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Scientific paper - Original scientific paper
Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study
Journal of Personalized Medicine, 13 (2023), 7; 1150. https://doi.org/10.3390/jpm13071150
Kovačević, Lucija; Štajduhar, Andrija; Stemberger, Karlo; Korša, Lea; Marušić, Zlatko; Prutki, Maja

Institutional repository: Dr Med - University of Zagreb School of Medicine Digital Repository

Cite this document

Kovačević, L., Štajduhar, A., Stemberger, K., Korša, L., Marušić, Z. & Prutki, M. (2023). Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study. Journal of Personalized Medicine, 13. (7). doi: 10.3390/jpm13071150

Kovačević, Lucija, et al. "Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study." Journal of Personalized Medicine, vol. 13, no. 7, 2023. https://doi.org/10.3390/jpm13071150

Kovačević, Lucija, Andrija Štajduhar, Karlo Stemberger, Lea Korša, Zlatko Marušić and Maja Prutki. "Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study." Journal of Personalized Medicine 13, no. 7 (2023). https://doi.org/10.3390/jpm13071150

Kovačević, L., et al. (2023) 'Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study', Journal of Personalized Medicine, 13(7). doi: 10.3390/jpm13071150

Kovačević L, Štajduhar A, Stemberger K, Korša L, Marušić Z, Prutki M. Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study. Journal of Personalized Medicine [Internet]. 2023 July 18 [cited 2024 November 05];13(7). doi: 10.3390/jpm13071150

L. Kovačević, A. Štajduhar, K. Stemberger, L. Korša, Z. Marušić and M. Prutki, "Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study", Journal of Personalized Medicine, vol. 13, no. 7, July 2023. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:105:486279. [Accessed: 05 November 2024]

Metadata
Title (english)Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study
AuthorLucija Kovačević
AuthorAndrija Štajduhar
AuthorKarlo Stemberger
AuthorLea Korša
AuthorZlatko Marušić
AuthorMaja Prutki
Author's institutionUniversity of Zagreb
School of Medicine
(Department of Radiology)
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Clinical Medical Sciences
Radiology
Abstract (english)
This study aimed to explore the potential of multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics for classifying breast cancer surrogate subtypes. This retrospective study analyzed 360 breast cancers from 319 patients who underwent pretreatment DCE-MRI between January 2015 and January 2019. The cohort consisted of 33 triple-negative, 26 human epidermal growth factor receptor 2 (HER2)-positive, 109 luminal A-like, 144 luminal B-like HER2-negative, and 48 luminal B-like HER2-positive lesions. A total of 1781 radiomic features were extracted from manually segmented breast cancers in each DCE-MRI sequence. The model was internally validated and selected using ten times repeated five-fold cross-validation on the primary cohort, with further evaluation using a validation cohort. The most successful models were logistic regression models applied to the third post-contrast subtraction images. These models exhibited the highest area under the curve (AUC) for discriminating between luminal A like vs. others (AUC: 0.78), luminal B-like HER2 negative vs. others (AUC: 0.57), luminal B-like HER2 positive vs. others (AUC: 0.60), HER2 positive vs. others (AUC: 0.81), and triple negative vs. others (AUC: 0.83). In conclusion, the radiomic features extracted from multi-phase DCE-MRI are promising for discriminating between breast cancer subtypes. The best-performing models relied on tissue changes observed during the mid-stage of the imaging process.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review
Publication versionPublished version
Journal titleJournal of Personalized Medicine
Numberingvol. 13, no. 7, 1150
e-ISSN2075-4426
DOIhttps://doi.org/10.3390/jpm13071150
URN:NBNurn:nbn:hr:105:486279
Publication2023-07-18
ProjectNumber: IP-2019-04-3684
Title (croatian): Biomedicinsko oslikavanje karcinoma dojke
Title (english): Biomedical imaging of breast carcinoma
Acronym: BOBCAt
Leader: Maja Prutki
Jurisdiction: Croatia
Funding stream: Research Projects
Document URLhttps://www.mdpi.com/2075-4426/13/7/1150
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-09-04 09:41:01