{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Deng F"],"funding":["Geriatric Research Education and Clinical Center","High Performance Computing Center of Central South University","Health Research Project of Hunan Provincial Health Commission","Natural Science Foundation of Hunan Province","Chen Xiao-Ping Foundation for the Development of Science and Technology of Hubei Province","National Multidisciplinary Cooperative Diagnosis and Treatment Capacity","Advanced Lung Cancer Targeted Therapy Research Foundation of China","Postdoctoral Fellowship Program of CPSF"],"pagination":["e2412590"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11904944"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(10)"],"pubmed_abstract":["Non-small cell lung cancer (NSCLC) frequently metastasizes to the brain, significantly worsened prognoses. This study aimed to develop an interpretable model for predicting survival in NSCLC patients with brain metastases (BM) integrating radiomic features and RNA sequencing data. 292 samples are collected and analyzed utilizing T1/T2 MRIs. Bidirectional stepwise logistic regression is employed to identify significant variables, facilitating the construction of a prognostic model, which is benchmarked against four machine learning algorithms. BM tissue samples are processed for RNA extraction and sequencing. The optimal model achieved an AUC of 0.96 and a C-index of 0.89 in the train set and an AUC of 0.84 with a C-index of 0.78 in the test set, indicating strong predictive performance and"],"journal":["Advanced science (Weinheim, Baden-Wurttemberg, Germany)"],"pubmed_title":["Predicting Survival Rates in Brain Metastases Patients from Non-Small Cell Lung Cancer Using Radiomic Signatures Associated with Tumor Immune Heterogeneity."],"pmcid":["PMC11904944"],"funding_grant_id":["2022LNJJ10","W20242005","2023JJ50014","CTONG-YC20210303","CXPJJH121005-01","GZC20233187"],"pubmed_authors":["Tanzhu G","Zhang Z","Chen L","Zhou R","Xiao G","Chu X","Ning J","Deng F","Lu R"],"additional_accession":[]},"is_claimable":false,"name":"Predicting Survival Rates in Brain Metastases Patients from Non-Small Cell Lung Cancer Using Radiomic Signatures Associated with Tumor Immune Heterogeneity.","description":"Non-small cell lung cancer (NSCLC) frequently metastasizes to the brain, significantly worsened prognoses. This study aimed to develop an interpretable model for predicting survival in NSCLC patients with brain metastases (BM) integrating radiomic features and RNA sequencing data. 292 samples are collected and analyzed utilizing T1/T2 MRIs. Bidirectional stepwise logistic regression is employed to identify significant variables, facilitating the construction of a prognostic model, which is benchmarked against four machine learning algorithms. BM tissue samples are processed for RNA extraction and sequencing. The optimal model achieved an AUC of 0.96 and a C-index of 0.89 in the train set and an AUC of 0.84 with a C-index of 0.78 in the test set, indicating strong predictive performance and","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-04-18T12:50:37.034Z","creation":"2025-04-06T22:13:37.551Z"},"accession":"S-EPMC11904944","cross_references":{"pubmed":["39840456"],"doi":["10.1002/advs.202412590"]}}