ArticleStatistics in medicine2021
Spatial process decomposition for quantitative imaging biomarkers using multiple images of varying shapes.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies.Frontiers in oncology · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Quantitative imaging biomarkers (QIB) are extracted from medical images in radiomics for a variety of purposes including noninvasive disease detection, cancer monitoring, and precision medicine. The existing methods for QIB extraction tend to be ad hoc and not reproducible. In this article, a general and flexible statistical approach is proposed for handling up to three-dimensional medical images and reasonably capturing features with respect to specific spatial patterns. In particular, a model-based spatial process decomposition is developed where the random weights are unique to individual patients for component functions common across patients. Model fitting and selection are based on maximum likelihood, while feature extractions are via optimal prediction of the underlying true image. Simulation studies are conducted to investigate the properties of the proposed methodology. For illustration, a cancer image data set is analyzed and QIBs are extracted in association with a clinical endpoint.
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Registered trials
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