Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy

Ahmed Mekki (1, 2, 3, 4) , Laurent Dercle (5, 3, 6, 7) , Philip Lichtenstein (6) , Ghaida Nasser (4) , Aurélien Marabelle (8) , Stéphane Champiat (8) , Emilie Chouzenoux (9) , Corinne Balleyguier (10) , Samy Ammari (2, 3)

1 STF - Systèmes Tolérants aux Fautes
2 Département d'imagerie médicale [Gustave Roussy]
3 Université Paris-Saclay
4 Department of Neuroradiology
5 IGR - Institut Gustave Roussy
6 Departement of Radiology [New York]
7 ITIC - Immunologie anti-tumorale et immunothérapie des cancers
8 DITEP - Département d’Innovation Thérapeutique et essais précoces [Gustave Roussy]
9 OPIS - OPtimisation Imagerie et Santé
10 Imagerie diagnostique (radiologie / échographie)

Purpose : New-onset pituitary gland lesions are observed in up to 18% of cancer patients undergoing treatment with immune checkpoint blockers (ICB). We aimed to develop and validate an imaging-based decision-making algorithm for use by the clinician that helps differentiate pituitary metastasis (PM) from ICB-induced autoimmune hypophysitis (HP). Materials and methods : A systematic search was performed in the MEDLINE and EMBASE databases up to October 2018 to identify studies concerning PM and HP in patients treated with cytotoxic T–lymphocyte–associated protein 4 and programmed cell death (ligand) 1. The reference standard for diagnosis was confirmation by histology or response on follow-up imaging. Patients from included studies were randomly assigned to the training set or the validation set. Using machine learning (random forest tree algorithm) with the most-described six imaging and three clinical features, a multivariable prediction model (the signature) was developed and validated for diagnosing PM. Signature performance was evaluated using area under a receiver operating characteristic curves (AUCs).Results : Out of 3174 screened articles, 65 were included totalising 122 patients (HP: 60 pts, PM: 62 pts). Complete radiological data were available in 82 pts (Training: 62 pts, Validation: 20 pts). The signature reached an AUC = 0.91 (0.82, 1.00), P < 10−8 in the training set and AUC = 0.94 (0.80, 1.00), P = 0.001 in the validation set. The signature predicted PM in lesions either ≥ 2 cm in size or < 2 cm if associated with heterogeneous contrast enhancement and cavernous extension.Conclusion : An image-based signature was developed with machine learning and validated for differentiating PM from HP. This tool could be used by clinicians for enhanced decision-making in cancer patients undergoing ICB treatment with new-onset, concerning lesions of the pituitary gland.

Mots clés: Immune-related adverse events; CTLA-4; PD-1; Hypophisitis; Machine learning

Domaines: Traitement du signal et de l'image [eess.SP]; Intelligence artificielle [cs.AI]

Ahmed Mekki, Laurent Dercle, Philip Lichtenstein, Ghaida Nasser, Aurélien Marabelle, et al.. Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy. European Journal of Cancer, 2019, 119, pp.44-56. ⟨10.1016/j.ejca.2019.06.020⟩. ⟨hal-02269518⟩