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Multiple comparison correction methods for whole-body magnetic resonance imaging

Breznik, Eva (author)
Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen för visuell information och interaktion
Malmberg, Filip, 1980- (author)
Uppsala universitet,Radiologi
Kullberg, Joel, 1979- (author)
Uppsala universitet,Radiologi
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Ahlström, Håkan, 1953- (author)
Uppsala universitet,Radiologi
Strand, Robin, 1978- (author)
Uppsala universitet,Radiologi,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion
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 (creator_code:org_t)
SPIE-Intl Soc Optical Eng, 2020
2020
English.
In: Journal of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 2329-4302 .- 2329-4310. ; 7:1
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Purpose: Voxel-level hypothesis testing on images suffers from test multiplicity. Numerous correction methods exist, mainly applied and evaluated on neuroimaging and synthetic datasets. However, newly developed approaches like Imiomics, using different data and less common analysis types, also require multiplicity correction for more reliable inference. To handle the multiple comparisons in Imiomics, we aim to evaluate correction methods on whole-body MRI and correlation analyses, and to develop techniques specifically suited for the given analyses. Approach: We evaluate the most common familywise error rate (FWER) limiting procedures on whole-body correlation analyses via standard (synthetic no-activation) nominal error rate estimation as well as smaller prior-knowledge based stringency analysis. Their performance is compared to our anatomy-based method extensions. Results: Results show that nonparametric methods behave better for the given analyses. The proposed prior-knowledge based evaluation shows that the devised extensions including anatomical priors can achieve the same power while keeping the FWER closer to the desired rate. Conclusions: Permutation-based approaches perform adequately and can be used within Imiomics. They can be improved by including information on image structure. We expect such method extensions to become even more relevant with new applications and larger datasets.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)

Keyword

Imiomics
correction methods
multiple comparisons
statistical analysis
whole-body magnetic resonance imaging

Publication and Content Type

ref (subject category)
art (subject category)

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