prikaz prve stranice dokumenta 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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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

Institucijski repozitorij: Dr Med - digitalni repozitorij Medicinskog fakulteta Sveučilišta u Zagrebu

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Kovačević, L., Štajduhar, A., Stemberger, K., Korša, L., Marušić, Z. i 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, br. 7, 2023. https://doi.org/10.3390/jpm13071150

Kovačević, Lucija, Andrija Štajduhar, Karlo Stemberger, Lea Korša, Zlatko Marušić i 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, br. 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]. 18.07.2023. [pristupljeno 26.11.2024.];13(7). doi: 10.3390/jpm13071150

L. Kovačević, A. Štajduhar, K. Stemberger, L. Korša, Z. Marušić i 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, br. 7, Srpanj 2023. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:105:486279. [Citirano: 26.11.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:105:486279
Podaci o radu
Naslov (engleski)Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study
AutorLucija Kovačević
AutorAndrija Štajduhar
AutorKarlo Stemberger
AutorLea Korša
AutorZlatko Marušić
AutorMaja Prutki
Autorova ustanovaSveučilište 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)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaJournal of Personalized Medicine
Brojčani podacivol. 13, br. 7, 1150
e-ISSN2075-4426
DOIhttps://doi.org/10.3390/jpm13071150
URN:NBNurn:nbn:hr:105:486279
Datum objave publikacije2023-07-18
ProjektŠifra: 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 dokumentahttps://www.mdpi.com/2075-4426/13/7/1150
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2024-09-04 09:41:01