Histogram analysis based on contrast-enhanced T1-weighted imaging in pituitary macroadenomas: relation to...

Non-gonadotroph macroadenomas may exhibit higher cavernous sinus invasiveness compared to gonadotroph macroadenomas. Magnetic resonance imaging (MRI) is the preferred modality for detecting pituitary adenomas, yet, conventional MRI cannot distinguish gonadotroph from non-gonadotroph macroadenomas. This study aimed to evaluate the efficacy of histogram analysis based on contrast-enhanced T1-weighted imaging (CE-T1WI) for differentiating gonadotroph from non-gonadotroph macroadenomas.

A retrospective analysis was conducted on 58 gonadotroph and 60 non-gonadotroph macroadenomas, pathologically confirmed at Henan Provincial People's Hospital between January 2022 and September 2024. Using 3D Slicer software, regions of interest (ROIs) were delineated on the coronal section with the largest area on the CE-T1WI images for grayscale histogram analysis. Then, the ROIs were copied to T1-weighted imaging (T1WI) maps to yield the subtraction between CE-T1WI and T1WI. Eight histogram parameters were obtained from the CE-T1WI and the subtraction between CE-T1WI and T1WI, including: the 10th percentile (Perc.10%), 90th percentile (Perc.90%), kurtosis, mean, median, maximum, minimum, and skewness values. A combined parameter model incorporating statistically significant parameters was developed. Continuous variables were compared using the independent samples t-test or Mann-Whitney U test. Tumor invasiveness was assessed via Knosp grading. The diagnostic performance of significant parameters was assessed using receiver operating characteristic (ROC) curves with the area under the curve (AUC) calculations. Pearson analysis determined the correlations between histogram parameters and Ki-67/P53 expression levels.

Among the histogram parameters derived from the CE-T1WI and the subtraction between CE-T1WI and T1WI, the Perc.10%, Perc.90%, mean, median, maximum, and minimum values demonstrated statistically significant differences (P<0.001 for all) in distinguishing gonadotroph from non-gonadotroph macroadenomas. The histogram analysis derived from the subtraction between CE-T1WI and T1WI demonstrated better discrimination performance compared to that derived from CE-T1WI. For instance, Perc.10% derived from subtraction (AUC =0.979) had significantly greater AUC than that derived from CE-T1WI (AUC =0.838, P<0.05). The combined parameters achieved an AUC of 0.992, significantly outperforming individual parameters (P<0.05). Non-gonadotroph macroadenomas exhibited greater invasiveness than gonadotroph macroadenomas (Knosp grades 3 and 4: 58.3% versus 37.9%, P=0.027), and showed higher Ki-67/P53 expression levels (P=0.007, 0.042, respectively). Additionally, there were positive correlations between Perc.90%, maximum, minimum and Ki-67, as well as between Perc.10%, Perc.90%, minimum and P53 (P<0.05 for all).

Histogram analysis of both CE-T1WI and the subtraction between CE-T1WI and T1WI could differentiate gonadotroph macroadenomas from non-gonadotroph macroadenomas. The CE-T1WI histogram parameters were correlated with the Ki-67 and P53 expression levels in the macroadenomas.