ArticleBMC cancer2020
Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis.Diagnostics (Basel, Switzerland) · 2026Review
- Radiomics for Detecting Metaplastic Histology in Triple-Negative Breast Cancer: A Step Towards Personalized Therapy.Bioengineering (Basel, Switzerland) · 2025Article
- The Role of Synthetic MRI and Radiomics in Breast Cancer: A Review.Advanced biomedical research · 2025Review
- Performance of low- and high-temporal-resolution DCE-MRI texture analysis in distinguishing breast lesions from background enhancement.American journal of translational research · 2025Article
- Radiomics analysis in differentiating osteosarcoma and chondrosarcoma based on T2-weighted imaging and contrast-enhanced T1-weighted imaging.Scientific reports · 2024Article
- Predicting HER2 Status Associated with Breast Cancer Aggressiveness Using Four Machine Learning Models.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Differences in Histological Subtypes of Invasive Lobular Breast Carcinoma According to Immunohistochemical Molecular Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward.BJR artificial intelligence · 2024Review
- The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review.Journal of medical radiation sciences · 2023Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer.Sensors (Basel, Switzerland) · 2023Article
- Fisher discriminant model based on LASSO logistic regression for computed tomography imaging diagnosis of pelvic rhabdomyosarcoma in children.Scientific reports · 2022Article
- Prediction of carotid plaque by blood biochemical indices and related factors based on Fisher discriminant analysis.BMC cardiovascular disorders · 2022Article
- Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma.BMC cancer · 2022Article
- Immunological significance of prognostic markers for breast cancer based on alternative splicing.American journal of translational research · 2022Article
- Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence.La Radiologia medica · 2022Review
- Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.Frontiers in oncology · 2021Article
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7 authors.
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Abstract
backgroundThe clinicopathological classification of breast cancer is proposed according to therapeutic purposes. It is simplified and can be conducted easily in clinical practice, and this subtyping undoubtedly contributes to the treatment selection of breast cancer. This study aims to investigate the feasibility of using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI for predicting the clinicopathological subtypes of breast cancer.
methodsPatients who underwent breast magnetic resonance imaging were confirmed by retrieving data from our institutional picture archiving and communication system (PACS) between March 2013 and September 2017. Five clinicopathological subtypes were determined based on the status of ER, PR, HER2 and Ki-67 from the immunohistochemical test. The radiomic features of diffusion-weighted imaging were derived from the volume of interest (VOI) of each tumour. Fisher discriminant analysis was performed for clinicopathological subtyping by using a backward selection method. To evaluate the diagnostic performance of the radiomic features, ROC analyses were performed to differentiate between immunohistochemical biomarker-positive and -negative groups.
resultsA total of 84 radiomic features of four statistical methods were included after preprocessing. The overall accuracy for predicting the clinicopathological subtypes was 96.4% by Fisher discriminant analysis, and the weighted accuracy was 96.6%. For predicting diverse clinicopathological subtypes, the prediction accuracies ranged from 92 to 100%. According to the cross-validation, the overall accuracy of the model was 82.1%, and the accuracies of the model for predicting the luminal A, luminal B
conclusionsThe Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI is a reliable method for the prediction of clinicopathological breast cancer subtypes.
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