Pituitary Adenoma Recurrence: Modeling Using a New Modification of the SIPAP Classification
Notice bibliographique
Résumé
Introduction: Pituitary adenomas are a common skull base tumor that varies in size, often classified based on function and size, either micro- or macroadenoma. Patient clinical presentation depends on multiple factors, including excessive hormone secretion, the mass effect of large tumors, and tumor invasion to surrounding structures. With the advancement of diagnostic modalities, MRI is considered being the modality of choice to evaluate pituitary tumor features including parasellar extension. Endoscopic endonasal transsphenoidal (EETS) resection is the surgical standard of care for pituitary adenoma resection due to its superior visualization of the sella and surrounding anatomy. However, recurrence of the pituitary tumor following surgery has been reported widely. Yet, intuitively, adenoma size and involvement of parasellar structures should impact gross tumor resection (GTR) and recurrence. We evaluated a modified score using the SIPAP classification system, combining the suprasellar and paraseller extension scores of the pituitary tumor to determine its impact on adenoma recurrence. Study Design: Retrospective cohort, single institutional study. Methods: Institutional REB approval was attained for a retrospective review of all EETS cases for pituitary tumor resection between November 2009 and October 2018. Queries of the hospital database were completed by medical records personnel to identify cases of pituitary tumor treated using the EETS approach. Patient characteristics, tumor type, endocrine data, and operation characteristics were then extracted from medical records pertaining to patient baseline characteristics. Preoperative MRI images were reviewed and the SIPAP classification applied to the pituitary tumors. Postoperative results were extracted for the duration of the follow-up period available for each patient. Within the SIPAP score, the suprasellar (S) and parasellar (P) scores are the most variable and believed to be the main drivers of surgical GTR. The suprasellar score and the highest parasellar score from both sides were numerically summed a bilateral suprasellar and parasellar (SaP) score and combined to make four grades. Results: A total of 276 patients were identified, 56.5% of the cohort was male. The mean age of the cohort was 54 years old. During the study period, five different neurosurgeons performed EETS for patients with pituitary tumors. The mean of the length of follow-up was 32 months. Patient perioperative tumor grade according to SaP classification and recurrence rate in each grade were as follows: grade 1: 11%; grade 2: 10%; grade 3: 15%; and grade 4: 22%. The results followed a pattern of logarithmic curve. Conclusion: The SaP classification was demonstrated to be useful for determining the expected recurrence of pituitary tumor following EETS, with the most advanced tumors demonstrating the highest rates of recurrence. Use of the SaP score will allow for more accurate preoperative counseling of patients with pituitary adenoma when considering recurrence requiring further surgery.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».