Multi-Dynamic-Multi-Echo-based MRI for the Pre-Surgical Determination of Sellar Tumor Consistency: a Quantitative Approach for Predicting Lesion Resectability.
Clin Neuroradiol
; 2024 Apr 19.
Article
in En
| MEDLINE
| ID: mdl-38639770
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 3â¯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 1221.0â¯ms⯱ 211.9; eRAsp 1500.2â¯ms ±â¯496.4; pâ¯= 0.003) and T2R (hRAsp 88.8â¯ms⯱ 14.5; eRAsp 137.2â¯ms⯱ 166.6; pâ¯= 0.03) between both groups. The ROC analysis revealed an area under the curve of 0.72 (95% CI 0.60-0.85) at pâ¯= 0.003 for T1R (cutoff value 1248â¯ms; sensitivity/specificity 78%/58%) and 0.66 (95% CI 0.53-0.79) at pâ¯= 0.03 for T2R (cutoff value 110â¯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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Clin Neuroradiol
Journal subject:
NEUROLOGIA
/
RADIOLOGIA
Year:
2024
Document type:
Article
Affiliation country:
Austria