ReviewMagnetic resonance imaging2012
Promise and pitfalls of quantitative imaging in oncology clinical trials.
Review in Magnetic resonance imaging, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 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
48 citing papers in PubMed, 106 citations in OpenAlex.
- Real-Time Quantitative Assessment of Accuracy and Precision of Blood Volume Derived from DCE-MRI in Individual Patients During a Clinical Trial.Tomography (Ann Arbor, Mich.) · 2019Trial
- A novel radiomics model combining GTVp, GTVnd, and clinical data for chemoradiotherapy response prediction in patients with advanced NSCLC.Frontiers in medicine · 2025Article
- Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning.Heliyon · 2024Article
- Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model.BMC cancer · 2024Article
- Review
- A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology.Tomography (Ann Arbor, Mich.) · 2023Article
- Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions.Scientific reports · 2023Article
- Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach.Molecular imaging and biology · 2023Article
- BlenderPhotonics: an integrated open-source software environment for three-dimensional meshing and photon simulations in complex tissues.Journal of biomedical optics · 2022Article
- A review on advances inInsights into imaging · 2022Review
- Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.Frontiers in oncology · 2022Article
- Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma.Radiology · 2021Article
- Interpretable Diagnosis for Whole-Slide Melanoma Histology Images Using Convolutional Neural Network.Journal of healthcare engineering · 2021Article
- Derivation and Validation of a Prognostic Scoring Model Based on Clinical and Pathological Features for Risk Stratification in Oral Squamous Cell Carcinoma Patients: A Retrospective Multicenter Study.Frontiers in oncology · 2021Article
- Prognostic Value of Dual-Time-PointDiagnostics (Basel, Switzerland) · 2020Article
- Clinical Trial Design and Development Work Group Within the Quantitative Imaging Network.Tomography (Ann Arbor, Mich.) · 2020Review
- Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network.IEEE transactions on medical imaging · 2020Article
- Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects.Magnetic resonance in medicine · 2020Article
- NCTN Assessment on Current Applications of Radiomics in Oncology.International journal of radiation oncology, biology, physics · 2019Review
- Gastric cancer and imaging biomarkers: Part 1 - a critical review of DW-MRI and CE-MDCT findings.European radiology · 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
13 authors at 9 institutions in 1 country.
Funding
Abstract
Quantitative imaging using computed tomography, magnetic resonance imaging and positron emission tomography modalities will play an increasingly important role in the design of oncology trials addressing molecularly targeted, personalized therapies. The advent of molecularly targeted therapies, exemplified by antiangiogenic drugs, creates new complexities in the assessment of response. The Quantitative Imaging Network addresses the need for imaging modalities which can accurately and reproducibly measure not just change in tumor size but changes in relevant metabolic parameters, modulation of relevant signaling pathways, drug delivery to tumor and differentiation of apoptotic cell death from other changes in tumor volume. This article provides an overview of the applications of quantitative imaging to phase 0 through phase 3 oncology trials. We describe the use of a range of quantitative imaging modalities in specific tumor types including malignant gliomas, lung cancer, head and neck cancer, lymphoma, breast cancer, prostate cancer and sarcoma. In the concluding section, we discuss potential constraints on clinical trials using quantitative imaging, including complexity of trial conduct, impact on subject recruitment, incremental costs and institutional barriers. Strategies for overcoming these constraints are presented.
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.