ReviewMolecular oncology2015
Integrative analysis of cancer imaging readouts by networks.
Review in Molecular oncology, 2015. 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.
- Chitosan Nanoparticles: Approaches to Preparation, Key Properties, Drug Delivery Systems, and Developments in Therapeutic Efficacy.AAPS PharmSciTech · 2025Review
- From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health.Journal of personalized medicine · 2020Review
- Current Advances in Chitosan Nanoparticles Based Drug Delivery and Targeting.Advanced pharmaceutical bulletin · 2019Review
- Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy.Frontiers in medicine · 2019Article
- Understanding cancer complexome using networks, spectral graph theory and multilayer framework.Scientific reports · 2017Article
- Expected Impacts of Connected Multimodal Imaging in Precision Oncology.Frontiers in pharmacology · 2016Article
- Immunomediated Pan-cancer Regulation Networks are Dominant Fingerprints After Treatment of Cell Lines with Demethylation.Cancer informatics · 2016Article
- Article
- Integrative analysis of cancer imaging readouts by networks.Molecular oncology · 2015Review
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
No grant is acknowledged in the PubMed record.
Abstract
Cancer is a multifactorial and heterogeneous disease. The corresponding complexity appears at multiple levels: from the molecular and the cellular constitution to the macroscopic phenotype, and at the diagnostic and therapeutic management stages. The overall complexity can be approximated to a certain extent, e.g. characterized by a set of quantitative phenotypic observables recorded in time-space resolved dimensions by using multimodal imaging approaches. The transition from measures to data can be made effective through various computational inference methods, including networks, which are inherently capable of mapping variables and data to node- and/or edge-valued topological properties, dynamic modularity configurations, and functional motifs. We illustrate how networks can integrate imaging data to explain cancer complexity, and assess potential pre-clinical and clinical impact.
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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.