Evidence map›Paper›PMID 35111608›Full record

ArticleQuantitative imaging in medicine and surgery2022

Diagnostic performance of a novel high-resolution dedicated axillary PET system in the assessment of regional nodal spread of disease in early breast cancer.

Jingyi Cheng, Junjie Li, Guangyu Liu, Ruohong Shui, Sheng Chen, Benlong Yang, Zhimin Shao

Open access · diamondAbstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 49% of its field
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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 2 institutions in 1 country.

Jingyi Cheng *Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.
Junjie Li *Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Guangyu Liu *Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Ruohong ShuiDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Sheng ChenDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Benlong YangDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Zhimin ShaoDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Fudan University Shanghai Cancer Center · CNShanghai Medical College of Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn early breast cancer, a non-invasive method with higher sensitivity and negative predictive value (NPV) is needed to identify and recognize more indolent axillary lymph nodes (ALNs). This study aimed to assess whether a novel high-resolution dedicated ALN positron emission tomography (LymphPET) system could improve sensitivity in detecting early breast cancer (clinical N0-N1 stage).

methodsA total of 103 patients with clinical stage T1-2N0-1M0 breast cancer were evaluated by

resultsA total of 99 invasive breast carcinoma cases were included in this study. The diagnostic sensitivity of LymphPET was 88%, specificity was 79%, false-negative rate was 12%, the false-positive rate was 21%, positive predictive value was 75%, NPV was 90%, and accuracy was 83%. The maxLUV was superior to TBR1 and TBR2 in detecting ALNs, with 0.27 being the most optimal cutoff value.

conclusionsThe

Indexed as

Lymph node assessmentmolecular imagingnew PET technology

Identifiers

PMID35111608
PMCPMC8739142
OpenAlexW3205907246

What OpenQuestion holds

Textmetadata
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.