In a retrospective study reported in The Lancet Oncology, Lalchungnunga et al tested the classification accuracy of a molecular inference–based artificial intelligence (AI) model (Neuropath-AI) in central nervous system (CNS) tumor diagnosis.
Study Details
The multi-institutional study included data from whole slide images of samples from patients (aged 0–95 years) diagnosed with primary or recurrent CNS tumors. Reference diagnostic labels were determined using DNA methylation-based tumor classification to match 1 of 52 tumor types representing most types within classes of gliomas, embryonal tumors, and meningeal and mesenchymal tumors found in clinical practice. The Neuropath-AI model was trained using 5,835 samples from the U.S. National Cancer Institute (NCI), U.S. Children’s Brain Tumor Network, and Austrian Digital Brain Tumour Atlas to infer molecular features that were used to predict tumor types. The test cohort consisted of 5,516 samples identified between May 2024 and May 2025 from NCI, U.S. Northwestern Medicine, University of Pittsburgh Medical Center, and University College London. The primary objective was to assess the classification accuracy of the model family–level and terminal classification predictions in test samples.
Key Findings
In the test cohort, tumor family–level classifications were obtained in 5,299 (96%) of 5,516 samples. Predictions of placement in one of the terminal classifications with at least moderate confidence were obtained for 4,772 (87%) samples.
The single highest-scoring classification matched the reference label in 3,817 (95% confidence interval [CI] = 3,770–3,865; 80%, 95% CI = 79%–81%) of 4,772 samples (balanced accuracy [accuracy across all classes] = 66%, 95% CI = 63%–70%). The two highest-scoring classifications contained the reference label in 4,103 (95% CI = 4,056–4,152; 86%, 95% CI = 85%–87%) of 4,772 samples (balanced accuracy = 75%, 95% CI = 71%–78%).
The investigators concluded: “Our model provides the basis for a clinically applicable deep-learning assistant to improve human efficiency and accuracy of CNS tumour diagnosis. The model will be made publicly available and could be implemented to assist human pathologists in future prospective studies.”
Kenneth Aldape, MD, of the Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, is the corresponding author for The Lancet Oncology article.
DISCLOSURE: The study was funded by The Intramural Research Program of the National Institutes of Health. For full disclosures of the study authors, visit thelancet.com.
