Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study

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• Autor: Lucija Kovačević
• Autor: Andrija Štajduhar
• Autor: Karlo Stemberger
• Autor: Lea Korša
• Autor: Zlatko Marušić
• Autor: Maja Prutki
• Autorova ustanova: Sveučilište u Zagrebu; Medicinski fakultet; (KATEDRA ZA RADIOLOGIJU)
• Znanstveno / umjetničko područje, polje i grana: BIOMEDICINA I ZDRAVSTVO; Kliničke medicinske znanosti; Radiologija

Sažetak (engleski)

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.

• Ključne riječi (engleski): breast cancer biomarkers; machine learning; magnetic resonance imaging; precision medicine; radiomics
• Jezik: engleski
• Vrsta publikacije: Znanstveni rad - Izvorni znanstveni rad
• Status objave: Objavljen
• Vrsta recenzije: Recenziran
• Verzija publikacije: Objavljena verzija rada (izdavačev PDF)
• Naslov časopisa: Journal of Personalized Medicine
• Brojčani podaci: vol. 13, br. 7, 1150
• e-ISSN: 2075-4426
• DOI: https://doi.org/10.3390/jpm13071150
• URN:NBN: urn:nbn:hr:105:486279
• Datum objave publikacije: 2023-07-18
• Projekt: Šifra: IP-2019-04-3684; Naziv (hrvatski): Biomedicinsko oslikavanje karcinoma dojke; Naziv (engleski): Biomedical imaging of breast carcinoma; Kratica: BOBCAt; Voditelj: Maja Prutki; Pravna nadležnost: Hrvatska; Financijer: Hrvatska zaklada za znanost; Linija financiranja: Research Projects
• URL dokumenta: https://www.mdpi.com/2075-4426/13/7/1150
• Vrsta resursa: Tekst
• Prava pristupa: Otvoreni pristup
• Datum i vrijeme pohrane: 2024-09-04 09:41:01