ReviewJournal of digital imaging2018
Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.
Review in Journal of digital imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses 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
31 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Application of radiomics for diagnosis, subtyping, and prognostication of medulloblastomas: a systematic review.Neurosurgical review · 2024Pooled it
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
- A Systematic Review of Three-Dimensional Printing in Liver Disease.Journal of digital imaging · 2018Pooled it
- Machine learning based diagnostics of veterinary cancer on ultrasound and optical imaging data.The veterinary quarterly · 2025Article
- Differentiation of canine and feline neoplasms using multi-modal imaging and machine learning.Scientific reports · 2025Article
- AI-ready rectal cancer MR imaging: a workflow for tumor detection and segmentation.BMC medical imaging · 2025Article
- Artificial Intelligence in Pancreatic Image Analysis: A Review.Sensors (Basel, Switzerland) · 2024Review
- QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research.European radiology experimental · 2023Article
- Machine learning approach to differentiation of peripheral schwannomas and neurofibromas: A multi-center study.Neuro-oncology · 2022Article
- Artificial intelligence and machine learning in cancer imaging.Communications medicine · 2022Review
- Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.Frontiers in oncology · 2022Review
- Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis.Journal of digital imaging · 2021Article
- Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma.AJNR. American journal of neuroradiology · 2021Article
- Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Radiogenomics of gastroenterological cancer: The dawn of personalized medicine with artificial intelligence-based image analysis.Annals of gastroenterological surgery · 2021Review
- Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet.Sensors (Basel, Switzerland) · 2021Article
- Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform.European radiology · 2020Article
- Diagnostics of Melanocytic Skin Tumours by a Combination of Ultrasonic, Dermatoscopic and Spectrophotometric Image Parameters.Diagnostics (Basel, Switzerland) · 2020Article
- Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets.Tomography (Ann Arbor, Mich.) · 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
4 authors.
Funding
Abstract
The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.
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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.