AUTHOR=Tao Weijing , Lu Mengjie , Zhou Xiaoyu , Montemezzi Stefania , Bai Genji , Yue Yangming , Li Xiuli , Zhao Lun , Zhou Changsheng , Lu Guangming 

TITLE=Machine Learning Based on Multi-Parametric MRI to Predict Risk of Breast Cancer

JOURNAL=Frontiers in Oncology

VOLUME=11

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.570747

DOI=10.3389/fonc.2021.570747

ISSN=2234-943X

ABSTRACT=<sec><title>Purpose</title><p>Machine learning (ML) can extract high-throughput features of images to predict disease. This study aimed to develop nomogram of multi-parametric MRI (mpMRI) ML model to predict the risk of breast cancer.</p></sec><sec><title>Methods</title><p>The mpMRI included non-enhanced and enhanced T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), <italic>K</italic><sup>trans</sup>, <italic>K</italic><sub>ep</sub>, <italic>V</italic><sub>e</sub>, and <italic>V</italic><sub>p</sub>. Regions of interest were annotated in an enhanced T1WI map and mapped to other maps in every slice. 1,132 features and top-10 principal components were extracted from every parameter map. Single-parametric and multi-parametric ML models were constructed <italic>via</italic> 10 rounds of five-fold cross-validation. The model with the highest area under the curve (AUC) was considered as the optimal model and validated by calibration curve and decision curve. Nomogram was built with the optimal ML model and patients’ characteristics.</p></sec><sec><title>Results</title><p>This study involved 144 malignant lesions and 66 benign lesions. The average age of patients with benign and malignant lesions was 42.5 years old and 50.8 years old, respectively, which were statistically different. The sixth and fourth principal components of <italic>K</italic><sup>trans</sup> had more importance than others. The AUCs of <italic>K</italic><sup>trans</sup>, <italic>K</italic><sub>ep</sub>, <italic>V</italic><sub>e</sub> and <italic>V</italic><sub>p</sub>, non-enhanced T1WI, enhanced T1WI, T2WI, and ADC models were 0.86, 0.81, 0.81, 0.83, 0.79, 0.81, 0.84, and 0.83 respectively. The model with an AUC of 0.90 was considered as the optimal model which was validated by calibration curve and decision curve. Nomogram for the prediction of breast cancer was built with the optimal ML models and patient age.</p></sec><sec><title>Conclusion</title><p>Nomogram could improve the ability of breast cancer prediction preoperatively.</p></sec>