ReviewMedComm2024
The roles of patient-derived xenograft models and artificial intelligence toward precision medicine.
Review in MedComm, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Deciphering the rules of regulated cell death subroutines by in silico methods.Journal of advanced research · 2026Review
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- Review
- Lipid nanoparticles for bone marrow-targeted RNA therapeutics.Journal of nanobiotechnology · 2026Review
- Harnessing PDX and PDX 2.0: the next-generation paradigm for precision oncology and translational breakthroughs.Molecular cancer · 2026Review
- A clinically concordant vulvar cancer patient-derived xenograft model retaining primary molecular signature for preclinical drug sensitivity assessment.Open medicine (Warsaw, Poland) · 2026Article
- Lung cancer organoids for functional precision oncology: from disease modeling to clinical decision support.Frontiers in oncology · 2026Review
- Recent advances in animal models for pathological scar research: A comprehensive review of experimental approaches and translational relevance.Animal models and experimental medicine · 2026Review
- Application of Artificial Intelligence in Stem Cells and Gene Therapy for Gynecological Cancers.Current stem cell research & therapy · 2026Review
- Preclinical screening models in anticancer drug development: strengths, limitations, and translational challenges.Frontiers in pharmacology · 2026Review
- Preclinical validation of magnetic hyperthermia therapy for locally advanced oral cancer treatment.RSC advances · 2025Article
- Cancer stem cells: landscape, challenges and emerging therapeutic innovations.Signal transduction and targeted therapy · 2025Review
- Current Bioinformatics Tools in Precision Oncology.MedComm · 2025Review
- A breast cancer PDX collection enriched in luminal (ERThe Journal of pathology · 2025Article
- Review
- Review
- The roles of patient-derived xenograft models and artificial intelligence toward precision medicine.MedComm · 2024Review
- New advances in the use of patient-derived organoids and mouse models for assessing heterogeneity among metastatic colorectal cancers.Cell transplantationReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patient-derived xenografts (PDX) involve transplanting patient cells or tissues into immunodeficient mice, offering superior disease models compared with cell line xenografts and genetically engineered mice. In contrast to traditional cell-line xenografts and genetically engineered mice, PDX models harbor the molecular and biologic features from the original patient tumor and are generationally stable. This high fidelity makes PDX models particularly suitable for preclinical and coclinical drug testing, therefore better predicting therapeutic efficacy. Although PDX models are becoming more useful, the several factors influencing their reliability and predictive power are not well understood. Several existing studies have looked into the possibility that PDX models could be important in enhancing our knowledge with regard to tumor genetics, biomarker discovery, and personalized medicine; however, a number of problems still need to be addressed, such as the high cost and time-consuming processes involved, together with the variability in tumor take rates. This review addresses these gaps by detailing the methodologies to generate PDX models, their application in cancer research, and their advantages over other models. Further, it elaborates on how artificial intelligence and machine learning were incorporated into PDX studies to fast-track therapeutic evaluation. This review is an overview of the progress that has been done so far in using PDX models for cancer research and shows their potential to be further improved in improving our understanding of oncogenesis.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.