Mr. Qianming Bai Profile

Qianming Bai

SPIE Involvement:

Author

Publications (1)

Proceedings Article | 15 February 2021 Paper

Prediction of Oncotype DX recurrence score in breast cancer by integration of DCE-MRI radiomics and clinicopathologic data

Yajing Cui, Ming Fan, Weijun Peng, Li Liu, Qianming Bai, Lihua Li

Proceedings Volume 11601, 1160111 (2021) https://doi.org/10.1117/12.2581336

KEYWORDS: Breast cancer, Remote sensing, Performance modeling, Yield improvement, Resonance enhancement, Network security, Magnetic resonance imaging, Genetics, Feature selection, Feature extraction

Read Abstract +

Oncotype DX recurrence score (RS) is increasingly used to differentiate patients at high risk of recurrence from those who have low risk of recurrence. Despite the promising value, this genetic technology has disadvantage in its expensive and currently not readily available in most of the institute, which limited the clinical applications in management of breast cancer. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is becoming standard-of-care in breast cancer management. It is of vital importance to predict Oncotype DX RS using radiomics from DCE-MRI to perform a noninvasive evaluation of recurrence in breast cancer. To this end, 131 patients were included in the dataset, of which 13 (9.9%) had a low risk of recurrence (RS<18), 74 (56.5%) samples had a moderate risk of recurrence (18≤RS<31), and 44 (33.6%) patients had a high risk of recurrence (RS≥31). Among these, 77 samples were used as the training set and 54 were testing set. Univariate and multivariate regression analyses were performed to evaluate the effectiveness of the radiomics. Specifically, in the multivariate regression analysis, an elastic network regression model was established with a ten-fold cross-validation method to evaluate the prediction performance. A total of 479 features were extracted from DCE-MRI, and 6 clinicopathologic indicators were included. After proper feature selection, 20 features were remained and was used for subsequent analysis. In the univariate linear regression analysis, 11 imaging features and 3 clinicopathologic features (i.e., PR, Ki-67 and molecular classification) were significantly correlated with RS (P<0.05). Multivariate model using 6 radiomic features generated the prediction performance in terms of R square 0.242 (P=0.0352) on the testing set. The prediction model yields an improved performance in terms of R square of 0.308 (P=0.0236) after combing clinicopathological factors. The results showed that DCE-MRI radiomics combined with clinicopathologic indicators would be promising in the risk of recurrence evaluation in breast cancer.

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years