ReviewPharmaceutics2019
Computational Approaches in Theranostics: Mining and Predicting Cancer Data.
Review in Pharmaceutics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Harnessing the Power of Nanocarriers to Exploit the Tumor Microenvironment for Enhanced Cancer Therapy.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Deciphering molecular pathways driving cancer invasion and metastasis: advances and therapeutic prospects.Frontiers in oncology · 2025Review
- Pain Management in Cancer Patients With Artificial Intelligence: Narrative Review.Scientifica · 2025Review
- Circulating Tumor Cells in Cancer Diagnosis, Therapy, and Theranostics Applications: An Overview of Emerging Materials and Technologies.Current pharmaceutical design · 2025Review
- Knockout cancer by nano-delivered immunotherapy using perfusion-aided scaffold-based tumor-on-a-chip.Nanotheranostics · 2024Review
- Spatiotemporal dissection of tumor microenvironment via in situ sensing and monitoring in tumor-on-a-chip.Biosensors & bioelectronics · 2023Article
- Advanced Computational Methodologies Used in the Discovery of New Natural Anticancer Compounds.Frontiers in pharmacology · 2021Review
- Nanomedicine of tyrosine kinase inhibitors.Theranostics · 2021Review
- Tumor microenvironment complexity and therapeutic implications at a glance.Cell communication and signaling : CCS · 2020Review
- The effect of ligand amount, affinity and internalization on PSMA-targeted imaging and therapy: A simulation study using a PBPK model.Scientific reports · 2019Article
- Artificial intelligence and big data facilitated targeted drug discovery.Stroke and vascular neurology · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.