Evidence map›Paper›PMID 40742503›Full record

ArticleEJNMMI research2025

Advanced quantification pipeline reveals new spatial and temporal tumor characteristics in preclinical multiple myeloma.

Zhixin Sun, Jacqueline M Godbe, Alexander Zheleznyak, Brad Manion, Junhao Hu, Julie L Prior, Kathleen Duncan, Ulugbek S Kamilov, Monica Shokeen

Abstract read
In one paragraph

Article in EJNMMI research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Zhixin SunDepartment of Electrical and Systems Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA.ORCID http://orcid.org/0000-0001-9701-9362
Jacqueline M GodbeEdward Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.ORCID http://orcid.org/0000-0002-8769-2083
Alexander ZheleznyakEdward Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.ORCID http://orcid.org/0000-0002-1054-3768
Brad ManionEdward Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.ORCID http://orcid.org/0000-0002-4215-0782
Junhao HuDepartment of Electrical and Systems Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA.
Julie L PriorEdward Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.ORCID http://orcid.org/0009-0009-0292-1552
Kathleen DuncanEdward Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.ORCID http://orcid.org/0000-0001-8767-8372
Ulugbek S KamilovDepartment of Electrical and Systems Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA. kamilov@wustl.edu.ORCID http://orcid.org/0000-0001-6770-3278
Monica ShokeenEdward Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA. shokeenm@wustl.edu.ORCID http://orcid.org/0000-0001-8114-9584

Funding

Washington University Center for Cellular ImagingP30CA091842 · NCI · WASHINGTON UNIVERSITY · PI TIMOTHY J. EBERLEIN · 2001 to 2026
$128.0M
Exploring CD38 molecular biology and imaging in multiple myeloma pathogenesisR01CA248493 · NCI · WASHINGTON UNIVERSITY · PI SHOKEEN, MONICA · 2020 to 2024
$3.1M
Training OPportunities in Translational Imaging Education and Research (TOP-TIER)T32EB021955 · NIBIB · WASHINGTON UNIVERSITY · PI Hongyu An, Joseph Edward Ippolito · 2017 to 2026
$2.4M
Acquisition of high-resolution PET/CT preclinical imaging instrument at MIRS10OD030403 · OD · WASHINGTON UNIVERSITY · PI SHOGHI, KOORESH ISAAC · 2021 to 2021
$929k
IVIS Spectrum CT Preclinical In Vivo Imaging SystemS10OD027042 · OD · WASHINGTON UNIVERSITY · PI ACHILEFU, SAMUEL · 2019 to 2019
$599k
Multispectral Optoacoustic Tomography (MSOT) SystemS10OD025264 · OD · WASHINGTON UNIVERSITY · PI ACHILEFU, SAMUEL · 2018 to 2018
$544k
CSR NIH HHS R01CA248493NCI NIH HHS P30 CA091842NCI NIH HHS R01 CA248493NIBIB NIH HHS T32 EB021955NIH HHS S10 OD025264NIH HHS S10 OD027042NIH HHS S10 OD030403
6 · The paper itself

Abstract

backgroundRadiological imaging plays an indispensable role in both preclinical and clinical studies of multiple myeloma (MM). However, manual quantification in longitudinal small animal PET/CT is limited by annotator bias, signal artifacts from urinary/fecal excretion, and voxel misalignment due to non-rigid registration. To address these challenges and improve characterization of tumor biology, we developed a semi-automated PET/CT quantification pipeline targeting defined regions of interest (ROIs) within the bone marrow-rich mouse skeleton, achieving sub-organ spatial resolution, including in anatomically complex sites such as the pelvis. We applied this MM-specific preclinical pipeline to analyze tumor distribution in a longitudinal molecular PET study using an immunocompetent mouse model of skeletally disseminated MM. An Attention U-Net was trained to segment the thoracolumbar spine, pelvis and pelvic joints, sacrum, and femurs from 2D CT slices. A custom algorithm masked spillover signal from physiological excretion, and a PCA-based projection was used to map tumor distribution along the skeletal axis. Quantification metrics included mean and maximum standardized uptake values (SUV

resultsTumor burden localized preferentially to skeletal regions near joints. Using precise CT-based alignment (DICE = 0.966 ± 0.005), we detected early disease progression and aggressive phenotypes. A marked increase in tumor uptake was observed by day 18 post-implantation, with significant SUV

conclusionsThis pipeline enables reproducible, anatomically precise quantification of region-specific trends in MM progression, including joint-specific lesion tropism and sex-based differences, from longitudinal PET/CT scans. By mitigating common challenges such as excretion artifacts and inconsistent mouse positioning, our approach overcomes limitations of manual analysis and enhances evaluation of tumor biology and treatment response in preclinical models of bone-involved cancers.

Indexed as

Bone segmentationMultiple myeloma (MM) imagingPET/CT quantificationSkeletal lesions

Identifiers

PMID40742503
PMCPMC12314142

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Registered trials

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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.