Evidence map›Paper›PMID 37255968›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2023

Estimating normal metabolic activity for disease quantification via PET/CT images.

Jieyu Li, Jayaram K Udupa, Yubing Tong, Drew A Torigian

Abstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2023. 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. Prediction of Disease Quantification via PET/CT Images and Auto-Segmentation of Target Objects.Proceedings of SPIE--the International Society for Optical Engineering · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jieyu LiDepartment of Automation, Tsinghua University.
Jayaram K UdupaMedical Image Processing Group, Department of Radiology, University of Pennsylvania.
Yubing TongMedical Image Processing Group, Department of Radiology, University of Pennsylvania.
Drew A TorigianMedical Image Processing Group, Department of Radiology, University of Pennsylvania.

Funding

Models and methods for automatically measuring disease body-wide and staging disease via FDG-PET/CT in LymphomaR01CA255748 · NCI · UNIVERSITY OF PENNSYLVANIA · PI SCHUSTER, STEPHEN J, TORIGIAN, DREW · 2021 to 2024
$2.3M
NCI NIH HHS R01 CA255748
6 · The paper itself

Abstract

In this paper, we propose a novel pipeline for conducting disease quantification in positron emission tomography/computed tomography (PET/CT) images on anatomically pre-defined objects. The pipeline is composed of standardized uptake value (SUV) standardization, object segmentation, and disease quantification (DQ). DQ is conducted on non-linearly standardized PET images and masks of target objects derived from CT images. Total lesion burden (TLB) is quantified by estimating normal metabolic activity (TMA

Indexed as

disease quantificationmetabolic activityPET/CTstandardized uptake valuetotal lesion burden

Identifiers

PMID37255968
PMCPMC10228705

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.