Evidence map›Paper›PMID 36105450›Full record

ArticleContrast media & molecular imaging2022

Evaluation of Short-Term Efficacy of PD-1 Monoclonal Antibody Immunotherapy for Lymphoma by Positron Emission Tomography/Computed Tomography Imaging with Convolutional Neural Network Image Registration Algorithm.

Jie Fang, Zhendong Chen

Open access · hybridAbstract read
In one paragraph

Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 37% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 1 institution in 1 country.

Jie FangDepartment of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, Anhui, China.
Zhendong ChenDepartment of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, Anhui, China.ORCID 0000-0001-7481-3985
Anhui Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The objective of this study was to investigate the value of PET/CT imaging based on CNN image registration algorithm in evaluating the short-term efficacy of PD-1 monoclonal antibody immunotherapy for lymphoma. 36 patients with advanced lymphoma confirmed by histology or cytochemistry and treated with PD-1 monoclonal antibody admitted to hospital were included. In addition, 38 normal controls were from healthy volunteers. All patients were treated with PD-1 monoclonal antibody intravenous infusion, and the image data were processed by CT with intelligent segmentation algorithm. Medication method: nivolumab 3 mg/kg for 2 weeks; pembrolizumab 2 mg/kg for 3 weeks; and continuous medication until tumor progression or unacceptable adverse reactions. Efficacy was evaluated every 3 cycles. Imaging examinations were performed after 3 weeks of medication to evaluate the therapeutic effect. The concentrations of IL-2, IL-7, basic fibroblast growth factor (FGF), granulocyte-macrophage colony-stimulating factor (GM-CSF), platelet-derived growth factor (PDGF-bb), and other factors in the normal control group were significantly higher than those in the advanced lymphoma patients, and the differences were statistically significant (

Indexed as

LymphomaPositron Emission Tomography Computed TomographyAlgorithmsAntibodies, MonoclonalHumansImmunotherapyNeural Networks, ComputerPositron-Emission TomographyProgrammed Cell Death 1 ReceptorTomography, X-Ray ComputedAntibodies, MonoclonalProgrammed Cell Death 1 Receptor

Identifiers

PMID36105450
PMCPMC9452982
OpenAlexW4293919050

What OpenQuestion holds

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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.