Tumor treating fields in glioblastoma: long-term treatment and high compliance as favorable prognostic factors.

Introduction: Tumor treating fields (TTFields) have earned substantial attention in recent years as a novel therapeutic approach with the potential to improve the prognosis of glioblastoma (GBM) patients. However, the impact of TTFields remains a subject of ongoing debate. This study aimed to offer real-world evidence on TTFields therapy for GBM, and to investigate the clinical determinants affecting its efficacy. Methods: We have reported a retrospective analysis of 81 newly diagnosed Chinese GBM patients who received TTFields/Stupp treatment in the Second Affiliated Hospital of Zhejiang University. Overall survival (OS) and progression-free survival (PFS) were analyzed using Kaplan-Meier method. Cox regression models with time-dependent covariates were utilized to address non-proportional hazards and to assess the influence of clinical variables on PFS and OS. Results: The median PFS and OS following TTFields/STUPP treatment was 12.6 months (95% CI 11.0-14.1) and 21.3 months (95% CI 10.0-32.6) respectively. Long-term TTFields treatment (>2 months) exhibits significant improvements in PFS and OS compared to the short-term treatment group (≤2 months). Time-dependent covariate COX analysis revealed that longer TTFields treatment was correlated with enhanced PFS and OS for up to 12 and 13 months, respectively. Higher compliance to TTFields (≥ 0.8) significantly reduced the death risk (HR=0.297, 95%CI 0.108-0.819). Complete surgical resection and MGMT promoter methylation were associated with significantly lower risk of progression (HR=0.337, 95% CI 0.176-0.643; HR=0.156, 95% CI 0.065-0.378) and death (HR=0.276, 95% CI 0.105-0.727; HR=0.249, 95% CI 0.087-0.710). Conclusion: The TTFields/Stupp treatment may prolong median OS and PFS in GBM patients, with long-term TTFields treatment, higher TTFields compliance, complete surgical resection, and MGMT promoter methylation significantly improving prognosis.

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas.

PURPOSE: Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. METHODS: Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. RESULTS: After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71-0.84, 95% CI) based on T1-weighted images, 0.79 (0.72-0.84, 95% CI) for T2-weighted images, 0.88 (0.83-0.92, 95% CI) for CE-T1 images, and 0.88 (0.83-0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66-0.78, 95% CI), 0.84 (0.78-0.89, 95% CI) based on T2-WI, 0.74 (0.67-0.80, 95% CI) for CE-T1, and 0.81 (0.76-0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. CONCLUSIONS: CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

Predicted factors of surgical site infection in glioblastoma patients: A meta-analysis.

Surgical site infection (SSI) is one of the common postoperative complications after craniotomy for glioblastoma patients. Previous studies have investigated the risk factors for SSI in patients with glioblastoma. Whereas big differences in research results exist, and the correlation coefficients of different research results are quite different. A meta-analysis was conducted to examine the risk factors related to surgical site infection in patients with glioblastoma. We searched English databases to collect case-control studies or cohort studies published before 15 October 2023 including PubMed, Web of Science, Embase. The risk of bias of the included studies was assessed via Newcastle-Ottawa Scale. The analysis was performed using RevMan 5.4.1 tool. A total of 4 articles (n = 2222) were selected in this meta-analysis. The following risk factors were presented to be correlated with SSI in glioblastoma: irradiation (OR = 1.88, 95% CI [0.46, 7.60]), more than 3 surgeries (OR = 2.99, 95% CI [1.47, 6.08]). Occurrence of SSI is influenced by a variety of factors. Thus, we should pay close attention to high-risk subjects and take crucial targeted interventions to lower the SSI risk following craniotomy. Owing to the limited quality and quantity of the included studies, more rigorous studies with adequate sample sizes are needed to verify the conclusion.

Diagnostic performance evaluation of adult Chiari malformation type I based on convolutional neural networks.

PURPOSE: This study aimed to evaluate the diagnostic performance of convolutional neural network (CNN) models in Chiari malformation type I (CMI) and to verify whether CNNs can identify the morphological features of the craniocervical junction region between patients with CMI and healthy controls (HCs). To date, numerous indicators based on manual measurements are used for the diagnosis of CMI. However, the corresponding postoperative efficacy and prognostic evaluations have remained inconsistent. From a diagnostic perspective, CNN models may be used to explore the relationship between the clinical features and image morphological parameters. METHODS: This study included a total of 148 patients diagnosed with CMI at our institution and 205 HCs were included. T1-weighted sagittal magnetic resonance imaging (MRI) images were used for the analysis. A total of 220 and 355 slices were acquired from 98 patients with CMI and 155 HCs, respectively, to train and validate the CNN models. In addition, median sagittal images obtained from 50 patients with CMI and 50 HCs were selected to test the models. We applied original cervical MRI images (CI) and images of posterior cranial fossa and craniocervical junction area (CVI) to train the CI- and CVI-based CNN models. Transfer learning and data augmentation were used for model construction and each model was retrained 10 times. RESULTS: Both the CI- and CVI-based CNN models achieved high diagnostic accuracy. In the validation dataset, the models had diagnostic accuracy of 100% and 97% (p = 0.005), sensitivity of 100% and 98% (p = 0.016), and specificity of 100% (p = 0.929), respectively. In the test dataset, the accuracy was 97% and 96% (p = 0.25), sensitivity was 97% and 92% (p = 0.109), and specificity was 100% (p = 0.123), respectively. For patients with cerebellar subungual herniation less than 5 mm, three out of the 10 CVI-based retrained models reached 100% sensitivity. CONCLUSIONS: Our results revealed that the CNN models demonstrated excellent diagnostic performance for CMI. The models had higher sensitivity than the application of cerebellar tonsillar herniation alone and could identify features in the posterior cranial fossa and craniocervical junction area of patients. Our preliminary experiments provided a feasible method for the diagnosis and study of CMI using CNN models. However, further studies are needed to identify the morphologic characteristics of patients with different clinical outcomes, as well as patients who may benefit from surgery.