High-Throughput 3D Bioprinted Organoids of Skin Cancer Utilized for Diagnosis and Personalized Therapy
Bibliographic record
Abstract
Recent advancements in three-dimensional (3D) bioprinting have revolutionized the modeling of skin cancer, enabling the fabrication of high-throughput, patient-specific organoids that recapitulate the structural, functional, and microenvironmental complexity of native tumors. This review focuses on the integration of cutting-edge bioprinting technologies with bioengineered extracellular matrices and patient-derived cells to generate physiologically relevant skin cancer models for diagnostic and personalized medicine applications. Key technological innovations, including novel bioinks, multi-material printing strategies, and biomimetic approaches, are discussed for their ability to replicate tumor-stroma interactions, vascularization, and immune microenvironments. The utility of bioprinted organoids in high-throughput drug screening, mutation-targeted therapy design, and biomarker discovery is critically evaluated. Additionally, we address current challenges in standardization, reproducibility, and clinical translation, highlighting regulatory and quality-control considerations. Collectively, this review emphasizes the transformative potential of 3D bioprinted skin cancer organoids as platforms for precision oncology, bridging bioengineering advances with translational research to accelerate therapeutic development and personalized treatment strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".