-0.86) with low heterogeneity. Conclusion: MRI radiomics demonstrated high predictive value in evaluating NACT response for HNSCC patients, showing consistent performance across different clinical scenarios. However, the current evidence base is largely retrospective, geographically limited, and predominantly derived from nasopharyngeal cancer cohorts; therefore, broader external validation and prospective multicenter designs are required before routine clinical implementation.
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