ReviewJournal of personalized medicine2020
Imaging Biomarkers of Tumour Proliferation and Invasion for Personalised Lung Cancer Therapy.
Review in Journal of personalized medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 15 citations in OpenAlex.
- Role and therapeutic potential of the NEDD4 family in breast cancer.Frontiers in pharmacology · 2025Review
- MiR-29b-3p Inhibits Migration and Invasion of Papillary Thyroid Carcinoma by Downregulating COL1A1 and COL5A1.Frontiers in oncology · 2022Article
- The Role of DACT Family Members in Tumorigenesis and Tumor Progression.International journal of biological sciences · 2022Review
- Comparing DESI-MSI and MALDI-MSI Mediated Spatial Metabolomics and Their Applications in Cancer Studies.Frontiers in oncology · 2022Review
- Personalized Medicine in Oncology; a Special Issue of theJournal of personalized medicine · 2021Article
- PET and SPECT Imaging of the EGFR Family (RTK Class I) in Oncology.International journal of molecular sciences · 2021Review
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 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Personalised treatment in oncology has seen great developments over the last decade, due to both technological advances and more in-depth knowledge of radiobiological processes occurring in tumours. Lung cancer therapy is no exception, as new molecular targets have been identified to further increase treatment specificity and sensitivity. Yet, tumour resistance to treatment is still one of the main reasons for treatment failure. This is due to a number of factors, among which tumour proliferation, the presence of cancer stem cells and the metastatic potential of the primary tumour are key features that require better controlling to further improve cancer management in general, and lung cancer treatment in particular. Imaging biomarkers play a key role in the identification of biological particularities within tumours and therefore are an important component of treatment personalisation in radiotherapy. Imaging techniques such as PET, SPECT, MRI that employ tumour-specific biomarkers already play a critical role in patient stratification towards individualized treatment. The aim of the current paper is to describe the radiobiological challenges of lung cancer treatment in relation to the latest imaging biomarkers that can aid in the identification of hostile cellular features for further treatment adaptation and tailoring to the individual patient's needs.
Indexed as
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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.