breast-cancer

A new peer-reviewed study identifies 485 chemicals in US pesticides linked to breast cancer, highlighting regulatory gaps and public health concerns.

A causal multi-modal AI model predicts personalized chemosensitivity for breast cancer using pathology and clinical data, outperforming existing tests and potentially reducing chemotherapy use by 30%.

A French court has recognized cosmic radiation as a factor in a flight attendant's breast cancer, in a landmark ruling that may enable similar occupational disease claims.

Scientists have developed an AI platform called CenSegNet to analyze centrosomes in breast cancer tumours, uncovering new patterns that can predict cancer progression and lead to personalized treatments.

A research paper integrates H&E-based deep learning recurrence risk heatmaps with mass spectrometry spatial proteomics to identify intratumoral molecular niches associated with recurrence in triple-negative breast cancer, achieving strong predictive performance and revealing distinct mitotic vs. immune programs.

This paper systematically benchmarks classical machine learning models (Random Forest, XGBoost, etc.) for ER status prediction using multi-omics data from TCGA-BRCA, finding that RNA expression provides the strongest predictive signal and that Random Forest achieves 90.3% balanced accuracy in the integrated multi-omic setting.

This paper proposes UMMT, a token-level cross-modal transformer with contrastive multi-task learning for breast cancer subtype classification and survival prediction, achieving state-of-the-art results on METABRIC and TCGA-BRCA datasets.

Evaluates agentic LLM systems for generating breast cancer treatment recommendations using 72 clinical cases, finding that the best system (Claude Opus 4.8 with D&C+SA pipeline) achieved a global score of 0.594 but remains insufficient for unsupervised clinical use due to persistent errors.

This paper proposes a token-centric dual-view learning framework that unifies prompt-based adaptation and cross-view fusion within a frozen vision transformer to improve breast cancer classification from mammography images, achieving consistent improvements on VinDr-Mammo and CMMD datasets.

This paper systematically evaluates three survival models (Cox, DeepSurv, RSF) under federated learning on heterogeneous breast cancer data, finding that FL outperforms local training and RSF offers the best balance of performance across clients.

This paper examines the integration of multi-modal clinical data, including treatment records, pathology reports, and clinician notes, using rule-based extraction and machine learning to improve breast cancer recurrence prediction compared to single-modal approaches.

This paper presents a deployment-oriented stress-testing framework to evaluate how well large language models identify side effects of breast cancer radiation treatments. The study highlights limitations in LLM reliability, such as sensitivity to minor documentation changes and under-recall of rare side effects, suggesting that grounding outputs in clinician-curated lists improves robustness.