Atıf İçin Kopyala
KAYADİBİ Y., Kocak B., Ucar N., Akan Y. N., Yildirim E., Bektas S.
ACADEMIC RADIOLOGY, cilt.29, 2022 (SCI-Expanded)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
29
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Basım Tarihi:
2022
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Doi Numarası:
10.1016/j.acra.2021.10.026
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Dergi Adı:
ACADEMIC RADIOLOGY
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Derginin Tarandığı İndeksler:
Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE
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Anahtar Kelimeler:
Breast cancer, Lymphovascular invasion, Machine learning, Radiomics, Texture analysis, PREOPERATIVE PREDICTION, LOCOREGIONAL RECURRENCE, PROGNOSTIC-FACTORS, FEATURES, THERAPY, RADIOGENOMICS, HETEROGENEITY, COEFFICIENTS, CARCINOMA, NUMBER
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İstanbul Üniversitesi Adresli:
Evet
Özet
Rationale and Objectives: In patients with breast cancer (BC), lymphovascular invasion (LVI) status is considered an important prognostic factor. We aimed to develop machine learning (ML)-based radiomics models for the prediction of LVI status in patients with BC, using preoperative MRI images.