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Abstract In this multi-center, assessor-blinded pilot study, the diagnostic efficacy of cCeLL-Ex vivo, a second-generation confocal laser endomicroscopy (CLE), was compared against the gold standard frozen section analysis for intraoperative brain tumor diagnosis. The study was conducted across three tertiary medical institutions in the Republic of Korea. Biopsy samples from newly diagnosed brain tumor patients were categorized based on location and divided for permanent section analysis, frozen section analysis, and cCeLL-Ex vivo imaging. Of the 74 samples from 55 patients, the majority were from the tumor core (74.3%). cCeLL-Ex vivo exhibited a relatively higher diagnostic accuracy (89.2%) than frozen section analysis (86.5%), with both methods showing a sensitivity of 92.2%. cCeLL-Ex vivo also demonstrated higher specificity (70% vs. 50%), positive predictive value (PPV) (95.2% vs. 92.2%), and negative predictive value (NPV) (58.3% vs. 50%). Furthermore, the time from sample preparation to diagnosis was notably shorter with cCeLL-Ex vivo (13 min 17 s) compared to frozen section analysis (28 min 28 s) (p-value

Abstract Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgical resection. Unfortunately, it is challenging to distinguish non-enhancing tumors from edema in the non-enhancing T2 hyperintensity areas using conventional MRI alone. Quantitative DCE MRI parameters such as Ktrans and Ve convey permeability information of glioblastomas that cannot be provided by conventional MRI. We used the publicly available nnU-Net to train a deep learning model that incorporated both conventional and DCE MRI to detect the subtle difference in vessel leakiness due to neoangiogenesis between the non-recurrence area and the local recurrence area, which contains a higher proportion of high-grade glioma cells. We found that the addition of Ve doubled the sensitivity while nonsignificantly decreasing the specificity for prediction of local recurrence in glioblastomas, which implies that the combined model may result in fewer missed cases of local recurrence. The deep learning model predictive of local recurrence may enable risk-adapted radiotherapy planning in patients with grade 4 adult-type diffuse gliomas.

Abstract Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.

Abstract This study aimed to find any ambiguous genetic outlier for “oligodendroglioma, IDH-mutant and 1p/19q-codeleted (O_IDH_mut)” and “astrocytoma, IDH-mutant (A_IDH_mut)” and to redefine the genetic landscape and prognostic factors of IDH-mutant gliomas. Next-generation sequencing (NGS) using a brain tumor-targeted gene panel, methylation profiles, and clinicopathological features were analyzed for O_IDH_mut (n = 74) in 70 patients and for A_IDH_mut (n = 95) in 90 patients. 97.3% of O_IDH_mut and 98.9% of A_IDH_mut displayed a classic genomic landscape. Combined CIC (75.7%) and/or FUBP1 (45.9%) mutations were detected in 93.2% and MGMTp methylation in 95.9% of O_IDH_mut patients. In A_IDH_mut, TP53 mutations were found in 86.3% and combined ATRX (82.1%) and TERTp (6.3%) mutations in 88.4%. Although there were 3 confusing cases, NOS (not otherwise specified) category, based on genetic profiles, but they were clearly classified by combining histopathology and DKFZ methylation classifier algorithms. The patients with MYCN amplification and/or CDKN2A/2B homozygous deletion in the A_IDH_mut category had a worse prognosis than those without these gene alterations and MYCN-amplified A_IDH_mut showed the worst prognosis. However, there was no prognostic genetic marker in O_IDH_mut. In histopathologically or genetically ambiguous cases, methylation profiles can be used as an objective tool to avoid a diagnosis of NOS or NEC (not elsewhere classified), as well as for tumor classification. The authors have not encountered a case of true mixed oligoastrocytoma using an integrated diagnosis of histopathological, genetic and methylation profiles. MYCN amplification, in addition to CDKN2A/2B homozygous deletion, should be included in the genetic criteria for CNS WHO grade 4 A_IDH_mut.

Abstract Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903–1.000) for local recurrence; 0.864 (0.726–0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or “radiomic risk score”, increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.