Evidence map›Paper›PMID 36723705›Full record

ArticleAnnals of nuclear medicine2023

PET-guided attention for prediction of microvascular invasion in preoperative hepatocellular carcinoma on PET/CT.

Zhengjie Ye, Jian Zhang, Nan Wu, Song Chen, Yutao Wang, Zhongfei Yu, Wei Jin

Abstract read
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In one paragraph

Article in Annals of nuclear medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 3 pooled it
1.1field-weighted citation impact, top 23% 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

5 citing papers in PubMed, 3 syntheses or guidelines pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Review
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.

Zhengjie Ye *Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China.
Jian Zhang *Shanghai University, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, 201103, China. abiaoxp@qq.com.
Nan WuFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China.
Song ChenFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China.
Yutao WangAffiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China.
Zhongfei YuShanghai University, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, 201103, China.
Wei JinFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China. xyjw1969@126.com.
Ningbo University · CNShanghai University of Medicine and Health Sciences · CN

Funding

Natural Science Foundation of Zhejiang Province No.LY20H180003Science and Technology Innovation 2025 Major Project of Ningbo No.2022S043Youth Key Health Talents Project of Ningbo No.2020SWSQNGG-06
6 · The paper itself

Abstract

purposeTo achieve PET/CT-based preoperative prediction of microvascular invasion in hepatocellular carcinoma by combining the advantages of PET and CT.

methodsThis retrospective study included a total of 100 patients from two institutions who underwent PET/CT imaging. The above patients were divided into a training cohort (n = 70) and a validation cohort (n = 30). This study was based on PET/CT images to evaluate the possibility of microvascular invasion (MVI) of patients. In this study, we proposed a two-branch PET-guided attention network to predict MVI. The model used a two-branch network to extract image features from PET and CT, respectively. The PET-guided attention module aimed to enable the model to focus on the lesion region and reduce the disturbance of irrelevant and redundant information. Model performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.

resultsThe method outperformed the single-modality prediction model for preoperative hepatocyte microvascular invasion, achieving an AUC of 0.907. On the validation set, accuracy reached 0.846, precision reached 0.881, recall 0.793, and F1-score 0.835.

conclusionThe model exploits the particularities of the molecular metabolic function of PET and the anatomical structure of CT and can strongly improve the accuracy of clinical diagnosis of MVI.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsHumansNeoplasm InvasivenessPositron Emission Tomography Computed TomographyRetrospective StudiesROC Curve18-FDG PET/CTAttentionDeep learningHepatocellular carcinomaMicrovascular invasion

Identifiers

PMID36723705
OpenAlexW4318754538

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.