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Our aim is to predict possible gross total and subtotal resections of skull meningiomas from pre-treatment T1 post contrast MR-images using radiomics and machine learning in a representative patient cohort. We analyse the accuracy of our model predictions depending on the tumor location within the skull and the postoperative tumor volume. In this retrospective, IRB-approved study, image segmentation of the contrast enhancing parts of the tumor was semi-automatically performed using the 3D Slicer open-source software platform. Imaging data were split into training data and independent test data at random. We extracted a total of 107 radiomic features by hand-delineated regions of interest on T1 post contrast MR images. Feature preselection and model construction were performed with eight different machine learning algorithms. Each model was estimated 100 times on new training data and then tested on a previously unknown, independent test data set to avoid possible overfitting. Our cohort included 138 patients. A gross total resection of the meningioma was performed in 107 cases and a subtotal resection in the remaining 31 cases. Using the training data, the mean area under the curve (AUC), mean accuracy, mean kappa, mean sensitivity and mean specificity were 0.901, 0.875, 0.629, 0.675 and 0.933 respectively. We obtained very similar results with the independent test data: mean AUC = 0.900, mean accuracy = 0.881, mean kappa = 0.644, mean sensitivity = 0.692 and mean specificity = 0.936. Thus, our model exposes good and stable predictive performance with both training and test data. Our radiomics approach shows that with machine learning algorithms and comparatively few explanatory factors such as the location of the tumor within the skull as well as its shape, it is possible to make accurate predictions about whether a meningioma can be completely resected by surgery. Complete resections and resections with larger postoperative tumor volumes can be predicted with very high accuracy. However, cases with very small postoperative tumor volumes are comparatively difficult to predict correctly.

The aim of this study was to develop a magnetic resonance imaging (MRI) based radiomics model to predict mitosis cycles in intracranial meningioma grading prior to surgery. Preoperative contrast-enhanced T1-weighted (T1CE) cerebral MRI data of 167 meningioma patients between 2015 and 2020 were obtained, preprocessed and segmented using the 3D Slicer software and the PyRadiomics plugin. In total 145 radiomics features of the T1CE MRI images were computed. The criterion on the basis of which the feature selection was made is whether the number of mitoses per 10 high power field (HPF) is greater than or equal to zero. Our analyses show that machine learning algorithms can be used to make accurate predictions about whether the number of mitoses per 10 HPF is greater than or equal to zero. We obtained our best model using Ridge regression for feature pre-selection, followed by stepwise logistic regression for final model construction. Using independent test data, this model resulted in an AUC (Area under the Curve) of 0.8523, an accuracy of 0.7941, a sensitivity of 0.8182, a specificity of 0.7500 and a Cohen’s Kappa of 0.5576. We analyzed the performance of this model as a function of the number of mitoses per 10 HPF. The model performs well for cases with zero mitoses as well as for cases with more than one mitosis per 10 HPF. The worst model performance (accuracy = 0.6250) is obtained for cases with one mitosis per 10 HPF. Our results show that MRI-based radiomics may be a promising approach to predict the mitosis cycles in intracranial meningioma prior to surgery. Specifically, our approach may offer a non-invasive means of detecting the early stages of a malignant process in meningiomas prior to the onset of clinical symptoms.

Introduction: Chronic pain after spinal surgery (CPSS), formerly known as failed back surgery syndrome, encompasses a variety of highly incapacitating chronic pain syndromes emerging after spinal surgery. The intractability of CPSS makes objective parameters that could aid classification and treatment essential. In this study, we investigated the use of cerebral diffusion-weighted magnetic resonance imaging. Methods: Cerebral 3T diffusion-weighted (DW-) MRI data from adult CPSS patients were assessed and compared with those of healthy controls matched by age and gender. Only imaging data without relevant artefacts or significant pathologies were included. Apparent diffusion coefficient (ADC) maps were calculated from the b0 and b1000 values using nonlinear regression. After skull stripping and affine registration of all imaging data, ADC values for fifteen anatomical regions were calculated and analyzed with independent samples T-tests. Results: A total of 32 subjects were included (sixteen CPSS patients and sixteen controls). The mean ADC value of the spinothalamic tract was found to be significantly higher in CPSS patients compared with in healthy controls (p = 0.013). The other anatomical regions did not show statistically different ADC values between the two groups. Conclusion: Our results suggest that patients suffering from CPSS are subject to microstructural changes, predominantly within the cerebral spinothalamic tract. Additional research could possibly lead to imaging biomarkers derived from ADC values in CPSS patients.

The clinical outcomes of Spinal Cord Stimulation (SCS) therapy in patients with a Failed Back Surgery Syndrome (FBSS) is mostly done by standardized pain and quality of life measurements instruments and hardly account for personal feelings and needs as a basis for a patient-centred approach and shared decision making. The objective of this study is to explore perspectives on personal health and quality of life (QoL) in FBSS patients concerning their physical-, psychological and spiritual well-being prior to receiving an SCS system. We performed face-to-face, semi-structured, in-depth interviews to obtain descriptive and detailed data on personal health, guided by the Web diagram of Positive Health (Huber et al.) and a topic list. The following main topics were assessed qualitatively: 1) Bodily functioning, 2) Mental function and perception 3) Spiritual dimension, 4) Quality of life, 5) Social and societal participation and 6) Daily functioning. Seventeen FBSS patients (eight male, nine female) were included from April-November 2019 at the department of pain medicine in the Albert Schweitzer Hospital in the Netherlands. Median age 49 years; range 28 to 67 years, and patients underwent between one and five lumbar surgical operations. The duration of their chronic pain was between four and 22 years. After analyzing the interviews, three themes emerged: 1) dealing with chronic pain, 2) the current situation regarding aspects of positive health, and 3) future perspectives on health and quality of life. These themes arose from eleven categories and a hundred ninety codes. This qualitative study explored FBSS patients 'views on their health and the ability to adapt to daily life having complex chronic pain, and showed that patients experienced shortcomings in daily life within the six dimensions of the Web diagram of Positive Health before the SCS implant.

Our aim is to define the capabilities of radiomics and machine learning in predicting pseudoprogression development from pre-treatment MR images in a patient cohort diagnosed with high grade gliomas. In this retrospective analysis, we analysed 131 patients with high grade gliomas. Segmentation of the contrast enhancing parts of the tumor before administration of radio-chemotherapy was semi-automatically performed using the 3D Slicer open-source software platform (version 4.10) on T1 post contrast MR images. Imaging data was split into training data, test data and an independent validation sample at random. We extracted a total of 107 radiomic features by hand-delineated regions of interest (ROI). Feature selection and model construction were performed using Generalized Boosted Regression Models (GBM). 131 patients were included, of which 64 patients had a histopathologically proven progressive disease and 67 were diagnosed with mixed or pure pseudoprogression after initial treatment. Our Radiomics approach is able to predict the occurrence of pseudoprogression with an AUC, mean sensitivity, mean specificity and mean accuracy of 91.49% [86.27%, 95.89%], 79.92% [73.08%, 87.55%], 88.61% [85.19%, 94.44%] and 84.35% [80.19%, 90.57%] in the full development group, 78.51% [75.27%, 82.46%], 66.26% [57.95%, 73.02%], 78.31% [70.48%, 84.19%] and 72.40% [68.06%, 76.85%] in the testing group and finally 72.87% [70.18%, 76.28%], 71.75% [62.29%, 75.00%], 80.00% [69.23%, 84.62%] and 76.04% [69.90%, 80.00%] in the independent validation sample, respectively. Our results indicate that radiomics is a promising tool to predict pseudo-progression, thus potentially allowing to reduce the use of biopsies and invasive histopathology.

Motor cortex stimulation (MCS) was introduced as a last-resort treatment for chronic neuropathic pain. Over the years, MCS has been used for the treatment of various pain syndromes but long-term follow-up is unknown. This paper reports the results of MCS from 2005 until 2012 with a 3-year follow-up. Patients who suffered from chronic neuropathic pain treated with MCS were studied. The analgesic effect was determined as successful by decrease in pain-intensity on the visual analog scale (VAS) of at least 40%. The modifications in drug regimens were monitored with use of the medication quantification scale (MQS). Stimulation parameters and complications were also noted. Interference of pain with quality of life (QoL), the Quality of Life Index (QLI), was determined with use of a specific subset of questions from the MPQ-DLV score. Eighteen patients were included. Mean pre-operative VAS changed from 89.4 ± 11.2 to 53.1 ± 25.0 after three years of follow-up (P < 0.0001). A successful outcome was achieved in seven responders (38.9%). All patients in the responder group suffered from pain caused by a central lesion. With regard to all the patients with central pain lesions (n = 10) and peripheral lesions (n = 8), a significant difference in response to MCS was noticed (P = 0.002). MQS scores and QLI-scores diminished during the follow-up period (P = 0.210 and P = 0.007, respectively). MCS seems a promising therapeutic option for patients with refractory pain syndromes of central origin.

Background Persistent postoperative low back pain, including persistent spinal pain syndrome type 2 (PSPS-T2), is a global healthcare challenge that lacks objective phenotypic diagnostic criteria or validated biomarkers. Molecular imaging techniques (nuclear medicine) could aid in establishing more objective phenotypical parameters as they are able to visualize biological processes underlying several diseases even before anatomical changes are present. This review aims to provide an overview of the status quo of molecular imaging for the diagnosis of PSPS-T2. Method and results An extensive search of PubMed and Embase was conducted to identify relevant studies that comprise imaging techniques using a radiopharmaceutical substance. Evidence reveals that these techniques can provide valuable insights into the underlying pathologies and mechanisms of PSPS-T2 by detecting pain generators that would have otherwise gone unnoticed. Moreover, the meta-analysis showed a pooled sensitivity of 90.3% (95% CI: 53–100%) and a specificity of 89.1% (95% CI: 31–100%) for [ 18 F]NaF PET-CT, and a pooled sensitivity of 61.5% (95% CI: 7–93%) and specificity of 96% (95% CI: 21–100%) for diphosphonates SPECT-CT. Conclusion These findings suggest a potential utility in identifying those who are likely to benefit from surgical re-intervention. This illustrates the potential of molecular imaging in establishing a personalized-medicine approach. However, the retrospective design and the limited sample sizes are among the limitations of the included studies and further research is needed to unravel the potential of molecular imaging techniques as a tool to detect phenotypical biomarkers and to optimize patient care in patients with persistent postoperative low back pain, including PSPS-T2.

Abstract BACKGROUND Predicting outcome after aneurysmal subarachnoid hemorrhage (aSAH) is known to be challenging and complex. Machine learning approaches, of which feedforward artificial neural networks (ffANNs) are the most widely used, could contribute to the patient-specific outcome prediction. OBJECTIVE To investigate the prediction capacity of an ffANN for the patient-specific clinical outcome and the occurrence of delayed cerebral ischemia (DCI) and compare those results with the predictions of 2 internationally used scoring systems. METHODS A prospective database was used to predict (1) death during hospitalization (ie, mortality) (n = 451), (2) unfavorable modified Rankin Scale (mRS) at 6 mo (n = 413), and (3) the occurrence of DCI (n = 362). Additionally, the predictive capacities of the ffANN were compared to those of Subarachnoid Haemorrhage International Trialists (SAHIT) and VASOGRADE to predict clinical outcome and occurrence of DCI. RESULTS The area under the curve (AUC) of the ffANN showed to be 88%, 85%, and 72% for predicting mortality, an unfavorable mRS, and the occurrence of DCI, respectively. Sensitivity/specificity rates of the ffANN for mortality, unfavorable mRS, and the occurrence of DCI were 82%/80%, 94%/80%, and 74%/68%. The ffANN and SAHIT calculator showed similar AUCs for predicting personalized outcome. The presented ffANN and VASOGRADE were found to perform equally with regard to personalized prediction of occurrence of DCI. CONCLUSION The presented ffANN showed equal performance when compared with VASOGRADE and SAHIT scoring systems while using less individual cases. The web interface launched simultaneously with the publication of this manuscript allows for usage of the ffANN-based prediction tool for individual data (https://nutshell-tool.com/).

Background/Objectives: Diagnosis of Persistent Spinal Pain Syndrome Type 2 (PSPS-T2) currently lacks objective biomarkers. Therefore, this retrospective study aimed to investigate differences in glucose metabolism in the axial musculoskeletal system in PSPS-T2 patients by means of [18F]FDG PET-CT imaging. Methods: Nine PSPS-T2 patients (five females, four males; mean age of 53 ± 4.82 years) and nine age- and gender-matched healthy controls (five females, four males; mean age of 53 ± 3.91 years) were included. For each participant, 24 regions of interest (ROIs) were manually drawn, including areas of the vertebral endplates, the intervertebral discs, and the psoas muscles. For each ROI, the mean standardized uptake values (SUVs) were assessed. Group differences were evaluated using repeated measures ANOVA with Bonferroni-adjusted post-hoc pairwise comparisons. Additionally, Pearson correlation analyses examined associations between SUVmean values and the Numerical Rating Scale (NRS) pain scores. Results: Results demonstrated significantly higher SUVmean values in healthy controls compared to PSPS-T2 patients, particularly at the superior endplates of L4 and S1, the intervertebral discs at L4-L5 and L5-S1, and the posterior endplates of L4 and L5. Although PSPS-T2 patients exhibited higher SUVmean values than controls in the psoas muscle, these differences were not statistically significant. Additionally, no significant correlations were found between SUVmean values and NRS pain scores, suggesting that metabolic activity alone does not directly reflect pain severity. Conclusions: Despite the limited sample size of this pilot study, the metabolic fingerprint of the axial musculoskeletal system was shown to be distinctly different in PSPS-T2 patients compared to healthy controls. This could lead to an improved understanding of PSPS-T2 pathophysiology and might open new doors for better diagnosis and treatment strategies.

Distinguishing treatment-related abnormalities (TRA) from tumor progression (TP) in glioblastoma patients is a diagnostic imaging challenge due to the identical morphology of conventional MR imaging sequences. Diffusion-weighted imaging (DWI) and its derived images of the apparent diffusion coefficient (ADC) have been suggested as diagnostic tools for this problem. The aim of this study is to determine the diagnostic accuracy of different cut-off values of the ADC to differentiate between TP and TRA. In total, 76 post-treatment glioblastoma patients with new contrast-enhancing lesions were selected. Lesions were segmented using a T1-weighted, contrast-enhanced scan. The mean ADC values of the segmentations were compared between TRA and TP groups. Diagnostic accuracy was compared by use of the area under the curve (AUC) and the derived sensitivity and specificity values from cutoff points. Although ADC values in TP (mean = 1.32 × 10−3 mm2/s; SD = 0.31 × 10−3 mm2/s) were significantly different compared to TRA (mean = 1.53 × 10−3 mm2/s; SD = 0.28 × 10−3 mm2/s) (p = 0.003), considerable overlap in their distributions exists. The AUC of ADC values to distinguish TP from TRA was 0.71, with a sensitivity and specificity of 65% and 70%, respectively, at an ADC value of 1.47 × 10−3 mm2/s. These findings therefore indicate that ADC maps should not be used in discerning between TP and TRA at a certain timepoint without information on temporal evolution.