Purpose: Five-aminolevulinic acid (5-ALA) is widely used as an intraoperative fluorescent probe for radical resection of high-grade glioma, and thus aids in extending progression-free survival of patients. However, there exist some cases where 5-ALA fails to fluoresce. In some other cases, it may undergo fluorescence quenching but cannot be orally readministered during surgery. This study aimed to develop a novel hydroxymethyl rhodamine green (HMRG)-based fluorescence labeling system that can be repeatedly administered as a topical spray during surgery for the detection of glioblastoma. Experimental Design: We performed a three-stage probe screening using tumor lysates and fresh tumor tissues with our probe library consisting of a variety of HMRG probes with different dipeptides. We then performed proteome and transcript expression analyses to detect candidate enzymes responsible for cleaving the probe. Moreover, in vitro and ex vivo studies using U87 glioblastoma cell line were conducted to validate the findings. Results: The probe screening identified proline-arginine–HMRG (PR-HMRG) as the optimal probe that distinguished tumors from peritumoral tissues. Proteome analysis identified calpain-1 (CAPN1) to be responsible for cleaving the probe. CAPN1 was highly expressed in tumor tissues which reacted to the PR-HMRG probe. Knockdown of this enzyme suppressed fluorescence intensity in U87 glioblastoma cells. In situ assay using a mouse U87 xenograft model demonstrated marked contrast of fluorescence with the probe between the tumor and peritumoral tissues. Conclusions: The novel fluorescent probe PR-HMRG is effective in detecting glioblastoma when applied topically. Further investigations are warranted to assess the efficacy and safety of its clinical use.

Chordoma and chondrosarcoma share common radiographic characteristics yet are distinct clinically. A radiomic machine learning model differentiating these tumors preoperatively would help plan surgery. MR images were acquired from 57 consecutive patients with chordoma (N = 32) or chondrosarcoma (N = 25) treated at the University of Tokyo Hospital between September 2012 and February 2020. Preoperative T1-weighted images with gadolinium enhancement (GdT1) and T2-weighted images were analyzed. Datasets from the first 47 cases were used for model creation, and those from the subsequent 10 cases were used for validation. Feature extraction was performed semi-automatically, and 2438 features were obtained per image sequence. Machine learning models with logistic regression and a support vector machine were created. The model with the highest accuracy incorporated seven features extracted from GdT1 in the logistic regression. The average area under the curve was 0.93 ± 0.06, and accuracy was 0.90 (9/10) in the validation dataset. The same validation dataset was assessed by 20 board-certified neurosurgeons. Diagnostic accuracy ranged from 0.50 to 0.80 (median 0.60, 95% confidence interval 0.60 ± 0.06%), which was inferior to that of the machine learning model (p = 0.03), although there are some limitations, such as the risk of overfitting and the lack of an extramural cohort for truly independent final validation. In summary, we created a novel MRI-based machine learning model to differentiate skull base chordoma and chondrosarcoma from multiparametric signatures.

Human coenurosis is caused by the larval stages of Taenia species, mainly Taenia multiceps and Taenia serialis. T. multiceps has been reported to cause human central nervous system (CNS) infections, but no CNS case caused by T. serialis has been reported. The authors report the first case of human neurocoenurosis caused by T. serialis, which was confirmed by mitochondrial DNA analysis. A 38-year-old man presented with visual disturbance and headache, and subsequent magnetic resonance imaging (MRI) revealed a ring-enhancing cystic lesion in the left occipital lobe. Biopsy was performed, and the resultant histopathological diagnosis was that of low-grade B-cell lymphoma. Chemotherapy was initiated, but a subsequent MRI showed increased ring enhancement. Due to the unexpected clinical course, a surgical resection of the lesion was performed. The lesion was completely removed. Pathological examination showed multiple scolices with hooklets, suckers, and numerous calcareous corpuscles. Therefore, the diagnosis of neurocysticercosis was made. However, mitochondrial DNA analysis showed that the disease was definitively coenurosis caused by T. serialis. Albendazole was administered, with no evidence of recurrence at 12 months following the operation. In this study, we demonstrate that T. serialis can cause CNS infection and that genetic analysis is recommended to establish a definitive diagnosis.

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Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.