Lo Gullo Roberto, Ochoa-Albiztegui Rosa Elena, Chakraborty Jayasree, Thakur Sunitha B, Robson Mark, Jochelson Maxine S, Varela Keitha, Resch Daphne, Eskreis-Winkler Sarah, Pinker Katja
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.
Cancers (Basel). 2024 Oct 14;16(20):3480. doi: 10.3390/cancers16203480.
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.
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 ( = 250) and validation ( = 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.
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%].
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.
本研究旨在开发一种基于影像组学的机器学习模型，以根据乳腺癌患者对侧未受影响乳腺的纤维腺组织（FGT）预测三阴性乳腺癌（TNBC）。
本研究回顾性纳入了541例患者（平均年龄51岁；范围26 - 82岁），这些患者在2016年11月至2018年9月期间接受了乳腺筛查MRI检查，随后被活检确诊为未经治疗的乳腺癌。患者被分为训练组（n = 250）和验证组（n = 291）。在训练组中，使用开源的CERR平台提取了132个影像组学特征。经过特征选择后，基于二阶多项式核的支持向量机创建了最终的预测模型。
在验证组中，包含四个影像组学特征的最终预测模型的F1分数为0.66，曲线下面积为0.71，敏感性为54%[47 - 60%]，特异性为74%[65 - 84%]，阳性预测值为84%[78 - 90%]，阴性预测值为39%[31 - 47%]。
可根据从患者对侧未受影响乳腺的FGT中提取的影像组学特征预测TNBC，这表明TNBC特异性风险预测具有潜力。
