AUTHOR=Han Yu , Wang Zi-Jun , Li Wen-Hua , Yang Yang , Zhang Jian , Yang Xi-Biao , Zuo Lin , Xiao Gang , Wang Sheng-Zhong , Yan Lin-Feng , Cui Guang-Bin 

TITLE=Differentiation Between Primary Central Nervous System Lymphoma and Atypical Glioblastoma Based on MRI Morphological Feature and Signal Intensity Ratio: A Retrospective Multicenter Study

JOURNAL=Frontiers in Oncology

VOLUME=12

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.811197

DOI=10.3389/fonc.2022.811197

ISSN=2234-943X

ABSTRACT=<sec><title>Objectives</title><p>To investigate the value of morphological feature and signal intensity ratio (SIR) derived from conventional magnetic resonance imaging (MRI) in distinguishing primary central nervous system lymphoma (PCNSL) from atypical glioblastoma (aGBM).</p></sec><sec><title>Methods</title><p>Pathology-confirmed PCNSLs (n = 93) or aGBMs (n = 48) from three institutions were retrospectively enrolled and divided into training cohort (n = 98) and test cohort (n = 43). Morphological features and SIRs were compared between PCNSL and aGBM. Using linear discriminant analysis, multiple models were constructed with SIRs and morphological features alone or jointly, and the diagnostic performances were evaluated <italic>via</italic> receiver operating characteristic (ROC) analysis. Areas under the curves (AUCs) and accuracies (ACCs) of the models were compared with the radiologists’ assessment.</p></sec><sec><title>Results</title><p>Incision sign, T<sub>2</sub> pseudonecrosis sign, reef sign and peritumoral leukomalacia sign were associated with PCNSL (training and overall cohorts, <italic>P</italic> &lt; 0.05). Increased T<sub>1</sub> ratio, decreased T<sub>2</sub> ratio and T<sub>2</sub>/T<sub>1</sub> ratio were predictive of PCNSL (all <italic>P</italic> &lt; 0.05). ROC analysis showed that combination of morphological features and SIRs achieved the best diagnostic performance for differentiation of PCNSL and aGBM with AUC/ACC of 0.899/0.929 for the training cohort, AUC/ACC of 0.794/0.837 for the test cohort and AUC/ACC of 0.869/0.901 for the overall cohort, respectively. Based on the overall cohort, two radiologists could distinguish PCNSL from aGBM with AUC/ACC of 0.732/0.724 for radiologist A and AUC/ACC of 0.811/0.829 for radiologist B.</p></sec><sec><title>Conclusion</title><p>MRI morphological features can help differentiate PCNSL from aGBM. When combined with SIRs, the diagnostic performance was better than that of radiologists’ assessment.</p></sec>