ArticleEuropean radiology2022
Multi-parametric MRI-based radiomics signature for preoperative prediction of Ki-67 proliferation status in sinonasal malignancies: a two-centre study.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 34 citations in OpenAlex.
- Reproducibility of radiomics quality score: an intra- and inter-rater reliability study.European radiology · 2024Trial
- MRI-based radiomics nomogram for distinguishing solitary fibrous tumor from schwannoma in the orbit: a two-center study.European radiology · 2024Trial
- Application Value of a nomogram integrating contrast-enhanced CT radiomics and clinical indicators in evaluating lymph node metastasis in pediatric peripheral neuroblastoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Multiparametric MRI radiomics model incorporating ADC improves differentiation of benign and malignant sinonasal tumors.Neuroradiology · 2026Article
- CT-based radiomics nomogram for preoperative prediction of Ki-67 in lung neuroendocrine neoplasms: a multicenter study.BMC medical imaging · 2026Article
- Review
- Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning.European radiology · 2025Article
- Sinonasal adenoid cystic carcinoma: preoperative apparent diffusion coefficient histogram analysis in prediction of prognosis and Ki-67 proliferation status.Japanese journal of radiology · 2025Article
- Preoperative multiparameter MRI-based prediction of Ki-67 expression in primary central nervous system lymphoma.Precision radiation oncology · 2025Article
- Multicenter study on predicting postoperative upper limb muscle strength improvement in cervical spinal cord injury patients using radiomics and deep learning.Scientific reports · 2025Article
- Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study.BMC medical imaging · 2025Article
- Prognostic value of FDX1, the cuprotosis key gene, and its prediction models across imaging modalities and histology.BMC cancer · 2024Article
- Radiomics nomogram based on CT radiomics features and clinical factors for prediction of Ki-67 expression and prognosis in clear cell renal cell carcinoma: a two-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Development of a CT radiomics nomogram for preoperative prediction of Ki-67 index in pancreatic ductal adenocarcinoma: a two-center retrospective study.European radiology · 2024Article
- Article
- Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study.BMC cancer · 2024Article
- Multiparametric MRI-based radiomics approach with deep transfer learning for preoperative prediction of Ki-67 status in sinonasal squamous cell carcinoma.Frontiers in oncology · 2024Article
- An MRI-based radiomics nomogram for preoperative prediction of Ki-67 index in nasopharyngeal carcinoma: a two-center study.Frontiers in oncology · 2024Article
- Contrast enhanced magnetic resonance imaging-based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study.Quantitative imaging in medicine and surgery · 2023Article
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Authors and funding
8 authors at 2 institutions in 1 country.
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Abstract
objectiveTo assess the predictive ability of a multi-parametric MRI-based radiomics signature (RS) for the preoperative evaluation of Ki-67 proliferation status in sinonasal malignancies.
methodsA total of 128 patients with sinonasal malignancies that underwent multi-parametric MRIs at two medical centres were retrospectively analysed. Data from one medical centre (n = 77) were used to develop the predictive models and data from the other medical centre (n = 51) constitute the test dataset. Clinical data and conventional MRI findings were reviewed to identify significant predictors. Radiomics features were determined using maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms. Subsequently, RSs were established using a logistic regression (LR) algorithm. The predictive performance of RSs was assessed using calibration, decision curve analysis (DCA), accuracy, and AUC.
resultsNo independent predictors of high Ki-67 proliferation were observed based on clinical data and conventional MRI findings. RS-T1, RS-T2, and RS-T1c (contrast enhancement T1WI) were established based on a single-parametric MRI. RS-Combined (combining T1WI, FS-T2WI, and T1c features) was developed based on multi-parametric MRI and achieved an AUC and accuracy of 0.852 (0.733-0.971) and 86.3%, respectively, on the test dataset. The calibration curve and DCA demonstrated an improved fitness and benefits in clinical practice.
conclusionsA multi-parametric MRI-based RS may be used as a non-invasive, dependable, and accurate tool for preoperative evaluation of the Ki-67 proliferation status to overcome the sampling bias in sinonasal malignancies. KEY POINTS: • Multi-parametric MRI-based radiomics signatures (RSs) are used to preoperatively evaluate the proliferation status of Ki-67 in sinonasal malignancies. • Radiomics features are determined using maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms. • RSs are established using a logistic regression (LR) algorithm.
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