A novel MRI feature, the cut green pepper sign, can help differentiate a suprasellar pilocytic astrocytoma from an adamantinomatous craniopharyngioma

A Fusion Model of MRI Deep Transfer Learning and Radiomics for Discriminating between Pilocytic Astrocytoma and Adamantinomatous Craniopharyngioma

RATIONALE AND OBJECTIVES

This study aimed to develop and validate a fusion model combining MRI deep transfer learning (DTL) and radiomics for discriminating between pilocytic astrocytoma (PA) and adamantinomatous craniopharyngioma (ACP) in the sellar region.

METHODS

This study included 348 patients with histologically confirmed PA (n = 139) and ACP (n = 209). Data were randomly divided into training and testing cohorts in a 7:3 ratio. Pre-trained ResNet50 network was utilized to extract DTL features from T1WI, T2WI, and CET1, while radiomics features (Rad) were extracted from manually delineated images of the same modalities. The fusion feature set (DLR) was constructed by integrating these features. Semantic features were used to develop clinical models. Pearson rank correlation and The least absolute shrinkage and selection operator regression were used for feature selection, and K-nearest neighbor algorithm was applied to establish the model. The performance of the model was evaluated using receiver operating characteristic curve. DeLong's test was performed to assess differences between models, and decision curve analysis was conducted to evaluate the clinical utility of the models.

RESULTS

The DLR model achieved AUC values of 0.945 (95% CI, 0.9149-0.9760) in the training cohort and 0.929 (95% CI, 0.8824-0.9762) in the testing cohort, significantly higher than those of models using DTL features, Rad features, or clinical features alone.

CONCLUSION

The fusion model based on MRI deep transfer learning and radiomics (DLR) demonstrated high accuracy and clinical utility in discriminating between PA and ACP, providing an effective tool for the non-invasive diagnosis of these two diseases.

Comparison of MRI findings of hypothalamic-optic chiasmatic gliomas and craniopharyngiomas

BACKGROUND

Differential diagnosis of hypothalamic-optic chiasmatic gliomas (HOCGs) and craniopharyngiomas on magnetic resonance imaging (MRI) can be quite challenging.

PURPOSE

To compare the MRI features of HOCGs and cranipharyngiomas.

MATERIAL AND METHODS

Patients diagnosed with HOCG or craniopharyngioma in histopathological evaluation between 2012 and 2022 and who underwent preoperative contrast-enhanced brain MRI were included. Various MRI features were retrospectively evaluated for each lesion: T2-weighted imaging and fluid attenuation inversion recovery hyperintensity, calcification, cystic change, T1-weighted (T1W) imaging hyperintensity of the cystic component, hemorrhage, involvement of sellar, suprasellar or other adjacent structures, lobulated appearance, presence of hydrocephalus, and contrast enhancement pattern. Apparent diffusion coefficient (ADC) values were also evaluated and compared.

RESULTS

Among 38 patients included, 13 (34%) had HOCG and 25 (66%) had craniopharyngioma. Craniopharyngiomas had a significantly higher rate of cystic changes, calcification, and T1W imaging hyperintensity of the cystic component than HOCGs (P <0.05). Of HOCGs, 92% had chiasm involvement, 23% had optic nerve involvement, and 31% had brain stem involvement. On the other hand, chiasm involvement was observed in 8% of craniopharyngiomas, but none had optic nerve and/or brain stem involvement (P <0.05). While 62% (8/13) of HOCGs had diffuse homogeneous enhancement, 80% (20/25) of craniopharyngiomas had a diffuse heterogeneous enhancement pattern. Mean ADC values were significantly higher in craniopharyngiomas compared to HOCGs (2.1 vs. 1.6 ×10-3mm2/s, P <0.05).

CONCLUSION

Although some neuroimaging findings may overlap, features such as presence of cyst and calcification, brain stem and optic pathway involvement, different enhancement patterns, and ADC values may be helpful in the differential diagnosis of HOCGs and craniopharyngiomas.