Trial reportEuropean journal of nuclear medicine and molecular imaging2023
Application of an artificial intelligence-based tool in [
Trial report in European journal of nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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
20 citing papers in PubMed, 30 citations in OpenAlex.
- Validation of novel low-dose CT methods for quantifying bone marrow in the appendicular skeleton of patients with multiple myeloma: initial results from the [European journal of nuclear medicine and molecular imaging · 2026Trial
- The deep learning radiomics nomogram for risk stratification in multiple myeloma using automatic whole-body [European journal of nuclear medicine and molecular imaging · 2026Article
- Automated Deauville Score computation from baseline [¹⁸F]FDG PET/CT predicts progression-free survival in multiple myeloma: a radiogenomic framework.European journal of nuclear medicine and molecular imaging · 2026Article
- PET in myeloma redefined: a comparative imaging study with FDG and fluorocholine PET/CT.European journal of nuclear medicine and molecular imaging · 2026Article
- AI-Quantified ¹¹C-MET PET/CT bone marrow metabolic activity for prognostic assessment in newly diagnosed multiple myeloma.European journal of nuclear medicine and molecular imaging · 2026Article
- Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [European journal of nuclear medicine and molecular imaging · 2026Article
- Fifteen years of use of functional imaging in multiple myeloma: where we started and where we are going.Blood advances · 2025Review
- Global and regional accuracy of deep learning-based tumor segmentation from whole-body [EJNMMI research · 2025Article
- Organ-based tumor distribution for predicting prognosis in small-cell lung cancer using fluorodeoxyglucose positron emission tomography/computed tomography.Scientific reports · 2025Article
- Splenic T2 signal intensity loss on MRI is associated with disease burden in multiple myeloma.European radiology · 2025Article
- Habitat Analysis in Tumor Imaging: Advancing Precision Medicine Through Radiomic Subregion Segmentation.Cancer management and research · 2025Review
- Recent Breakthroughs in PET-CT Multimodality Imaging: Innovations and Clinical Impact.Bioengineering (Basel, Switzerland) · 2024Review
- Review
- AI predictive modeling of survival outcomes for renal cancer patients undergoing targeted therapy.Scientific reports · 2024Article
- Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [European journal of nuclear medicine and molecular imaging · 2024Article
- The Challenging Approach to Multiple Myeloma: From Disease Diagnosis and Monitoring to Complications Management.Cancers · 2024Review
- Artificial intelligence-based application in multiple myeloma.European journal of nuclear medicine and molecular imaging · 2024Article
- Prognostic value of visual IMPeTUs criteria and metabolic tumor burden at baseline [EJNMMI research · 2024Article
- What is the role of artificial intelligence in general surgery?Ewha medical journal · 2024Review
- Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma.American journal of nuclear medicine and molecular imaging · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose[ MATERIALS AND
methodsWhole-body [
resultsBM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [
conclusionsThe automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts.
Indexed as
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.