ArticleTomography (Ann Arbor, Mich.)2019
QIN Benchmarks for Clinical Translation of Quantitative Imaging Tools.
Article in Tomography (Ann Arbor, Mich.), 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.
- AI-BLADE toolbox: AI-powered BLADdEr multiparametric MRI analysis for clinical application.BJR artificial intelligence · 2026Article
- A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology.Tomography (Ann Arbor, Mich.) · 2023Article
- A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation.Tomography (Ann Arbor, Mich.) · 2023Review
- Post-acquisition standardization of positron emission tomography images.Frontiers in nuclear medicine · 2023Article
- In vivo tumor immune microenvironment phenotypes correlate with inflammation and vasculature to predict immunotherapy response.Nature communications · 2022Article
- Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma.Academic radiology · 2021Article
- Comparison of Segmentation Methods in Assessing Background Parenchymal Enhancement as a Biomarker for Response to Neoadjuvant Therapy.Tomography (Ann Arbor, Mich.) · 2020Article
- Quantitative Imaging Enters the Clinical Arena: A Personal Viewpoint.Tomography (Ann Arbor, Mich.) · 2020Article
- Repeatability and Reproducibility of ADC Histogram Metrics from the ACRIN 6698 Breast Cancer Therapy Response Trial.Tomography (Ann Arbor, Mich.) · 2020Article
- Clinical Trial Design and Development Work Group Within the Quantitative Imaging Network.Tomography (Ann Arbor, Mich.) · 2020Review
- Quantitative Imaging Informatics for Cancer Research.JCO clinical cancer informatics · 2020Article
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
The Quantitative Imaging Network of the National Cancer Institute is in its 10th year of operation, and research teams within the network are developing and validating clinical decision support software tools to measure or predict the response of cancers to various therapies. As projects progress from development activities to validation of quantitative imaging tools and methods, it is important to evaluate the performance and clinical readiness of the tools before committing them to prospective clinical trials. A variety of tests, including special challenges and tool benchmarking, have been instituted within the network to prepare the quantitative imaging tools for service in clinical trials. This article highlights the benchmarking process and provides a current evaluation of several tools in their transition from development to validation.
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.