AUTHOR=Yu Hongwei , Meng Xianqi , Chen Huang , Liu Jian , Gao Wenwen , Du Lei , Chen Yue , Wang Yige , Liu Xiuxiu , Liu Bing , Fan Jingfan , Ma Guolin 

TITLE=Predicting the Level of Tumor-Infiltrating Lymphocytes in Patients With Breast Cancer: Usefulness of Mammographic Radiomics Features

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.628577

DOI=10.3389/fonc.2021.628577

ISSN=2234-943X

ABSTRACT=This study aimed to investigate whether radiomics classifiers from mammography can help predict tumour-infiltrating lymphocyte (TIL) levels in breast cancer.  121 consecutive patients with pathologically-proven invasive breast cancer who underwent preoperative mammography  were retrospectively analysed. Patients were randomly divided into training (n = 85) and validation (n = 36) sets. A total of 612 quantitative radiomics features were extracted from mammograms Radiomics feature selection and radiomics classifier were generated through recursive feature elimination and logistic regression analysis model. The relationship between radiomics features and TIL levels in breast cancer patients was explored. The predictive capacity of the radiomics classifiers for the TIL levels was investigated through receiver operating characteristic curves in the training and validation groups. A radiomics score (Rad-score) was generated using a logistic regression analysis method to compute the training and validation datasets, and combining the Mann-Whitney U test to evaluate the level of TILs in the low and high groups. Among the 121 patients, 32 (26.44%) exhibited high TIL levels and 89 (73.56%) showed low TIL levels. The ER negativity (p = 0.01) and the Ki-67 negative threshold level (p = 0.03) in the low TIL group was higher than that in the high TIL group. Through the radiomics feature selection, six top-class features [Wavelet GLDM low grey-level emphasis (mediolateral oblique, MLO), GLRLM short-run low grey-level emphasis (craniocaudal, CC), LBP2D GLRLM short-run high grey-level emphasis (CC), LBP2D GLDM dependence entropy (MLO), wavelet interquartile range (MLO), and LBP2D median (MLO)] were selected to constitute the radiomics classifiers. The radiomics classifier had an excellent predictive performance for TIL levels both in the training and validation sets [area under the curve (AUC): 0.83, 95% confidence interval (CI), 0.738-0.917, with positive predictive value (PPV) of 0.913; AUC: 0.79, 95% CI, 0.615-0.964, with PPV of 0.889, respectively]. Moreover, the Rad-score in the training group was higher than that in the validation group (p = 0.007 and p = 0.001, respectively).  Radiomics from digital mammograms not only predicts the TIL levels in breast cancer patients, but can also serve as non-invasive biomarkers in precision medicine, allowing for the development of treatment plans.