97 papers
Oncology is the dynamic field dedicated to understanding and treating cancer, a complex group of diseases where cells grow uncontrollably. This area of study spans everything from identifying genetic mutations that drive tumor formation to developing new therapies and improving patient care strategies. Because cancer research moves at a rapid pace, staying updated with the very latest findings is essential for both experts and the curious public.
At Gist.Science, we curate the most recent preprints in oncology directly from medRxiv, ensuring you have immediate access to cutting-edge research before it undergoes formal peer review. We process every new submission in this category, transforming dense scientific manuscripts into both plain-language overviews and detailed technical summaries. This dual approach makes critical discoveries accessible to everyone, regardless of their background in medicine or biology.
Below are the latest oncology papers added from medRxiv, complete with our simplified explanations and full technical breakdowns to help you navigate the latest breakthroughs in cancer science.
By applying a novel approach to quantify evolutionary tree shape in the TRACERx non-small cell lung cancer cohort, this study demonstrates that clone tree balance is a robust predictor of disease-free survival that outperforms previous evolutionary indices and suggests the evenness of branching is more critical for clinical outcomes than intratumour heterogeneity.
In a retrospective single-institution study of 57 patients with pT1-2N1 breast cancer, postmastectomy radiotherapy (PMRT) was not associated with improved recurrence-free or overall survival, suggesting that outcomes are primarily driven by tumor biology, nodal burden, and systemic therapy rather than PMRT.
This SEER database study demonstrates that while liver transplantation yields excellent long-term survival for early-stage hepatocellular carcinoma, its outcomes for early-stage intrahepatic cholangiocarcinoma are significantly inferior, highlighting the need for distinct selection criteria and further molecular research to identify potential ICC subpopulations that may benefit.
This study demonstrates that machine learning models trained on National Cancer Database data and refined with real-world electronic health records using transfer learning and threshold optimization can accurately predict rapid relapse in triple-negative breast cancer patients, offering a promising tool for early identification and improved clinical care.
This study demonstrates that integrating patient-reported insomnia changes with a mathematical tumor growth model can predict treatment progression in non-small cell lung cancer patients with approximately 72% accuracy, enabling clinicians to intervene 6–8 weeks earlier than usual.
This study defines three molecular subtypes of cervical cancer based on PIK3CA and YAP1 status, demonstrating that PI3K pathway inhibitors selectively suppress PIK3CA-mutant tumors and synergize with HPV-directed immunotherapy to enhance anti-tumor cytotoxicity.
This study protocol outlines a randomized controlled trial investigating whether an individualized, home-based prehabilitation program combining exercise, nutrition, and monitoring can improve brain health and cognitive function in patients with colorectal cancer undergoing chemotherapy.