Evidence map›Paper›PMID 39285357›Full record

ArticleBMC medical imaging2024

Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound.

Hang-Tong Hu, Ming-De Li, Jian-Chao Zhang, Si-Min Ruan, Shan-Shan Wu, Xin-Xin Lin, Hai-Yu Kang, Xiao-Yan Xie, Ming-De Lu, Ming Kuang and 2 more

Abstract read
In one paragraph

Article in BMC medical imaging, 2024. 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
–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

2 citing papers in PubMed.

  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

12 authors.

Hang-Tong Hu *Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
Ming-De Li *Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
Jian-Chao ZhangPuer People's Hospital, Puer, China.
Si-Min RuanDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
Shan-Shan WuDepartment of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, No. 3025, Shennanzhong Road, Shenzhen, 518033, PR China.
Xin-Xin LinDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
Hai-Yu KangDepartment of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, No. 3025, Shennanzhong Road, Shenzhen, 518033, PR China.
Xiao-Yan XieDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
Ming-De LuDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
Ming KuangDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
Er-Jiao XuDepartment of Medical Ultrasonics, The Eighth Affiliated Hospital of Sun Yat-sen University, No. 3025, Shennanzhong Road, Shenzhen, 518033, PR China. xuerjiao@mail.sysu.edu.cn.
Wei WangDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China. wangw73@mail.sysu.edu.cn.

Funding

Guangzhou Basic and Applied Basic Research Project 2023A04J2231National Natural Science Foundation of China 82102141National Natural Science Foundation of China 82171960Natural Science Foundation of Guangdong Province 2021B1515020054
6 · The paper itself

Abstract

objectivesTo establish a nomogram for differentiating malignant and benign focal liver lesions (FLLs) using ultrasomics features derived from contrast-enhanced ultrasound (CEUS).

methods527 patients were retrospectively enrolled. On the training cohort, ultrasomics features were extracted from CEUS and b-mode ultrasound (BUS). Automatic feature selection and model development were performed using the Ultrasomics-Platform software, outputting the corresponding ultrasomics scores. A nomogram based on the ultrasomics scores from artery phase (AP), portal venous phase (PVP) and delayed phase (DP) of CEUS, and clinical factors were established. On the validation cohort, the diagnostic performance of the nomogram was assessed and compared with seniorexpert and resident radiologists.

resultsIn the training cohort, the AP, PVP and DP scores exhibited better differential performance than BUS score, with area under the curve (AUC) of 84.1-85.1% compared with the BUS (74.6%, P < 0.05). In the validation cohort, the AUC of combined nomogram and expert was significantly higher than that of the resident (91.4% vs. 89.5% vs. 79.3%, P < 0.05). The combined nomogram had a comparable sensitivity with the expert and resident (95.2% vs. 98.4% vs. 97.6%), while the expert had a higher specificity than the nomogram and the resident (80.6% vs. 72.2% vs. 61.1%, P = 0.205).

conclusionsA CEUS ultrasomics based nomogram had an expert level performance in FLL characterization.

Indexed as

Contrast MediaLiver NeoplasmsNomogramsUltrasonographyAdultAgedDiagnosis, DifferentialFemaleHumansLiverMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityContrast MediaContrast-enhanced ultrasoundFocal liver lesionNomogramUltrasomics

Identifiers

PMID39285357
PMCPMC11403768

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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.