Confirmatory clinical trial evaluating diagnostic performance of confocal laser endomicroscopy (cCeLL - Ex vivo) for intraoperative brain tumor diagnosis

초록

cCeLL-Ex vivo is a second-generation confocal laser endomicroscopy (CLE) platform that uses indocyanine green (ICG) as a fluorescent dye. This multicenter, assessor-blinded, prospective confirmatory trial (KCT0008869) comprehensively evaluated the diagnostic efficacy of CLE compared to frozen section (FS) analysis for intraoperative brain tumor diagnosis. In parallel, an early-stage AI diagnostic algorithm was developed using CLE images to support tumor classification. The trial was conducted from October 2022 to December 2024 across four tertiary medical centers in Korea and Canada. Patients aged ≥19 years undergoing surgery for newly diagnosed brain tumors were enrolled. The primary endpoint was to evaluate the diagnostic accuracy and prove non-inferiority of CLE compared to FS. Secondary endpoints included comparison of sensitivity, specificity, diagnostic accuracy by sample location, diagnostic turnaround time, interobserver variability, and tumor subtype classification. An AI model was developed using a Swin Transformer-based hierarchical framework. A total of 461 samples from 376 patients (mean age: 55.6 years; 162 males) were analyzed. Most samples were obtained from the tumor core (n=382, 82.8%). Meningioma (n=121, 26.2%) and glioma (n=106, 23.0%) were the most common tumor types. CLE demonstrated non-inferior diagnostic accuracy to FS (0.92 vs. 0.94; P=0.14), with comparable sensitivity (95% vs. 96%; P=0.404) and specificity (79% vs. 68%; P=0.307). Accuracy at the tumor margin was also comparable (CLE: 0.72 vs. FS: 0.68; P=0.831). CLE had a significantly faster diagnostic turnaround (8m12s vs. 23m38s; P<0.001). Interobserver agreement between neuropathologists was excellent (κ=0.805). The AI algorithm achieved 97% diagnostic accuracy (sensitivity: 98.9%, specificity: 84.6%) and 88.6% accuracy for tumor classification. Overall, CLE demonstrated non-inferior diagnostic efficacy with a significantly faster diagnostic turnaround than FS. Integration of AI with CLE may further assist in making faster and more accurate intraoperative decisions.