Magnetic resonance imaging grading of pituitary macroadenoma - SIPAP classification revisited.
Eur J Radiol Open
; 10: 100486, 2023.
Article
en En
| MEDLINE
| ID: mdl-36969507
Background: Magnetic resonance imaging (MRI) is regarded as the modality of choice in diagnosis of pituitary macroadenomas. Since surgery is the first line therapy for all pituitary adenomas, simple and reproducible MRI classification based on major directions of tumour growth is an essential tool. SIPAP MRI classification for pituitary adenoma describes tumor extension in parasellar, suprasellar, infrasellar, anterior and posterior directions. We, therefore, evaluated reproducibility of SIPAP classification in reporting of pituitary adenomas. Methods: Forty-nine patients with biopsy-proven pituitary macroadenoma were graded according to SIPAP classification. Data was analyzed using Stata version 15. Interobserver variability was calculated using Cohen's Kappa. Comparison between grading before and after treatment was performed by Chi-square test. P values < 0.05 were considered statistically significant. Results: Individual tumour extensions according to SIPAP for pre- and post-operative grading showed significant difference (p-value <0.001), except for anterior extension. For suprasellar extension, 67.3 % patients had pre-operative grade-3 and 63.3 % had post-operative grade-0. For infrasellar extension, 51.0 % had pre-operative grade-2 and 71.4 % had post-operative grade-0. Anterior, posterior and parasellar extensions showed increased frequency in grade-0 in post-operative stage compared to pre-operative. Substantial inter-observer agreement was achieved for Superior, Inferior, Anterior and Posterior extent with all Kappa statistics values above 0.7 (p-value <0.001). Conclusion: We propose incorporating simple and objective SIPAP classification in routine MR reporting for ideal pituitary tumour delineation, relationship to juxtasellar structures and tumour size, hence facilitating greater success rate in surgical and subsequent clinical management.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Eur J Radiol Open
Año:
2023
Tipo del documento:
Article
País de afiliación:
Pakistán
Pais de publicación:
Reino Unido