基线 18F FDG PET/CT 影像衍生的影像组学特征可预测原发性乳腺癌患者的 TIL（肿瘤浸润淋巴细胞）数值

To determine the value of radiomics data extraction from baseline 18F FDG PET/CT in the prediction of tumor-infiltrating lymphocytes (TILs) among patients with primary breast cancer (BC).

We retrospectively evaluated 74 patients who underwent baseline 18F FDG PET/CT scans for BC evaluation between October 2020 and April 2022. Radiomics data extraction resulted in a total of 131 radiomic features from primary tumors. TILs status was defined based on histological analyses of surgical specimens and patients were categorized as having low TILs or moderate & high TILs. The relationships between TILs groups and tumor features, patient characteristics and molecular subtypes were examined. Features with a correlation coefficient of less than 0. 6 were analyzed by logistic regression to create a predictive model. The diagnostic performance of the model was calculated via receiver operating characteristics (ROC) analysis.

Menopausal status, histological grade, nuclear grade, and four radiomics features demonstrated significant differences between the two TILs groups. Multivariable logistic regression revealed that nuclear grade and three radiomics features (Morphological COMShift, GLCM Correlation, and GLSZM Small Zone Emphasis) were independently associated with TIL grouping.

The diagnostic performance analysis of the model showed an AUC of 0. 864 (95% CI: 0. 776-0. 953; p < 0. 001). The sensitivity, specificity, PPV, NPV and accuracy values of the model were 69. 6%, 82. 4%, 64%, 85. 7% and 78. 4%, respectivelyThe pathological TIL scores of BC patients can be predicted by using radiomics feature extraction from baseline 18F FDG PET/CT scans.