ArticleFrontiers in oncology2022
XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Pipeline for Mammography-Based Breast Cancer Detection: An Integrated Systematic Review and Large-Scale Experimental Validation.Medicina (Kaunas, Lithuania) · 2025Pooled it
- 3.0 T multi-parametric MRI combined with clinical features improve malignancy prediction of BI-RADS 4 lesions and preoperative prediction of Nottingham Prognostic Index.European journal of radiology open · 2025Article
- Diagnostic Accuracy of Machine Learning-Assisted MRI for Mild Cognitive Impairment in Parkinson's Disease: A Systematic Review and Meta-Analysis.Parkinson's disease · 2025Review
- 3.0 T multi-parametric MRI reveals metabolic and microstructural abnormalities in the posterior visual pathways in patients with thyroid eye disease.Frontiers in neuroscience · 2023Article
- Exploring the Potential of Machine Learning Algorithms to Improve Diffusion Nuclear Magnetic Resonance Imaging Models Analysis.Journal of medical physicsArticle
- Diagnostic value of Peptest™ combined with gastroesophageal reflux disease questionnaire in identifying patients with gastroesophageal reflux-induced chronic cough.Chronic respiratory diseaseArticle
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7 authors.
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Abstract
Background: The malignant probability of MRI BiRADS 4 breast lesions ranges from 2% to 95%, leading to unnecessary biopsies. The purpose of this study was to construct an optimal XGboost prediction model through a combination of DKI independently or jointly with other MR imaging features and clinical characterization, which was expected to reduce false positive rate of MRI BiRADS 4 masses and improve the diagnosis efficiency of breast cancer. Methods: 120 patients with 158 breast lesions were enrolled. DKI, Diffusion-weighted Imaging (DWI), Proton Magnetic Resonance Spectroscopy ( Results: MK derived from DKI performed better for differentiating between malignant and benign lesions than ADC, MD, tCho, K Conclusions: DKI is promising for breast cancer diagnosis and prognostic factor assessment. An optimized XGboost model that included DKI, age, shape and menstrual status is effective in improving the diagnostic accuracy of BiRADS 4 masses.
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