ReviewInternational journal of radiation oncology, biology, physics2018
The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective.
Review in International journal of radiation oncology, biology, physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed.
- Impact of qualitative Diffusion weighted imaging and apparent diffusion coefficient (DWI/ADC) on target volume delineation in cervical cancer: a feasibility study.Technical innovations & patient support in radiation oncology · 2026Article
- Article
- Diagnostic, Therapeutic, and Prognostic Applications of Artificial Intelligence (AI) in the Clinical Management of Brain Metastases (BMs).Brain sciences · 2025Review
- Multi-institution longitudinal apparent diffusion coefficient measurements in a diffusion weighted imaging phantom at room temperature.Physics and imaging in radiation oncology · 2025Article
- Multi-center and multi-vendor evaluation study across 1.5 T and 3 T scanners (part 2): T1 and T2 standardization in the ISMRM/NIST MR phantom.Magma (New York, N.Y.) · 2025Article
- Artificial intelligence integrates multi-omics data for precision stratification and drug resistance prediction in breast cancer.Frontiers in oncology · 2025Review
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- Treatment Planning Methods for Dose Painting by Numbers Treatment in Gamma Knife Radiosurgery.Advances in radiation oncology · 2024Article
- Validation of quantitative magnetic resonance imaging techniques in head and neck healthy structures involved in the salivary and swallowing function: Accuracy and repeatability.Physics and imaging in radiation oncology · 2024Article
- Development of a CT-based radiomics-clinical model to diagnose acute pancreatitis on nonobvious findings on CT in children with pancreaticobiliary maljunction.The British journal of radiology · 2024Article
- An Update on MR Spectroscopy in Cancer Management: Advances in Instrumentation, Acquisition, and Analysis.Radiology. Imaging cancer · 2024Review
- Harnessing progress in radiotherapy for global cancer control.Nature cancer · 2023Review
- Status of Government-Funded Radiotherapy Services in Nigeria.JCO global oncology · 2023Article
- Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases.Frontiers in oncology · 2023Article
- Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer.Translational lung cancer research · 2022Article
- The Potential of Photoacoustic Imaging in Radiation Oncology.Frontiers in oncology · 2022Article
- Medical imaging and nuclear medicine: a Lancet Oncology Commission.The Lancet. Oncology · 2021Review
- Review
- Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment.Frontiers in oncology · 2021Review
- Biology-guided radiotherapy: redefining the role of radiotherapy in metastatic cancer.The British journal of radiology · 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
26 authors.
Funding
Abstract
Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.
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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.