Development of an MRI Radiomic Machine-Learning Model to Predict Triple-Negative Breast Cancer Based on Fibroglandular Tissue of the Contralateral Unaffected Breast in Breast Cancer Patients.

Autor: Lo Gullo R; Department of Radiology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, New York, NY 10065, USA.; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA., Ochoa-Albiztegui RE; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA., Chakraborty J; Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA., Thakur SB; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA., Robson M; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA., Jochelson MS; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA., Varela K; CUNY School of Medicine, New York, NY 10031, USA., Resch D; Medical School, Sigmund Freud University, A-1020 Vienna, Austria., Eskreis-Winkler S; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA., Pinker K; Department of Radiology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, New York, NY 10065, USA.; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Jazyk: angličtina
Zdroj: Cancers [Cancers (Basel)] 2024 Oct 14; Vol. 16 (20). Date of Electronic Publication: 2024 Oct 14.
DOI: 10.3390/cancers16203480
Abstrakt: Aim: The purpose of this study was to develop a radiomic-based machine-learning model to predict triple-negative breast cancer (TNBC) based on the contralateral unaffected breast's fibroglandular tissue (FGT) in breast cancer patients.
Materials and Methods: This study retrospectively included 541 patients (mean age, 51 years; range, 26-82) who underwent a screening breast MRI between November 2016 and September 2018 and who were subsequently diagnosed with biopsy-confirmed, treatment-naïve breast cancer. Patients were divided into training ( n = 250) and validation ( n = 291) sets. In the training set, 132 radiomic features were extracted using the open-source CERR platform. Following feature selection, the final prediction model was created, based on a support vector machine with a polynomial kernel of order 2.
Results: In the validation set, the final prediction model, which included four radiomic features, achieved an F1 score of 0.66, an area under the curve of 0.71, a sensitivity of 54% [47-60%], a specificity of 74% [65-84%], a positive predictive value of 84% [78-90%], and a negative predictive value of 39% [31-47%].
Conclusions: TNBC can be predicted based on radiomic features extracted from the FGT of the contralateral unaffected breast of patients, suggesting the potential for risk prediction specific to TNBC.
Databáze: MEDLINE
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