Radiomic features and tumor immune microenvironment associated with anaplastic lymphoma kinase-rearranged lung adenocarcinoma and their prognostic value.

PURPOSE

To identify radiomic features from preoperative computed tomography (CT) images and characteristics of the tumor immune microenvironment (TIME) associated with anaplastic lymphoma kinase () rearrangement in patients with lung adenocarcinomas and their prognostic value in predicting recurrence or metastases after surgery.

MATERIALS AND METHODS

This retrospective study included 66 -positive and 66 -negative patients who underwent surgical resected lung adenocarcinoma. The number of CD8 T cells and Human leukocyte antigen class I (HLA-I)/programmed death ligand 1 (PD-L1) expression were determined using immunohistochemistry. Radiomic features were extracted from the preoperative CT images. Combined radiomic, clinicopathological, and clinicopathological-radiomic models were built to predict rearrangements. The models' prediction performance was analyzed using receiver operating characteristic (ROC) curves with five-fold cross-validation. Prediction models for determining disease-free survival (DFS) of -rearranged patients were developed, and the C-index after internal cross-validation was calculated to evaluate the performance of the models.

RESULTS

HLA-I and PD-L1 expression were negatively associated with rearrangement (both P < 0.001). The ROC curve indicated areas under the curve of 0.763, 0.817, and 0.878 for the radiomics, clinicopathology, and combined models in predicting rearrangement, respectively. The combined model showed significant improvement compared to the clinicopathological (P = 0.02) and radiomics (P < 0.001) models alone. The validation C-indices were 0.752, 0.712, and 0.808 for the radiomic, clinicopathological, and combined models in predicting the DFS of -rearranged patients, respectively. The combined model showed a significant improvement (P < 0.001) compared to the clinicopathological model alone.

CONCLUSION

This study demonstrated the potential role of radiomics and TIME characteristics in identifying rearrangements in lung adenocarcinomas and the prognostic value of radiomics in predicting DFS in patients with rearrangements.