Multiparametric MRI-Based Radiomics for Identifying Primary Central Nervous System Diffuse Large B-cell Lymphomas' Pathological Subtypes.

RATIONALE AND OBJECTIVES

To explore the predictive potential of radiomics features extracted from preoperative multiparametric magnetic resonance imaging (MRI) for identifying pathological subtypes of primary central nervous system diffuse large B-cell lymphomas (PCNS-DLBCL).

METHODS

This study recruited 186 patients with PCNS-DLBCL, including 55 with germinal center B-cell-like (GCB) subtype and 131 with non-GCB subtype. The largest abnormal signal regions of the tumor were automatically segmented in T1-weighted images (T1WI), T2-weighted images, T2 fluid-attenuated inversion recovery, contrast-enhanced T1-weighted (CE-T1WI), and apparent diffusion coefficient (ADC) maps, respectively. Radiomics features were extracted from preprocessed multiparameter preoperative MRI images. To identify GCB and non-GCB subtypes, radiomics models were constructed based on each MRI sequence and combinations of sequences. Clinical models and models combining radiomics and clinical features were also constructed to compare performance.

RESULTS

Radiomics models combining multiple sequences generally outperformed single-sequence radiomics models. The ADC+CE-T1WI model exhibited superior discriminative power, with an area under the curve of 0.867 (95% CI, 0.745-0.988). Models incorporating more sequences (3-5 sequences) did not demonstrate better performance. The performance of the model combining radiomics features with clinical features showed no improvement.

CONCLUSION

Radiomics based on multiparametric MRI have independent value in predicting the pathological subtypes of PCNS-DLBCL patients.