Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation.

Autor: Santinha J; Computational Clinical Imaging Group, Champalimaud Research , Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal. joao.santinha@research.fchampalimaud.org.; Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal. joao.santinha@research.fchampalimaud.org., Katsaros V; Department of Radiology, General Anti-Cancer and Oncological Hospital of Athens, St. Savvas, Athens, Greece., Stranjalis G; Department of Neurosurgery, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece.; Hellenic Center for Neurosurgical Research 'Prof. Petros Kokkalis', Athens, Greece.; Athens Microneurosurgery Laboratory, Athens, Greece., Liouta E; Department of Neurosurgery, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece.; Hellenic Center for Neurosurgical Research 'Prof. Petros Kokkalis', Athens, Greece., Boskos C; Athens Microneurosurgery Laboratory, Athens, Greece.; IATROPOLIS CyberKnife Center, Hellenic Neuro-Oncology Society, Chalandri, Greece., Matos C; Radiology Department, Champalimaud Clinical Centre, Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal., Viegas C; Department of Neurosurgery, Hospital Garcia de Orta, Almada, Portugal., Papanikolaou N; Computational Clinical Imaging Group, Champalimaud Research , Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal.
Jazyk: angličtina
Zdroj: Journal of imaging informatics in medicine [J Imaging Inform Med] 2024 Feb; Vol. 37 (1), pp. 31-44. Date of Electronic Publication: 2024 Jan 12.
DOI: 10.1007/s10278-023-00918-6
Abstrakt: Radiogenomics has shown potential to predict genomic phenotypes from medical images. The development of models using standard-of-care pre-operative MRI images, as opposed to advanced MRI images, enables a broader reach of such models. In this work, a radiogenomics model for IDH mutation status prediction from standard-of-care MRIs in patients with glioma was developed and validated using multicentric data. A cohort of 142 (wild-type: 32.4%) patients with glioma retrieved from the TCIA/TCGA was used to train a logistic regression model to predict the IDH mutation status. The model was evaluated using retrospective data collected in two distinct hospitals, comprising 36 (wild-type: 63.9%) and 53 (wild-type: 75.5%) patients. Model development utilized ROC analysis. Model discrimination and calibration were used for validation. The model yielded an AUC of 0.741 vs. 0.716 vs. 0.938, a sensitivity of 0.784 vs. 0.739 vs. 0.875, and a specificity of 0.657 vs. 0.692 vs. 1.000 on the training, test cohort 1, and test cohort 2, respectively. The assessment of model fairness suggested an unbiased model for age and sex, and calibration tests showed a p < 0.05. These results indicate that the developed model allows the prediction of the IDH mutation status in gliomas using standard-of-care MRI images and does not appear to hold sex and age biases.
(© 2024. The Author(s).)
Databáze: MEDLINE
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