Multi-Dynamic-Multi-Echo-based MRI for the Pre-Surgical Determination of Sellar Tumor Consistency: a Quantitative Approach for Predicting Lesion Resectability

Author:

Yildirim Mehmet Salih,Schmidbauer Victor Ulrich,Micko Alexander,Lechner Lisa,Weber Michael,Furtner Julia,Wolfsberger Stefan,Malla Houech Intesar-Victoria,Cho Anna,Dovjak Gregor,Kasprian Gregor,Prayer Daniela,Marik WolfgangORCID

Abstract

Abstract Purpose Pre-surgical information about tumor consistency could facilitate neurosurgical planning. This study used multi-dynamic-multi-echo (MDME)-based relaxometry for the quantitative determination of pituitary tumor consistency, with the aim of predicting lesion resectability. Methods Seventy-two patients with suspected pituitary adenomas, who underwent preoperative 3 T MRI between January 2020 and January 2022, were included in this prospective study. Lesion-specific T1-/T2-relaxation times (T1R/T2R) and proton density (PD) metrics were determined. During surgery, data about tumor resectability were collected. A Receiver Operating Characteristic (ROC) curve analysis was performed to investigate the diagnostic performance (sensitivity/specificity) for discriminating between easy- and hard-to-remove by aspiration (eRAsp and hRAsp) lesions. A Mann-Whitney-U-test was done for group comparison. Results A total of 65 participants (mean age, 54 years ± 15, 33 women) were enrolled in the quantitative analysis. Twenty-four lesions were classified as hRAsp, while 41 lesions were assessed as eRAsp. There were significant differences in T1R (hRAsp: 1221.0 ms ± 211.9; eRAsp: 1500.2 ms ± 496.4; p = 0.003) and T2R (hRAsp: 88.8 ms ± 14.5; eRAsp: 137.2 ms ± 166.6; p = 0.03) between both groups. The ROC analysis revealed an area under the curve of 0.72 (95% CI: 0.60–0.85) at p = 0.003 for T1R (cutoff value: 1248 ms; sensitivity/specificity: 78%/58%) and 0.66 (95% CI: 0.53–0.79) at p = 0.03 for T2R (cutoff value: 110 ms; sensitivity/specificity: 39%/96%). Conclusion MDME-based relaxometry enables a non-invasive, pre-surgical characterization of lesion consistency and, therefore, provides a modality with which to predict tumor resectability.

Funder

Medical University of Vienna

Publisher

Springer Science and Business Media LLC

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