Evidence map›Paper›PMID 37518678›Full record

ArticleEuropean radiology2024

Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study.

Yu Du, Mengjun Cai, Hailing Zha, Baoding Chen, Jun Gu, Manqi Zhang, Wei Liu, Xinpei Liu, Xiaoan Liu, Min Zong and 1 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
8.3field-weighted citation impact, top 2% 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

19 citing papers in PubMed, 36 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. 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

11 authors at 3 institutions in 1 country.

Yu Du *Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
Mengjun Cai *Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
Hailing ZhaDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
Baoding ChenDepartment of Ultrasound, Affiliated Hospital of Jiangsu University, 438 Jiefang Road, Zhenjiang, 212050, China.
Jun GuDepartment of Ultrasound, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, 215002, China.
Manqi ZhangDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
Wei LiuDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
Xinpei LiuDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
Xiaoan LiuDepartment of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
Min ZongDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China. mzong@njmu.edu.cn.
Cuiying LiDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China. lcy_njmu@163.com.ORCID http://orcid.org/0000-0002-8633-791X
Jiangsu Province Hospital · CNJiangsu University · CNSuzhou Municipal Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo develop and validate an ultrasound (US) radiomics-based nomogram for the preoperative prediction of the lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC). MATERIALS AND

methodsIn this multicentre, retrospective study, 456 consecutive women were enrolled from three institutions. Institutions 1 and 2 were used to train (n = 320) and test (n = 136), and 130 patients from institution 3 were used for external validation. Radiomics features that reflected tumour information were derived from grey-scale US images. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy (mRMR) algorithm were used for feature selection and radiomics signature (RS) building. US radiomics-based nomogram was constructed by using multivariable logistic regression analysis. Predictive performance was assessed with the receiving operating characteristic curve, discrimination, and calibration.

resultsThe nomogram based on clinico-ultrasonic features (menopausal status, US-reported lymph node status, posterior echo features) and RS yielded an optimal AUC of 0.88 (95% confidence interval [CI], 0.84-0.91), 0.89 (95% CI, 0.84-0.94) and 0.95 (95% CI, 0.92-0.99) in the training, internal and external validation cohort. The nomogram outperformed the clinico-ultrasonic and RS model (p < 0.05). The nomogram performed favourable discrimination (C-index, 0.88; 95% CI: 0.84-0.91) and was confirmed in the validation (0.88 for internal, 0.95 for external) cohorts. The calibration and decision curve demonstrated the nomogram showed good calibration and was clinically useful.

conclusionsThe radiomics nomogram incorporated in the RS and US and the clinical findings exhibited favourable preoperative individualised prediction of LVI. CLINICAL RELEVANCE STATEMENT: The US radiomics-based nomogram incorporating menopausal status, posterior echo features, US reported-ALN status, and radiomics signature has the potential to predict lymphovascular invasion in patients with invasive breast cancer. KEY POINTS: • The clinico-ultrsonic model of menopausal status, posterior echo features, and US-reported ALN status achieved a better predictive efficacy for LVI than either of them alone. • The radiomics nomogram showed optimal prediction in predicting LVI from patients with IBC (ROC, 0.88 and 0.89 in the training and validation sets). • A nomogram demonstrated favourable performance (area under the receiver operating characteristic curve, 0.95) and well calibration (C-index, 0.95) in an independent validation cohort (n = 130).

Indexed as

Breast NeoplasmsFemaleHumansNomogramsRadiomicsRetrospective StudiesUltrasonographyBreast neoplasmsLymphovascular invasionNomogramRadiomicsUltrasonography

Identifiers

PMID37518678
OpenAlexW4385406883

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.