Advanced Biomarkers and Precision Medicine: Innovative Strategies to Prevent Cancer Recurrence
Bibliographic record
Abstract
Objective: This review aims to synthesize evidence on the efficacy and challenges of precision medicine strategies in cancer treatment, focusing on their role in mitigating recurrence and enhancing patient-specific therapy. Data Sources: Examination of current literature on precision medicine techniques such as immunotherapy (including checkpoint inhibitors, adoptive cell therapy, and cancer vaccines), genetic and molecular profiling for personalized treatment strategies, predictive biomarkers for selecting responsive patients, AI for improved diagnostic and prognostic accuracy, and liquid biopsies for non-invasive monitoring of minimal residual disease. Conclusion: Precision medicine in oncology offers a paradigm shift toward personalized care, potentially reducing cancer recurrence through tailored treatment modalities. While immunotherapy introduces novel mechanisms to fight cancer, its efficacy is sometimes limited by tumor evolution. Genetic and molecular profiling, along with predictive biomarkers, enable the customization of therapy plans. AI and machine learning algorithms promise to refine detection, treatment, and monitoring processes. Liquid biopsies emerge as a pivotal tool for early detection and surveillance of cancer recurrence. Further research and clinical trials are crucial for integrating these advanced strategies into standard care, aiming to enhance patient outcomes and minimize recurrence rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".