ArticleTranslational oncology2014
Automated tracking of quantitative assessments of tumor burden in clinical trials.
Article in Translational oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper.Frontiers in oncology · 2021Pooled it
- Article
- The Stanford Medicine data science ecosystem for clinical and translational research.JAMIA open · 2023Article
- Discrepant Assessments of Progressive Disease in Clinical Trials between Routine Clinical Reads and Formal RECIST 1.1 Interpretations.Radiology. Imaging cancer · 2023Article
- Article
- Fine-grained spatial information extraction in radiology as two-turn question answering.International journal of medical informatics · 2022Article
- Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Machine and deep learning methods for radiomics.Medical physics · 2020Review
- Interreader Variability in Semantic Annotation of Microvascular Invasion in Hepatocellular Carcinoma on Contrast-enhanced Triphasic CT Images.Radiology. Imaging cancer · 2020Article
- Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies.Journal of digital imaging · 2020Article
- Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup.Journal of digital imaging · 2020Article
- RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning.Journal of digital imaging · 2019Review
- QIN Benchmarks for Clinical Translation of Quantitative Imaging Tools.Tomography (Ann Arbor, Mich.) · 2019Article
- A Web-Based Response-Assessment System for Development and Validation of Imaging Biomarkers in Oncology.Tomography (Ann Arbor, Mich.) · 2019Article
- ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging.Tomography (Ann Arbor, Mich.) · 2019Article
- Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.Cancer · 2018Review
- The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective.International journal of radiation oncology, biology, physics · 2018Review
- Machine learning: from radiomics to discovery and routine.Der Radiologe · 2018Article
- A radiogenomic dataset of non-small cell lung cancer.Scientific data · 2018Article
- Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications.Radiology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
there are two key challenges hindering effective use of quantitative assessment of imaging in cancer response assessment1) Radiologists usually describe the cancer lesions in imaging studies subjectively and sometimes ambiguously, and 2) it is difficult to repurpose imaging data, because lesion measurements are not recorded in a format that permits machine interpretation and interoperability. We have developed a freely available software platform on the basis of open standards, the electronic Physician Annotation Device (ePAD), to tackle these challenges in two ways. First, ePAD facilitates the radiologist in carrying out cancer lesion measurements as part of routine clinical trial image interpretation workflow. Second, ePAD records all image measurements and annotations in a data format that permits repurposing image data for analyses of alternative imaging biomarkers of treatment response. To determine the impact of ePAD on radiologist efficiency in quantitative assessment of imaging studies, a radiologist evaluated computed tomography (CT) imaging studies from 20 subjects having one baseline and three consecutive follow-up imaging studies with and without ePAD. The radiologist made measurements of target lesions in each imaging study using Response Evaluation Criteria in Solid Tumors 1.1 criteria, initially with the aid of ePAD, and then after a 30-day washout period, the exams were reread without ePAD. The mean total time required to review the images and summarize measurements of target lesions was 15% (P < .039) shorter using ePAD than without using this tool. In addition, it was possible to rapidly reanalyze the images to explore lesion cross-sectional area as an alternative imaging biomarker to linear measure. We conclude that ePAD appears promising to potentially improve reader efficiency for quantitative assessment of CT examinations, and it may enable discovery of future novel image-based biomarkers of cancer treatment response.
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.