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Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathologists. Here we sought to develop and validate a deep learning model capable of precisely distinguishing PCNSL from non-PCNSL lesions, especially glioma, using hematoxylin and eosin (H&E)-stained frozen whole-slide images. Also, we compared its performance against pathologists of varying expertise. Additionally, a human-machine fusion approach integrated both model and pathologic diagnostics. In external cohorts, LGNet achieved AUROCs of 0.965 and 0.972 in distinguishing PCNSL from glioma and AUROCs of 0.981 and 0.993 in differentiating PCNSL from non-PCNSL lesions. Outperforming several pathologists, LGNet significantly improved diagnostic performance, further augmented to some extent by fusion approach. LGNet's proficiency in frozen section analysis and its synergy with pathologists indicate its valuable role in intraoperative diagnosis, particularly in discriminating PCNSL from glioma, alongside other lesions. Correct diagnosis of primary central nervous system lymphoma is key in determining treatment, however, this depends on pathology analysis. Here, the authors develop a deep learning method to diagnose primary nervous system lymphoma from stained whole-slide images. [ABSTRACT FROM AUTHOR]

Infant‐type hemispheric glioma, a new subtype of pediatric high‐grade glioma, arises in the cerebral hemispheres. Despite better survival outcomes, the treatment of infant‐type hemispheric glioma is still facing challenges. Here, we reported a case of QKI‐ALK fusion, infant‐type hemispheric glioma with lung metastasis who achieved a complete clinical response after lorlatinib treatment. This typical case demonstrated the importance of appropriate molecularly targeted treatments in ALK‐fused tumors, and lorlatinib may serve as an effective complement to conventional chemotherapy and radiotherapy in primary glioma harboring ALK fusions and its metastasis. [ABSTRACT FROM AUTHOR]

Background: Diffuse hemispheric glioma H3 G34-mutant (G34-DHG) is a new type of pediatric-type diffuse high-grade glioma in the fifth edition of the WHO Classification of Tumors of the Central Nervous System. The current treatment for G34-DHG involves a combination of surgery and conventional radiotherapy or chemotherapy; however, the therapeutic efficacy of this approach is not satisfactory. In recent years, molecular targeted therapy and immunotherapy have achieved significant benefits in a variety of tumors. In-depth understanding of molecular changes and immune infiltration in G34-DHGs will help to establish personalized tumor treatment strategies. Here, we report the clinicopathological, molecular and immune infiltration characteristics of G34-DHG cases from our center along with cases from the HERBY Trial and the Chinese Glioma Genome Atlas database (CGGA).Methods: Hematoxylin-eosin (HE) and immunohistochemistry (IHC) staining were used to present the clinicopathological characteristics of 10 Chinese G34-DHG patients treated at our institution. To address the molecular characteristics of G34-DHG, we performed whole-exome sequencing (WES) and RNA sequencing (RNA-seq) analyses of 5 patients from our center and 3 Chinese patients from the Chinese Glioma Genome Atlas (CGGA) database. Additionally, 7 European G34-DHG patients from the HERBY Trail were also subjected to analyses, with 7 cases of WES data and 2 cases of RNA-seq data. Six G34-DHG patients from another organization were used as external validation.Results: WES showed a high frequency of PDGFRA mutation in G34-DHGs (12/15). We further identified frequent mutations in MUC family genes in G34-DHGs, including MUC16 (8/15) and MUC17 (8/15). Although no statistical difference was found, PDGFRA mutation tended to be an indicator for worse prognosis whereas MUC16/MUC17 mutation indicated a favorable prognosis in G34-DHGs. RNA sequencing results revealed that most G34-DHG are considered to be immune cold tumors. However, one patient in our cohort with MUC16 mutation showed significant immune infiltration, and the total overall survival of this patient reached 75 months.Conclusions: Our results demonstrate that G34-DHG is a new high-grade glioma with high frequency of PDGFRA and MUC gene family mutations. PDGFRA may serve as an indicator of poor prognosis and an effective therapeutic target. Moreover, MUC16 tends to be a favorable prognostic factor and indicates high immune infiltration in certain patients, and these findings may provide a new direction for targeted therapy and immunotherapy of patients with G34-DHGs. [ABSTRACT FROM AUTHOR]

CHI3L2 (Chitinase-3-Like Protein 2) is a member of chitinase-like proteins (CLPs), which belong to the glycoside hydrolase 18 family. Its homologous gene, CHI3L1, has been extensively studied in various tumors and has been shown to be related to immune infiltration in breast cancer and glioblastoma. High CHI3L2 expression was reported to be associated with poor prognosis in breast cancer and renal cell carcinoma. However, the prognostic significance of CHI3L2 in glioma and its correlation between immune infiltration remains unclear. In this study, we examined 288 glioma samples by immunohistochemistry to find that CHI3L2 is expressed in tumor cells and macrophages in glioma tissues and highly expressed in glioblastoma and IDH wild-type gliomas. Relationships between CHI3L2 expression and clinical features (grade, age, Ki67 index, P53, PHH3 (mitotic figures), ATRX, TERTp, MGMTp, IDH, and 1p/19q co-deleted status) were evaluated. Kaplan-Meier survival was conducted to show high CHI3L2 expression in tumor cells (TC) and macrophage cells (MC) indicated poor prognosis in diffusely infiltrating glioma (DIG), lower-grade glioma (LGG), and IDH wild-type gliomas (IDH-wt). The overall survival time was higher in patients with dual-low CHI3L2 expression in TC and MC compared to those in patients with non-dual CHI3L2 expression and dual high expression in DIG and IDH wild-type gliomas. By univariate and multivariate analysis, we found that high CHI3L2 expression in tumor cells was an independent unfavorable prognostic factor in glioma patients. Moreover, we used two datasets (TCGA and CGGA) to verify the results of our study and explore the potential functional role of CHI3L2 by GO and KEGG analyses in gliomas. TIMER platform analysis indicated CHI3L2 expression was closely related to diverse marker genes of tumor immune infiltrating cells, including monocytes, TAMs, M1 macrophages, M2 macrophages, TGFβ1+ Treg and T cell exhaustion in GBM and LGG. Western Blot validated CHI3L2 is expressed in glioma cells and microglia cells. The results of flow cytometry showed that CHI3L2 induces the apoptosis of CD8+ T cells. In conclusion, these results demonstrate CHI3L2 is related to poor prognosis and immune infiltrates in gliomas, suggesting it may serve as a promising prognostic biomarker and represent a new target for glioma patients. [ABSTRACT FROM AUTHOR]

Purpose: H3 K27M-mutant diffuse midline gliomas are associated with worse prognosis than H3 K27M wild-type gliomas. In the present study, we sought to evaluate the conventional magnetic resonance imaging (cMRI) of H3 K27M-mutant glioma and examine whether diffusion-weighted imaging (DWI) derived apparent diffusion coefficient (ADC) could noninvasively predict H3 K27M mutational status in brain diffuse midline gliomas.Materials and Methods: The institutional review board approved this study and waived the requirement for informed consent. Thirty-eight patients with brain diffuse midline gliomas were retrospectively reviewed. The parameters of preoperative cMRI were evaluated. The minimal ADC, peritumoral ADC, ratio of minimal ADC, and ratio of peritumoral ADC were measured, and significant differences between the two groups were identified by logistic regression analysis adjusted for age and tumor location. Receiver operating characteristic curves and logistic regression analysis adjusted for age and tumor location were used to assess the diagnostic performances of the minimal ADC, peritumoral ADC, ratio of minimal ADC, and ratio of peritumoral ADC.Results: H3 K27M-mutant gliomas in different locations have diverse imaging characteristics. Minimal ADC, peritumoral ADC, ratio of minimal ADC, and ratio of peritumoral ADC values were significantly lower in the H3 K27M-mutant gliomas than in the wild-type gliomas (P < 0.05). The combination of ratio of minimal ADC and ratio of peritumoral ADC provided the largest area under the curve (AUC) of 0.872 in defining H3 K27M-mutational status.Conclusions: The combination of ratio of minimal ADC and ratio of peritumoral ADC can noninvasively detect the H3 K27M mutational status in brain diffuse midline gliomas. [ABSTRACT FROM AUTHOR]

To construct an optimal prognostic model to assess the prognosis of patients with diffuse glioma. Preoperative magnetic resonance imaging and clinical data were retrospectively collected from 266 patients (training cohort: validation cohort = 7:3) with pathologically confirmed diffuse gliomas. A radiomics prognostic model (R-model) based on the radiomics features was constructed. A prognostic model based on clinical factors (C-model) and a fusion model (F-model) was also constructed. Based on the optimal model of three models, the nomogram was constructed. Finally, a "Prognosis Calculator for Diffuse Glioma" was constructed based on the nomogram. The c-index of the R-, C-, and F-models in the validation cohort was 0.742, 0.796, and 0.814, respectively. In the validation cohort, the 1-year area under the curve of the R-, C-, and F-models was 0.749, 0.806, and 0.836, respectively; the 3-year area under the curve was 0.896, 0.966, and 0.963, respectively. In the training cohort, validation cohort, all cohorts, and different grades of glioma cohorts, F-model (optimal model) could identify low- and high-risk groups well. The "Prognosis Calculator for Diffuse Glioma" was available at https://github.com/HDCurry/prognosis. Among the three models, the F-model (radiomics combined with clinical factors) had optimal predictive efficacy and could more accurately assess the prognosis of diffuse glioma. The "Prognosis Calculator for Diffuse Glioma" constructed based on this model could assist clinicians in more easily and accurately assessing the prognosis of patients with diffuse glioma, thus enabling them to make more reasonable treatment strategies. [ABSTRACT FROM AUTHOR]