ArticleBMC cancer2024
Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Radiomics in laryngeal squamous cell carcinoma: state of the art.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2026Review
- Sex-based immune differences influence tumor progression in head and neck squamous cell carcinoma.Oral oncology · 2026Article
- Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.International journal of general medicine · 2026Review
- Antitumor Effects of Metformin in Squamous Cell Carcinoma under Leptin Treatment Conditions.Preventive nutrition and food science · 2025Article
- Habitat imaging radiomics increases the accuracy of a nomogram for predicting Ki-67-positivity in laryngeal squamous cell carcinoma.European journal of radiology open · 2025Article
- Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma.Scientific reports · 2025Article
- Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Digital spatial profiling identifies phospho-JNK as a biomarker for early risk stratification of aggressive prostate cancer.Frontiers in oncology · 2025Article
- Using non-Gaussian diffusion models to distinguish benign from malignant head and neck lesions.Frontiers in oncology · 2025Article
- Recent advances in biomarker detection of oral squamous cell carcinoma.Frontiers in oncology · 2025Review
- MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma.Frontiers in medicine · 2024Article
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12 authors.
Funding
Abstract
backgroundThis study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI).
methodsA total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility.
resultsThirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model.
conclusionsThe ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.
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