ReviewACS materials Au2023
3D Printing for Cancer Diagnosis: What Unique Advantages Are Gained?
Review in ACS materials Au, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 16 citations in OpenAlex.
- Exploring the role of 4D printing materials, techniques, and characteristics for personalized oncology.Translational oncology · 2026Review
- The promising applications of 3D printing technology for diagnosis and therapy of cancer: Recent advances and challenges.BioImpacts : BI · 2026Review
- Review
- Exercise therapy: an effective approach to mitigate the risk of cancer metastasis.World journal of surgical oncology · 2025Review
- The Role of 3D Printing in Revolutionizing Pharmaceuticals and Medicine.Mini reviews in medicinal chemistry · 2025Review
- 3D Printing in Biocatalysis and Biosensing: From General Concepts to Practical Applications.Chemistry, an Asian journal · 2024Review
- Advancement in Cancer Vasculogenesis Modeling through 3D Bioprinting Technology.Biomimetics (Basel, Switzerland) · 2024Review
- Applications of 3D Bioprinting Technology to Brain Cells and Brain Tumor Models: Special Emphasis to Glioblastoma.ACS biomaterials science & engineering · 2024Review
- 3D Printed Nanosensors for Cancer Diagnosis: Advances and Future Perspective.Current pharmaceutical design · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is a complex disease with global significance, necessitating continuous advancements in diagnostics and treatment. 3D printing technology has emerged as a revolutionary tool in cancer diagnostics, offering immense potential in detection and monitoring. Traditional diagnostic methods have limitations in providing molecular and genetic tumor information that is crucial for personalized treatment decisions. Biomarkers have become invaluable in cancer diagnostics, but their detection often requires specialized facilities and resources. 3D printing technology enables the fabrication of customized sensor arrays, enhancing the detection of multiple biomarkers specific to different types of cancer. These 3D-printed arrays offer improved sensitivity, allowing the detection of low levels of biomarkers, even in complex samples. Moreover, their specificity can be fine-tuned, reducing false-positive and false-negative results. The streamlined and cost-effective fabrication process of 3D printing makes these sensor arrays accessible, potentially improving cancer diagnostics on a global scale. By harnessing 3D printing, researchers and clinicians can enhance early detection, monitor treatment response, and improve patient outcomes. The integration of 3D printing in cancer diagnostics holds significant promise for the future of personalized cancer care.
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.