Machine Learning Based on Multi-Parametric MRI to Predict Risk of Breast Cancer.
breast cancer
machine learning
multi-parametric MRI
nomogram
risk prediction
Journal
Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867
Informations de publication
Date de publication:
2021
2021
Historique:
received:
08
06
2020
accepted:
18
01
2021
entrez:
15
3
2021
pubmed:
16
3
2021
medline:
16
3
2021
Statut:
epublish
Résumé
Machine learning (ML) can extract high-throughput features of images to predict disease. This study aimed to develop nomogram of multi-parametric MRI (mpMRI) ML model to predict the risk of breast cancer. The mpMRI included non-enhanced and enhanced T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), This study involved 144 malignant lesions and 66 benign lesions. The average age of patients with benign and malignant lesions was 42.5 years old and 50.8 years old, respectively, which were statistically different. The sixth and fourth principal components of Nomogram could improve the ability of breast cancer prediction preoperatively.
Identifiants
pubmed: 33718131
doi: 10.3389/fonc.2021.570747
pmc: PMC7952867
doi:
Types de publication
Journal Article
Langues
eng
Pagination
570747Informations de copyright
Copyright © 2021 Tao, Lu, Zhou, Montemezzi, Bai, Yue, Li, Zhao, Zhou and Lu.
Déclaration de conflit d'intérêts
YY, XL, and LZ were employed by the company of Deepwise. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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