ArticleBreast cancer research : BCR2023
Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study.
Article in Breast cancer research : BCR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Deciphering the biological underpinnings behind prognostic MRI-based imaging signatures in breast cancer: a systematic review.Journal of translational medicine · 2025Pooled it
- Deep learning radiomics based on preoperative multiparametric MRI in predicting breast cancer recurrence risk.BMC medical imaging · 2026Article
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- Pretreatment Prediction of Tumor Recurrence in Breast Cancer After Neoadjuvant Systemic Therapy Using Machine Learning With Clinical and CT Radiomics Features.The Kaohsiung journal of medical sciences · 2026Article
- Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.Journal of medical Internet research · 2026Article
- Multimodal radiomics incorporating intratumoral heterogeneity for prognostic assessment of metastatic outcomes in invasive breast cancer.Breast cancer research : BCR · 2026Article
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- Integrating deep learning and radiomics for precise identification of luminal A/B breast cancer subtypes on dynamic contrast-enhanced MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Review
- Radiomics and clinical data predict pseudoprogression after radiotherapy in high-grade glioma.Frontiers in oncology · 2026Article
- Multi-institutional MRI-based radiomic pilot study to measure the variations between scanner vendors and imaging sessions.Frontiers in oncology · 2026Article
- An explainable-AI framework reveals novel lncRNAs specific for breast cancer subtypes.Frontiers in bioinformatics · 2026Article
- Machine learning models based on magnetic resonance imaging for predicting Lymphovascular Invasion in Invasive Breast Cancer.PloS one · 2026Article
- Clinical utility of artificial intelligence models in radiology: a systemic scoping review of diagnostic and endovascular applications.CVIR endovascular · 2025Review
- Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies.Cell communication and signaling : CCS · 2025Review
- Preliminary Evaluation of Radiomics in Contrast-Enhanced Mammography for Prognostic Prediction of Breast Cancer.Cancers · 2025Article
- Development of PDAC diagnosis and prognosis evaluation models based on machine learning.BMC cancer · 2025Article
- Article
- Predicting the prognosis of epithelial ovarian cancer patients based on deep learning models.Frontiers in oncology · 2025Article
- Article
Corrections and comments
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Authors and funding
18 authors at 8 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms.
methodsIn this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics.
resultsThe RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort.
conclusionsThis study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.