This meta-analysis examines whether differences in biomarkers, such as programmed cell death ligand 1 (PD-L1) expression and tumor mutation burden, are associated with improved response to immunotherapy and survival in patients with advanced non–small cell lung cancer (NSCLC).

Key Points Question Can multiparametric magnetic resonance imaging (MRI) radiomic profiles be used to predict axillary lymph node metastasis (ALNM) and disease-free survival (DFS) in patients with early-stage breast cancer? Findings In this prognostic study that included 1214 patients, 2 clinical-radiomic nomograms were developed that accurately predicted ALNM and stratified patients into low-risk and high-risk groups for DFS. Meaning In this study, clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.

cohort, there were significant differences in the key radiomic features before and after neoadjuvant chemotherapy, especially in the gray-level dependence matrix features. Furthermore, there was an association between MRI radiomics and tumor microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites. Interpretation this study presented a multiomic signature that could be preoperatively and conveniently used for identifying patients with ALN metastasis in early-stage invasive breast cancer. The multiomic signature exhibited powerful predictive ability and showed the prospect of extended application to tailor surgical management. Besides, significant changes in key radiomic features after neoadjuvant chemotherapy may be explained by changes in the tumor microenvironment, and the association between MRI radiomic features and tumor microenvironment features may reveal the potential biological underpinning of MRI radiomics.

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