How well do neurosurgeons predict survival in patients with high-grade glioma?

Due to the lack of reliable prognostic tools, prognostication and surgical decisions largely rely on the neurosurgeons’ clinical prediction skills. The aim of this study was to assess the accuracy of neurosurgeons’ prediction of survival in patients with high-grade glioma and explore factors possibly associated with accurate predictions. In a prospective single-center study, 199 patients who underwent surgery for high-grade glioma were included. After surgery, the operating surgeon predicted the patient’s survival using an ordinal prediction scale. A survival curve was used to visualize actual survival in groups based on this scale, and the accuracy of clinical prediction was assessed by comparing predicted and actual survival. To investigate factors possibly associated with accurate estimation, a binary logistic regression analysis was performed. The surgeons were able to diferentiate between patients with diferent lengths of survival, and median survival fell within the predicted range in all groups with predicted survival24 months, median survival was shorter than predicted. The overall accuracy of surgeons’ survival estimates was 41%, and over- and underestimations were done in 34% and 26%, respectively. Consultants were 3.4 times more likely to accurately predict survival compared to residents (p=0.006). Our fndings demonstrate that although especially experienced neurosurgeons have rather good predictive abilities when estimating survival in patients with high-grade glioma on the group level, they often miss on the individual level. Future prognostic tools should aim to beat the presented clinical prediction skills.publishedVersio

A novel federated deep learning scheme for glioma and its subtype classification

Background:\ua0Deep learning (DL) has shown promising results in molecular-based classification of glioma subtypes from MR images. DL requires a large number of training data for achieving good generalization performance. Since brain tumor datasets are usually small in size, combination of such datasets from different hospitals are needed. Data privacy issue from hospitals often poses a constraint on such a practice. Federated learning (FL) has gained much attention lately as it trains a central DL model without requiring data sharing from different hospitals.Method:\ua0We propose a novel 3D FL scheme for glioma and its molecular subtype classification. In the scheme, a slice-based DL classifier, EtFedDyn, is exploited which is an extension of FedDyn, with the key differences on using focal loss cost function to tackle severe class imbalances in the datasets, and on multi-stream network to exploit MRIs in different modalities. By combining EtFedDyn with domain mapping as the pre-processing and 3D scan-based post-processing, the proposed scheme makes 3D brain scan-based classification on datasets from different dataset owners. To examine whether the FL scheme could replace the central learning (CL) one, we then compare the classification performance between the proposed FL and the corresponding CL schemes. Furthermore, detailed empirical-based analysis were also conducted to exam the effect of using domain mapping, 3D scan-based post-processing, different cost functions and different FL schemes.Results:\ua0Experiments were done on two case studies: classification of glioma subtypes (IDH mutation and wild-type on TCGA and US datasets in case A) and glioma grades (high/low grade glioma HGG and LGG on MICCAI dataset in case B). The proposed FL scheme has obtained good performance on the test sets (85.46%, 75.56%) for IDH subtypes and (89.28%, 90.72%) for glioma LGG/HGG all averaged on five runs. Comparing with the corresponding CL scheme, the drop in test accuracy from the proposed FL scheme is small (−1.17%, −0.83%), indicating its good potential to replace the CL scheme. Furthermore, the empirically tests have shown that an increased classification test accuracy by applying: domain mapping (0.4%, 1.85%) in case A; focal loss function (1.66%, 3.25%) in case A and (1.19%, 1.85%) in case B; 3D post-processing (2.11%, 2.23%) in case A and (1.81%, 2.39%) in case B and EtFedDyn over FedAvg classifier (1.05%, 1.55%) in case A and (1.23%, 1.81%) in case B with fast convergence, which all contributed to the improvement of overall performance in the proposed FL scheme.Conclusion:\ua0The proposed FL scheme is shown to be effective in predicting glioma and its subtypes by using MR images from test sets, with great potential of replacing the conventional CL approaches for training deep networks. This could help hospitals to maintain their data privacy, while using a federated trained classifier with nearly similar performance as that from a centrally trained one. Further detailed experiments have shown that different parts in the proposed 3D FL scheme, such as domain mapping (make datasets more uniform) and post-processing (scan-based classification), are essential

Clinical Course in Chronic Subdural Hematoma Patients Aged 18–49 Compared to Patients 50 Years and Above: A Multicenter Study and Meta-Analysis

Objective: Chronic Subdural Hematoma (cSDH) is primarily a disease of elderly, and is rare in patients <50 years, and this may in part be related to the increased brain atrophy from 50 years of age. This fact may also influence clinical presentation and outcome. The aim of this study was to study the clinical course with emphasis on clinical presentation of cSDH patients in the young (<50 years).Methods: A retrospective review of a population-based cohort of 1,252 patients operated for cSDH from three Scandinavian neurosurgical centers was conducted. The primary end-point was difference in clinical presentation between the patients <50 y/o and the remaining patients (≥50 y/o group). The secondary end-points were differences in perioperative morbidity, recurrence and mortality between the two groups. In addition, a meta-analysis was performed comparing clinical patterns of cSDH in the two age groups.Results: Fifty-two patients (4.2%) were younger than 50 years. Younger patients were more likely to present with headache (86.5% vs. 37.9%, p < 0.001) and vomiting (25% vs. 5.2%, p < 0.001) than the patients ≥50 y/o, while the ≥50 y/o group more often presented with limb weakness (17.3% vs. 44.8%, p < 0.001), speech impairment (5.8% vs. 26.2%, p = 0.001) and gait disturbance or falls (23.1% vs. 50.7%, p < 0.001). There was no difference between the two groups in recurrence, overall complication rate and mortality within 90 days. Our meta-analysis confirmed that younger patients are more likely to present with headache (p = 0.015) while the hemispheric symptoms are more likely in patients ≥50 y/o (p < 0.001).Conclusion: Younger patients with cSDH present more often with signs of increased intracranial pressure, while those ≥50 y/o more often present with hemispheric symptoms. No difference exists between the two groups in terms of recurrence, morbidity, and short-term mortality. Knowledge of variations in clinical presentation is important for correct and timely diagnosis in younger cSDH patients

Clinical outcomes and safety assessment in elderly patients undergoing decompressive laminectomy for lumbar spinal stenosis: a prospective study

<p>Abstract</p> <p>Background</p> <p>To assess safety, risk factors and clinical outcomes in elderly patients with spinal stenosis after decompressive laminectomy.</p> <p>Methods</p> <p>A prospective cohort of patients 70 years and older with spinal stenosis undergoing conventional laminectomy without fusion (n = 101) were consecutively enrolled from regular clinical practice and reassessed at 3 and 12 months. Primary outcome was change in health related quality of life measured (HRQL) with EuroQol-5 D (EQ-5D). Secondary outcomes were safety assessment, changes in Oswestry disability index (ODI), Visual Analogue Scale (EQ-VAS) score for self reported health, VAS score for leg and back pain and patient satisfaction. We used regression analyses to evaluate risk factors for less improvement.</p> <p>Results</p> <p>The mean EQ-5 D total score were 0.32, 0.63 and 0.60 at baseline, 3 months and 12 months respectively, and represents a statistically significant (P < 0.001) improvement. Effect size was > 0.8. Mean ODI score at baseline was 44.2, at 3 months 25.6 and at 27.9. This represents an improvement for all post-operative scores. A total of 18 (18.0%) complications were registered with 6 (6.0%) classified as major, including one perioperative death. Patients stating that the surgery had been beneficial at 3 months was 82 (89.1%) and at 12 months 73 (86.9%). The only predictor found was patients with longer duration of leg pain had less improvement in ODI (P < 0.001). Increased age or having complications did not predict a worse outcome in any of the outcome variables.</p> <p>Conclusions</p> <p>Properly selected patients of 70 years and older can expect a clinical meaningful improvement of HRQL, functional status and pain after open laminectomy without fusion. The treatment seems to be safe. However, patients with longstanding leg-pain prior to operation are less likely to improve one year after surgery.</p

A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas

In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for segmentation by using ellipse box areas surrounding the tumors. In the proposed method, the deep network is trained by using a large number of unannotated tumor images with foreground (FG) and background (BG) ellipse box areas surrounding the tumor and background, and a small number of patients (&lt;20) with annotated tumors. The training is conducted by initial training on two ellipse boxes on unannotated MRIs, followed by refined training on a small number of annotated MRIs. We use a multi-stream U-Net for conducting our experiments, which is an extension of the conventional U-Net. This enables the use of complementary information from multi-modality (e.g., T1, T1ce, T2, and FLAIR) MRIs. To test the feasibility of the proposed approach, experiments and evaluation were conducted on two datasets for glioma segmentation. Segmentation performance on the test sets is then compared with those used on the same network but trained entirely by annotated MRIs. Our experiments show that the proposed method has obtained good tumor segmentation results on the test sets, wherein the dice score on tumor areas is (0.8407, 0.9104), and segmentation accuracy on tumor areas is (83.88%, 88.47%) for the MICCAI BraTS’17 and US datasets, respectively. Comparing the segmented results by using the network trained by all annotated tumors, the drop in the segmentation performance from the proposed approach is (0.0594, 0.0159) in the dice score, and (8.78%, 2.61%) in segmented tumor accuracy for MICCAI and US test sets, which is relatively small. Our case studies have demonstrated that training the network for segmentation by using ellipse box areas in place of all annotated tumors is feasible, and can be considered as an alternative, which is a trade-off between saving medical experts’ time annotating tumors and a small drop in segmentation performance

Prediction of glioma‑subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors

Background: For brain tumors, identifying the molecular subtypes from magnetic resonance imaging (MRI) isdesirable, but remains a challenging task. Recent machine learning and deep learning (DL) approaches may help theclassification/prediction of tumor subtypes through MRIs. However, most of these methods require annotated datawith ground truth (GT) tumor areas manually drawn by medical experts. The manual annotation is a time consumingprocess with high demand on medical personnel. As an alternative automatic segmentation is often used. However, itdoes not guarantee the quality and could lead to improper or failed segmented boundaries due to differences in MRIacquisition parameters across imaging centers, as segmentation is an ill‑defined problem. Analogous to visual objecttracking and classification, this paper shifts the paradigm by training a classifier using tumor bounding box areas inMR images. The aim of our study is to see whether it is possible to replace GT tumor areas by tumor bounding boxareas (e.g. ellipse shaped boxes) for classification without a significant drop in performance.Method: In patients with diffuse gliomas, training a deep learning classifier for subtype prediction by employ‑ing tumor regions of interest (ROIs) using ellipse bounding box versus manual annotated data. Experiments wereconducted on two datasets (US and TCGA) consisting of multi‑modality MRI scans where the US dataset containedpatients with diffuse low‑grade gliomas (dLGG) exclusively.Results: Prediction rates were obtained on 2 test datasets: 69.86% for 1p/19q codeletion status on US dataset and79.50% for IDH mutation/wild‑type on TCGA dataset. Comparisons with that of using annotated GT tumor data fortraining showed an average of 3.0% degradation (2.92% for 1p/19q codeletion status and 3.23% for IDH genotype).Conclusion: Using tumor ROIs, i.e., ellipse bounding box tumor areas to replace annotated GT tumor areas for train‑ing a deep learning scheme, cause only a modest decline in performance in terms of subtype prediction. With moredata that can be made available, this may be a reasonable trade‑off where decline in performance may be counter‑acted with more data

The Risk of Getting Worse: Predictors of Deterioration After Decompressive Surgery for Lumbar Spinal Stenosis: A Multicenter Observational Study

ObjectiveTo investigate the frequency and predictors of deterioration after decompressive surgery for single and 2-level lumbar spinal stenosis.MethodsProspectively collected data were retrieved from the Norwegian Registry for Spine Surgery. Clinically significant deterioration was defined as an 8-point increase in Oswestry disability index (ODI) between baseline and 12 months' follow-up.ResultsThere were 2181 patients enrolled in the study. Of 1735 patients with complete 12 months follow-up, 151 (8.7%) patients reported deterioration. The following variables were significantly associated with deterioration at 12 months' follow-up; decreasing age (odds ratio [OR] 1.02, 95% confidence interval [95% CI] 1.00–1.04, P = 0.046), tobacco smoking (OR 2.10, 95% CI 1.42–3.22, P = 0.000), American Society of Anesthesiologists grade ≥3 (OR 1.80, 95% CI 1.07–2.94, P = 0.025), decreasing preoperative ODI (OR 1.05, 95% CI 1.02–1.07, P = 0.000), previous surgery at the same level (OR 2.00, 95% CI 1.18–3.27, P = 0.009), and previous surgery at other lumbar levels (OR 2.10, 95% CI 1.19–3.53, P = 0.009).ConclusionsOverall risk of clinically significant deterioration in patient-reported pain and disability after decompressive surgery for lumbar spinal stenosis is approximately 9%. Predictors for deterioration are decreasing age, current tobacco smoking, American Society of Anesthesiologists grade ≥3, decreasing preoperative ODI, and previous surgery at same or different lumbar level. We suggest that these predictors should be emphasized and discussed with the patients before surgery

Spatial distribution of malignant transformation in patients with low-grade glioma

Background Malignant transformation represents the natural evolution of diffuse low-grade gliomas (LGG). This is a catastrophic event, causing neurocognitive symptoms, intensified treatment and premature death. However, little is known concerning the spatial distribution of malignant transformation in patients with LGG. Materials and methods Patients histopathological diagnosed with LGG and subsequent radiological malignant transformation were identified from two different institutions. We evaluated the spatial distribution of malignant transformation with (1) visual inspection and (2) segmentations of longitudinal tumor volumes. In (1) a radiological transformation site < 2 cm from the tumor on preceding MRI was defined local transformation. In (2) overlap with pretreatment volume after importation into a common space was defined as local transformation. With a centroid model we explored if there were particular patterns of transformations within relevant subgroups. Results We included 43 patients in the clinical evaluation, and 36 patients had MRIs scans available for longitudinal segmentations. Prior to malignant transformation, residual radiological tumor volumes were > 10 ml in 93% of patients. The transformation site was considered local in 91% of patients by clinical assessment. Patients treated with radiotherapy prior to transformation had somewhat lower rate of local transformations (83%). Based upon the segmentations, the transformation was local in 92%. We did not observe any particular pattern of transformations in examined molecular subgroups. Conclusion Malignant transformation occurs locally and within the T2w hyperintensities in most patients. Although LGG is an infiltrating disease, this data conceptually strengthens the role of loco-regional treatments in patients with LGG.publishedVersio

Postoperative Deterioration in Health Related Quality of Life as Predictor for Survival in Patients with Glioblastoma: A Prospective Study

BACKGROUND: Studies indicate that acquired deficits negatively affect patients' self-reported health related quality of life (HRQOL) and survival, but the impact of HRQOL deterioration after surgery on survival has not been explored. OBJECTIVE: Assess if change in HRQOL after surgery is a predictor for survival in patients with glioblastoma. METHODS: Sixty-one patients with glioblastoma were included. The majority of patients (n = 56, 91.8%) were operated using a neuronavigation system which utilizes 3D preoperative MRI and updated intraoperative 3D ultrasound volumes to guide resection. HRQOL was assessed using EuroQol 5D (EQ-5D), a generic instrument. HRQOL data were collected 1-3 days preoperatively and after 6 weeks. The mean change in EQ-5D index was -0.05 (95% CI -0.15-0.05) 6 weeks after surgery (p = 0.285). There were 30 patients (49.2%) reporting deterioration 6 weeks after surgery. In a Cox multivariate survival analysis we evaluated deterioration in HRQOL after surgery together with established risk factors (age, preoperative condition, radiotherapy, temozolomide and extent of resection). RESULTS: There were significant independent associations between survival and use of temozolomide (HR 0.30, p = 0.019), radiotherapy (HR 0.26, p = 0.030), and deterioration in HRQOL after surgery (HR 2.02, p = 0.045). Inclusion of surgically acquired deficits in the model did not alter the conclusion. CONCLUSION: Early deterioration in HRQOL after surgery is independently and markedly associated with impaired survival in patients with glioblastoma. Deterioration in patient reported HRQOL after surgery is a meaningful outcome in surgical neuro-oncology, as the measure reflects both the burden of symptoms and treatment hazards and is linked to overall survival