ReviewCurrent cancer drug targets2025
A Review on Patient-derived 3D Micro Cancer Approach for Drug Screen in Personalized Cancer Medicine.
Review in Current cancer drug targets, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed, 4 citations in OpenAlex.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision medicine in oncology aims to identify an individualized treatment plan based on genomic alterations in a patient's tumor. It helps to select the most beneficial therapy for an individual patient. As it is now known that no patient's cancer is the same, and therefore, different patients may respond differently to conventional treatments, precision medicine, which replaces the one-size-fits-all approach, supports the development of tailored treatments for specific cancers of different patients. Patient-specific organoid or spheroid models as 3D cell culture models are very promising for predicting resistance to anti-cancer drugs and for identifying the most effective cancer therapy for high-throughput drug screening combined with genomic analysis in personalized medicine. Because tumor spheroids incorporate many features of solid tumors and reflect resistance to drugs and radiation, as in human cancers, they are widely used in drug screening studies. Testing patient-derived 3D cancer spheroids with some anticancer drugs based on information from molecular profiling can reveal the sensitivity of tumor cells to drugs and provide the right compounds to be effective against resistant cells. Given that many patients do not respond to standard treatments, patient-specific treatments will be more effective, less toxic. They will affect survival better compared to the standard approach used for all patients.
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.