ArticleFrontiers in oncology2022
Prediction of Prognostic Factors and Genotypes in Patients With Breast Cancer Using Multiple Mathematical Models of MR Diffusion Imaging.
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 10 papers.
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10 citing papers in PubMed, 25 citations in OpenAlex.
- Integrating time-dependent diffusion MRI and intravoxel incoherent motion for predicting NPI and molecular subtypes in breast cancer.BMC medical imaging · 2026Article
- Machine learning model based on dual-layer detector spectral CT radiomics features for differentiating luminal and non-luminal breast cancer.Frontiers in oncology · 2026Article
- Multimodal imaging and advanced quantitative techniques for HER-2 status prediction in breast cancer.Discover oncology · 2025Review
- A multifunctional MRI model based on IVIM and DKI predicts HIF-1α, Ki-67, and VEGF status in breast carcinoma.Frontiers in oncology · 2025Article
- The impact of radiogenomics on breast cancer.American journal of cancer research · 2025Review
- Multimodal apparent diffusion MRI model in noninvasive evaluation of breast cancer and Ki-67 expression.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Colorectal cancer histopathology image analysis: A comparative study of prognostic values of automatically extracted morphometric nuclear features in multispectral and red-blue-green imagery.Histology and histopathology · 2024Article
- Histogram analysis comparison of readout-segmented and single-shot echo-planar imaging for differentiating luminal from non-luminal breast cancer.Scientific reports · 2024Article
- Value of genomics- and radiomics-based machine learning models in the identification of breast cancer molecular subtypes: a systematic review and meta-analysis.Annals of translational medicine · 2022Article
- Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
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8 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeTo explore the clinical value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) based on diffusion-weighted MRI (DW-MRI) for predicting genotypes and prognostic factors of breast cancer. MATERIALS AND
methodsA total of 227 patients with breast cancer confirmed by pathology were reviewed retrospectively. Diffusion-weighted imaging (DWI), IVIM, and DKI were performed in all patients. The corresponding ADC, true diffusion coefficient (D), perfusion-related diffusion coefficient (D*), perfusion fraction (f), mean diffusion rate (MD), and mean kurtosis value (MK) were measured. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve were used to analyze the diagnostic efficacy in predicting the Nottingham prognostic index (NPI), the expression of antigen Ki-67, and the molecular subtypes of breast cancer. The nomogram of the combined genotype-prediction model was established based on the multivariate logistic regression model results.
resultsD* and MK values were significantly higher in the high-grade Nottingham group (NPI ≥ 3.4) than the low-grade Nottingham group (NPI < 3.4) (p < 0.01). When D* ≥ 30.95 × 10
conclusionDWI, IVIM, and DKI, as MR diffusion imaging techniques with different mathematical models showed potential to identify the prognosis and genotype of breast cancer. In addition, the combination of these three models can improve the diagnostic efficiency and thus may contribute to opting for an appropriate therapeutic approach in clinic treatment.
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