淋巴血管侵犯
随机森林
乳腺癌
医学
乳房磁振造影
人工智能
无线电技术
分割
特征选择
重采样
计算机科学
放射科
乳腺摄影术
癌症
内科学
转移
作者
Yasemin Kayadibi,Burak Koçak,Neşe Uçar,Yeşim Namdar Akan,Emine Yıldırım,Sibel Bektaş
标识
DOI:10.1016/j.acra.2021.10.026
摘要
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.This retrospective study included patients with BC with known LVI status and preoperative MRI. The dataset was split into training and unseen testing sets by stratified sampling with a 2:1 ratio. 2D and 3D radiomic features were extracted from contrast-enhanced T1 weighted images (C+T1W) and apparent diffusion coefficient (ADC) maps. The reliability of the features was assessed with two radiologists' segmentation data. Dimension reduction was done with reliability analysis, multi-collinearity analysis, removal of low-variance features, and feature selection. ML models were created with base, tuned, and boosted random forest algorithms.A total of 128 lesions (LVI-positive, 76; LVI-negative, 52) were included. The best model performance was achieved with tunning and boosting model based on 3D ADC maps and selected four radiomic features. The area under the curve and accuracy were 0.726 and 63.5% in the training data, 0.732 and 76.7% in the test data, respectively. The overall sensitivity and positive predictive values were 68% and 69.6% in the training data, 84.6% and 78.6% in the test data, respectively.ML and radiomics based on 3D segmentation of ADC maps can be used to predict LVI status in BC, with satisfying performance.
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