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openalexCancers2026-07-23Cited by 0

CDK4/6 Inhibitors in Breast Cancer: Clinical Applications, Translational Insights, and Future Directions

Mengying Guan, Hua Hao

Cyclin-dependent kinase 4/6 inhibitors have fundamentally changed the management of hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. However, these drugs are not interchangeable and the field is moving away from the notion of a uniform “class effect.” In early breast cancer, adjuvant abemaciclib and ribociclib improve invasive disease-free survival in patients at a high risk of recurrence, whereas palbociclib does not. This difference likely stems from agent-specific pharmacological profiles, differences in trial design, and patient selection, rather than simply dosing nuances. In metastatic breast cancer, all three agents prolong progression-free survival when combined with endocrine therapy, but only ribociclib and potentially abemaciclib have shown an overall survival advantage. In addition, resistance remains a major obstacle in clinical practice. We propose that resistance mechanisms can be meaningfully grouped into two categories: target-driven (e.g., RB1 loss, CDK6 amplification) and bypass-driven (e.g., ESR1 mutations, PI3K/AKT pathway activation, APOBEC3-mediated mutagenesis). Distinguishing between these classes helps in the design of rational sequencing algorithms and combinatorial regimens. Emerging strategies, such as next-generation protein degraders, oral selective estrogen receptor degraders, antibody–drug conjugates, and inhibition of autophagy, are promising methods for overcoming resistance. Moving forward, the greatest need in breast cancer treatment will be not simply developing additional agents but using current therapies more intelligently by refining biomarker-guided patient selection, tailoring treatment duration, and ensuring broad global access.

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crossrefCancers2026-04-03Cited by 1

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Background: Glioblastoma is an extremely aggressive brain tumor that diffusely infiltrates white matter and alters large-scale brain connectivity. Most prognostic models focus on localized tumor features and clinical variables, overlooking broader effects on the brain’s structura…

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crossrefCancers2026-03-18

From LQ to AI-BED-Fx: A Unified Multi-Fraction Radiobiological and Machine-Learning Framework for Gamma Knife Radiosurgery Across Intracranial Pathologies

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crossrefCancers2026-03-11

Unlocking Tumor Aggressiveness in Endometrial Cancer: AI-Driven PET/CT Radiomics and Machine Learning for Prediction of High-Risk Tumor Histology

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Purpose: Accurate preoperative risk stratification in endometrial cancer (EC) is essential for guiding surgical and therapeutic decisions. This study aimed to evaluate the discriminative performance of [18F]-FDG PET/CT-derived radiomic features combined with machine learning mode…

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crossrefCancers2026-01-29

MAGE (Multimodal AI-Enhanced Gastrectomy Evaluation): Comparative Analysis of Machine Learning Models for Postoperative Complications in Central European Gastric Cancer Population

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Introduction: By leveraging dedicated datasets and predictive modeling, machine-learning (ML) algorithms can estimate the probability of both short- and long-term outcomes after surgery. The aim of this study was to evaluate the ability of ML-based models to predict postoperative…

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crossrefCancers2025-10-31Cited by 1

Subtype Characterization of Ovarian Cancer Cell Lines Using Machine Learning and Network Analysis: A Pilot Study

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Background/Objectives: Ovarian cancer is a heterogeneous malignancy with molecular subtypes that strongly influence prognosis and therapy. High-dimensional mRNA data can capture this biological diversity, but its complexity and noise limit robust subtype characterization. Further…

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