{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["67(3)"],"submitter":["Wang M"],"pubmed_abstract":["Purpose
In primary central nervous system lymphoma (PCNSL), B-cell lymphoma-6 (BCL-6) is an unfavorable prognostic biomarker. We aim to non-invasively detect BCL-6 overexpression in PCNSL patients using multiparametric MRI and machine learning techniques.Methods
65 patients (101 lesions) with primary central nervous system lymphoma (PCNSL) diagnosed from January 2013 to July 2023, and all patients were randomly divided into a training set and a validation set according to a ratio of 8 to 2. ADC map derived from DWI (b = 0/1000 s/mm2), fast spin echo T2WI, T2FLAIR, were collected at 3.0 T. A total of 2234 radiomics features from the tumor segmentation area were extracted and LASSO were used to select features. Logistic regression (LR), Naive bayes (NB), Support vector machin"],"journal":["Neuroradiology"],"pagination":["563-573"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12003451"],"repository":["biostudies-literature"],"pubmed_title":["Detecting B-cell lymphoma-6 overexpression status in primary central nervous system lymphoma using multiparametric MRI-based machine learning."],"pmcid":["PMC12003451"],"pubmed_authors":["Wang M","Li Y","Ma L","Sun S","Tan Y","Zhang N","Liu G"],"additional_accession":[]},"is_claimable":false,"name":"Detecting B-cell lymphoma-6 overexpression status in primary central nervous system lymphoma using multiparametric MRI-based machine learning.","description":"Purpose
In primary central nervous system lymphoma (PCNSL), B-cell lymphoma-6 (BCL-6) is an unfavorable prognostic biomarker. We aim to non-invasively detect BCL-6 overexpression in PCNSL patients using multiparametric MRI and machine learning techniques.Methods
65 patients (101 lesions) with primary central nervous system lymphoma (PCNSL) diagnosed from January 2013 to July 2023, and all patients were randomly divided into a training set and a validation set according to a ratio of 8 to 2. ADC map derived from DWI (b = 0/1000 s/mm2), fast spin echo T2WI, T2FLAIR, were collected at 3.0 T. A total of 2234 radiomics features from the tumor segmentation area were extracted and LASSO were used to select features. Logistic regression (LR), Naive bayes (NB), Support vector machin","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-07-03T03:04:39.575Z","creation":"2025-07-03T03:04:39.575Z"},"accession":"S-EPMC12003451","cross_references":{"pubmed":["39853344"],"doi":["10.1007/s00234-025-03551-y"]}}