aceA multimodal MRI-based deep learning model for non-invasive diagnosis of pineal region germinoma
PURPOSE: To develop a deep learning model for the preoperative, non-invasive identification of germinomas in the pineal region. This retrospective study included 114 patients with pathologically confirmed pineal region tumors. The cohort was randomly divided into a training set (n = 91) and a test set (n = 23). The training set was further partitioned into three folds for cross-validation. A convolutional neural network (CNN) enhanced with contrastive learning was used to extract discriminative features from ind…
A CNN with contrastive learning and mixed-attention fusion of multimodal MRI and demographic data achieved test set ACC 0.855 and AUC 0.919 for preoperative identification of pineal region germinoma, outperforming single-modality models.
Retrospective design with small single-cohort sample (114 total, 23 test) limits generalizability and requires external validation before clinical use.
Evidence
- Peer-reviewedNeuroradiology2026-08-17
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Truvace Impact Record TRV-2026-0825, v1: “A multimodal MRI-based deep learning model for non-invasive diagnosis of pineal region germinoma.” Truvace, 2026-08-18. /record/TRV-2026-0825 (accessed at citation time). sha256 398405cbba17cce0…
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