New Pituitary Adenoma Classification System to Individualise Management and Improve Long‐Term Prognosis
Notice bibliographique
Résumé
There is a need for a classification that helps to risk-stratify patients with pituitary adenomas and to identify their best treatment options. The proposed clinical classification for pituitary neoplasms, published earlier this year in The Lancet Diabetes & Endocrinology [1], is an attempt to stratify patients according to their long-term prognosis and to enable individualised management and therapy decisions. The proposal was initiated by the Pituitary Society, and endorsed by the Endocrine Society, European Society of Endocrinology, International Society of Pituitary Surgeons, American Association of Clinical Endocrinology and US and Canadian Academy of Pathology. This proposal is partly a response to the 2022 WHO reclassification of pituitary adenomas as pituitary neuroendocrine tumours (PitNET) that is limited to patients who have received surgical treatment [2], and has been criticised for not adding any new support for clinical decision making of patients with pituitary adenoma [3]. Thus, the aim of the new clinical classification was to provide an evidence-based system that predicts prognosis, guides treatment and allows comparison of different types of interventions that can be used locally and in a global setting. The proposed classification model enables the assessment of all types of pituitary adenomas, regardless of whether surgical resection has provided tissue for histological examination or not. A panel of experts included nine factors associated with prognosis for patients with pituitary adenoma: phenotype, secretory status, hypopituitarism, size, mass effect, invasion, residual tumour, histopathology and genetic syndromes. A semiquantitative system was adopted for these nine risk factors where scores of 0 or 1 reflect the absence or presence of the risk factor, and a score of 2 was assigned to specific factors that are strongly associated with poor prognosis. The scores were then summed up to reflect the cumulative effect of all risk factors. The authors acknowledge that the classification system requires validation in independent studies, preferably prospective ones. The outcome among patients with pituitary adenoma is influenced by many factors as implicated in the proposed clinical classification system. It is influenced by the pituitary tumour phenotype and its behaviour, the presence of hypopituitarism, but also other related comorbidities such as cardiovascular disorders, hypertension and diabetes mellitus [4-7]. Since histopathology alone cannot adequately predict which tumours will progress after surgery, combined scoring systems incorporating adenoma invasiveness from imaging have been developed to support individualised tumour surveillance and management [8, 9]. This combination of imaging and histopathology has improved the prediction of tumour behaviour after surgery, however, the search for new biomarkers that better reflects postoperative tumour dynamics continues [5, 10]. The proposed clinical classification for pituitary neoplasm goes further in what has been done before by incorporating nine different risk factors to assess the overall prognosis for each individual patient in which tumour behaviour and tumour characteristics are just two out of nine factors. This approach better reflects the overall risk assessment of patients with pituitary adenomas, facilitating the comparison between individual patients and subgroups of patients and will enable standardised reporting in retrospective and prospective cohort studies. However, due to the simplicity of the classification, its use has some limitations that need to be considered. First, this classification does not provide sufficient information for individualised tumour surveillance and selection of secondary treatments after surgery. Also, the importance of secretory status, especially postoperatively in patients with Cushing's disease and acromegaly, is probably underestimated. For example, a patient with Cushing's disease and a non-visible pituitary adenoma that is not in biochemical remission after primary surgery receives a relatively low score (3/12). In contrast, a medically treated patient with prolactinoma that is under control but has a visible tumour that invades the cavernous sinus and still has elevated serum prolactin concentration and hypogonadism will receive a high score (5/9). Additionally, the system does not include factors critical for predicting survival, such as obesity (often related to the hypothalamic impact of the tumour), hypertension and diabetes mellitus, which are strong modulators of health and lifespan and common in patients with pituitary tumours, particularly in patients with acromegaly and Cushing's disease [4, 7]. Another potential criticism is the inclusion of both functioning and nonfunctioning pituitary adenoma, despite their significant difference in tumour phenotype, clinical picture and long-term prognosis [5]. The experts behind the proposed clinical classification for pituitary neoplasm (adenomas) have developed a tool intended to guide both therapy and prognosis through patient ‘staging’ for clinical and research purposes. After validation, ideally using retrospective data from centres with experience in long-term care of patients with pituitary adenomas, this evidence-based framework could offer a standardised approach to outcome assessment. However, some limitations remain, such as the omission of critical comorbidities, which hampers comprehensive risk assessment. Additionally, developing an IT-based solution to streamline score calculation would further enhance its utility. In conclusion, the proposed clinical classification for pituitary adenomas represents an advancement in the field, providing a standardised, evidence-based framework with the potential to transform patient management and research. However, some limitations exist, such as the need for validation and the exclusion of comorbidities known to be of importance for long-term prognosis. Nevertheless, the adoption of this classification, supported by IT solutions for ease of use, has the potential to pave the way for more informed decision-making, ultimately improving outcomes for patients with pituitary adenomas and fostering greater consistency in care across institutions. G.J. has served as a consultant for Novo Nordisk and AstraZeneca and received lecture fees from Novo Nordisk, Pharmanovia and Pfizer. The other authors declare no conflicts of interest.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».