MRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status.

Kayadibi, YASEMİN; Kocak, Burak; Ucar, Nese; Akan, Yesim; Yildirim, Emine; Bektas, Sibel

doi:10.1016/j.acra.2021.10.026

MRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status.

Kayadibi Y., Kocak B., Ucar N., Akan Y. N., Yildirim E., Bektas S.

Academic radiology, cilt.29, 2022 (SCI-Expanded, Scopus)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 29
Basım Tarihi: 2022
Doi Numarası: 10.1016/j.acra.2021.10.026
Dergi Adı: Academic radiology
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE
Anahtar Kelimeler: Breast cancer, Lymphovascular invasion, Machine learning, Radiomics, Texture analysis, PREOPERATIVE PREDICTION, LOCOREGIONAL RECURRENCE, PROGNOSTIC-FACTORS, FEATURES, THERAPY, RADIOGENOMICS, HETEROGENEITY, COEFFICIENTS, CARCINOMA, NUMBER
İstanbul Üniversitesi-Cerrahpaşa 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.