European journal of cancer (Oxford, England : 1990) · 2019
Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy.
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Mekki Ahmed, Dercle Laurent, Lichtenstein Philip, Nasser Ghaida, Marabelle Aurélien, Champiat Stéphane, Chouzenoux Emilie, Balleyguier Corinne, Ammari Samy
Аннотация
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.
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.