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.
This study demonstrates that combining Arylsulfatase B (ARSB), which promotes melanoma cell apoptosis via COP1 upregulation, with Pembrolizumab, which targets cytotoxic lymphocytes, yields synergistic effects that reduce tumor invasiveness and improve survival outcomes in metastatic B16F10 melanoma.
This study introduces and validates PROGRESS, a simplified machine-learning-derived risk stratification score for castration-resistant prostate cancer that utilizes three routinely available laboratory variables (PSA, ALP, and AST) to effectively distinguish patient risk subgroups and guide individualized clinical decision-making across diverse metastatic and non-metastatic settings.
This Czech national study utilized linked administrative and registry data to evaluate stage-stratified quality indicators for non-small-cell lung cancer care, revealing that despite improvements in multidisciplinary team discussions and centralization, fewer than half of patients initiated treatment within eight weeks and significant regional disparities in biomarker testing persist, leading to the implementation of these metrics as a national tool for continuous quality evaluation.
This study utilized network analysis on a large dataset of 50,000 pancreatic cancer patients to map symptom interdependencies across disease stages, revealing that while symptom networks become sparser over time and node predictability remains low, specific hubs like jaundice and weight loss emerge as dominant indicators that could aid in earlier clinical recognition.
This study demonstrates that the log odds of positive lymph nodes (LODDs) outperforms traditional AJCC N stage and lymph node ratio in prognostic accuracy for gastric adenocarcinoma, leading to the development of a validated nomogram for individualized survival assessment.
This study demonstrates that the prognostic impact of tumor-associated macrophages reverses across tumor histologies, with high expression of markers like FOLR2 and TREM2 predicting improved survival in melanoma but worse outcomes in lung squamous carcinoma, highlighting the critical importance of tumor-context-dependent macrophage polarization for therapeutic strategies.
This study introduces an integrated framework combining empirical interpolation, Dual Annealing-optimized logistic modeling, and the ThresholdXpert 1.0 combinatorial optimization tool to robustly determine diagnostic cut-off points, successfully identifying optimized multimarker panels for hepatocellular carcinoma.
This study demonstrates that integrating a 26-gene molecular prognostic classifier into the 9th edition TNM staging system creates a novel TNMEx model that significantly outperforms both the 8th and 9th editions in risk stratification and survival prediction for resected lung adenocarcinoma patients.
This study provides the first global mapping of research facilities conducting phase III oncology trials, revealing that trial availability is directly tied to the number of physical facilities and that research capacity in lower-income regions remains constrained, predominantly limited to industry-sponsored, multiregional systemic therapy trials.
This study demonstrates that fusing CT deep learning, radiomics, and peripheral blood immune profiles significantly improves the diagnostic accuracy of lung cancer in symptomatic patients, achieving an AUC of 0.81 compared to single-modality approaches.