Machine learning models based on quantitative dynamic contrast-enhanced MRI parameters assess the expression levels of CD3+, CD4+, and CD8+ tumor-infiltrating lymphocytes in advanced gastric carcinoma

MLA

Huizhen Huang, et al. “Machine Learning Models Based on Quantitative Dynamic Contrast-Enhanced MRI Parameters Assess the Expression Levels of CD3+, CD4+, and CD8+ Tumor-Infiltrating Lymphocytes in Advanced Gastric Carcinoma.” Frontiers in Oncology, vol. 14, Mar. 2024. EBSCOhost, https://doi.org/10.3389/fonc.2024.1365550.

APA

Huizhen Huang, Zhiheng Li, Dandan Wang, Ye Yang, Hongyan Jin, & Zengxin Lu. (2024). Machine learning models based on quantitative dynamic contrast-enhanced MRI parameters assess the expression levels of CD3+, CD4+, and CD8+ tumor-infiltrating lymphocytes in advanced gastric carcinoma. Frontiers in Oncology, 14. https://doi.org/10.3389/fonc.2024.1365550

Chicago

Huizhen Huang, Zhiheng Li, Dandan Wang, Ye Yang, Hongyan Jin, and Zengxin Lu. 2024. “Machine Learning Models Based on Quantitative Dynamic Contrast-Enhanced MRI Parameters Assess the Expression Levels of CD3+, CD4+, and CD8+ Tumor-Infiltrating Lymphocytes in Advanced Gastric Carcinoma.” Frontiers in Oncology 14 (March). doi:10.3389/fonc.2024.1365550.