ArticleEJNMMI physics2025
Interobserver ground-truth variability limits performance of automated glioblastoma segmentation on [
Article in EJNMMI physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Multimodal PET-MR segmentation for glioblastoma: complementarity for treatment planning and recurrence definition.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- First human whole-body biodistribution and dosimetry analysis of [European journal of nuclear medicine and molecular imaging · 2026Article
- Fully automated volumetry of ventricular subregions on computed tomography using object detection and semantic segmentation.Neuroimage. Reports · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundPositron emission tomography (PET) with a [
resultsThe proposed two-channel network shows increased performance with guidance of threshold maps originating from the same reader whose ground-truth tumor label the prediction is compared to (DSC = 0.901). When threshold maps were generated by a different reader, performance reverted to levels comparable to the one-channel network and inter-reader variability. The proposed full pipeline achieves results on par with current state of the art (DSC = 0.807).
conclusionsIncorporating a threshold map can significantly improve tumor segmentation performance when it aligns well with the ground-truth label. However, the current inability to reliably reproduce these maps-both manually and automatically-or the ground-truth tumor labels, restricts the achievable accuracy for automated glioblastoma segmentation on [
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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 Socratic graph.