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Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.
This prospective study sought to evaluate potential savings of radiation dose to medical staff using real-time dosimetry coupled with visual radiation dose feedback during angiographic interventions. For this purpose, we analyzed a total of 214 angiographic examinations that consisted of chemoembolizations and several other types of therapeutic interventions. The Unfors RaySafe i2 dosimeter was worn by the interventionalist at chest height over the lead protection. A total of 110 interventions were performed with real-time radiation dosimetry allowing the interventionalist to react upon higher x-ray exposure and 104 examinations served as the comparative group without real-time radiation monitoring. By using the real-time display during interventions, the overall mean operator radiation dose decreased from 3.67 (IQR, 0.95–23.01) to 2.36 μSv (IQR, 0.52–12.66) (−36%; p = 0.032) at simultaneously reduced operator exposure time by 4.5 min (p = 0.071). Dividing interventions into chemoembolizations and other types of therapeutic interventions, radiation dose decreased from 1.31 (IQR, 0.46-3.62) to 0.95 μSv (IQR, 0.53-3.11) and from 24.39 (IQR, 12.14-63.0) to 10.37 μSv (IQR, 0.85-36.84), respectively, using live-screen dosimetry (p ≤ 0.005). Radiation dose reductions were also observed for the participating assistants, indicating that they could also benefit from real-time visual feedback dosimetry during interventions (−30%; p = 0.039). Integration of real-time dosimetry into clinical processes might be useful in reducing occupational radiation exposure time during angiographic interventions. The real-time visual feedback raised the awareness of interventionalists and their assistants to the potential danger of prolonged radiation exposure leading to the adoption of radiation-sparing practices. Therefore, it might create a safer environment for the medical staff by keeping the applied radiation exposure as low as possible.
Background: To test the impact of urethral sphincter length (USL) and anatomic variants of prostatic apex (Lee-type classification) in preoperative multiparametric magnet resonance imaging (mpMRI) on mid-term continence in prostate cancer patients treated with radical prostatectomy (RP). Methods: We relied on an institutional tertiary-care database to identify patients who underwent RP between 03/2018 and 12/2019 with preoperative mpMRI and data available on mid-term (>6 months post-surgery) urinary continence, defined as usage 0/1 (-safety) pad/24 h. Univariable and multivariable logistic regression models were fitted to test for predictor status of USL and prostatic apex variants, defined in mpMRI measurements. Results: Of 68 eligible patients, rate of mid-term urinary continence was 81% (n = 55). Median coronal (15.1 vs. 12.5 mm) and sagittal (15.4 vs. 11.1 mm) USL were longer in patients reporting urinary continence in mid-term follow-up (both p < 0.01). No difference was recorded for prostatic apex variants distribution (Lee-type) between continent vs. incontinent patients (p = 0.4). In separate multivariable logistic regression models, coronal (odds ratio (OR): 1.35) and sagittal (OR: 1.67) USL, but not Lee-type, were independent predictors for mid-term continence. Conclusion: USL, but not apex anatomy, in preoperative mpMRI was associated with higher rates of urinary continence at mid-term follow-up.
Highlights
• MRI and ultrasound provided significant correlations between findings suggestive of vasculitis and the final diagnosis.
• Careful selection of available imaging techniques is warranted considering the time course, location, and clinical history.
• Considering its moderate diagnostic power to distinguish tracer uptake, a holistic view of PET/CT findings is essential.
Abstract
Purpose: To assess the diagnostic value of different imaging modalities in distinguishing systemic vasculitis from other internal and immunological diseases.
Methods: This retrospective study included 134 patients with suspected vasculitis who underwent ultrasound, magnetic resonance imaging (MRI), or 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) between 01/2010 and 01/2019, finally consisting of 70 individuals with vasculitis. The main study parameter was the confirmation of the diagnosis using one of the three different imaging modalities, with the adjudicated clinical and histopathological diagnosis as the gold standard. A secondary parameter was the morphological appearance of the vessel affected by vasculitis.
Results: Patients with systemic vasculitis had myriad clinical manifestations with joint pain as the most common symptom. We found significant correlations between different imaging findings suggestive of vasculitis and the final adjudicated clinical diagnosis. In this context, on MRI, vessel wall thickening, edema, and diameter differed significantly between vasculitis and non-vasculitis groups (p < 0.05). Ultrasound revealed different findings that may serve as red flags in identifying patients with vasculitis, such as vascular occlusion or halo sign (p = 0.02 vs. non-vasculitis group). Interestingly, comparing maximal standardized uptake values from PET/CT examinations with vessel wall thickening or vessel diameter did not result in significant differences (p > 0.05).
Conclusions: We observed significant correlations between different imaging findings suggestive of vasculitis on ultrasound or MRI and the final adjudicated diagnosis. While ultrasound and MRI were considered suitable imaging methods for detecting and discriminating typical vascular changes, 18F-FDG PET/CT requires careful timing and patient selection given its moderate diagnostic accuracy.
Purpose: To identify transjugular intrahepatic portosystemic shunt (TIPS) thrombosis in abdominal CT scans applying quantitative image analysis.
Materials and methods: We retrospectively screened 184 patients to include 20 patients (male, 8; female, 12; mean age, 60.7 ± 8.87 years) with (case, n = 10) and without (control, n = 10) in-TIPS thrombosis who underwent clinically indicated contrast-enhanced and unenhanced abdominal CT followed by conventional TIPS-angiography between 08/2014 and 06/2020. First, images were scored visually. Second, region of interest (ROI) based quantitative measurements of CT attenuation were performed in the inferior vena cava (IVC), portal vein and in four TIPS locations. Minimum, maximum and average Hounsfield unit (HU) values were used as absolute and relative quantitative features. We analyzed the features with univariate testing.
Results: Subjective scores identified in-TIPS thrombosis in contrast-enhanced scans with an accuracy of 0.667 – 0.833. Patients with in-TIPS thrombosis had significantly lower average (p < 0.001), minimum (p < 0.001) and maximum HU (p = 0.043) in contrast-enhanced images. The in-TIPS / IVC ratio in contrast-enhanced images was significantly lower in patients with in-TIPS thrombosis (p < 0.001). No significant differences were found for unenhanced images. Analyzing the visually most suspicious ROI with consecutive calculation of its ratio to the IVC, all patients with a ratio < 1 suffered from in-TIPS thrombosis (p < 0.001, sensitivity and specificity = 100%).
Conclusion: Quantitative analysis of abdominal CT scans facilitates the stratification of in-TIPS thrombosis. In contrast-enhanced scans, an in-TIPS / IVC ratio < 1 could non-invasively stratify all patients with in-TIPS thrombosis.
Higher grade meningiomas tend to recur. We aimed to evaluate protein levels of vascular endothelial growth factor (VEGF)-A with the VEGF-receptors 1-3 and the co-receptors Neuropilin (NRP)-1 and -2 in WHO grade II and III meningiomas to elucidate the rationale for targeted treatments. We investigated 232 specimens of 147 patients suffering from cranial meningioma, including recurrent tumors. Immunohistochemistry for VEGF-A, VEGFR-1-3, and NRP-1/-2 was performed on tissue micro arrays. We applied a semiquantitative score (staining intensity x frequency). VEGF-A, VEGFR-1-3, and NRP-1 were heterogeneously expressed. NRP-2 was mainly absent. We demonstrated a significant increase of VEGF-A levels on tumor cells in WHO grade III meningiomas (p = 0.0098). We found a positive correlation between expression levels of VEGF-A and VEGFR-1 on tumor cells and vessels (p < 0.0001). In addition, there was a positive correlation of VEGF-A and VEGFR-3 expression on tumor vessels (p = 0.0034). VEGFR-2 expression was positively associated with progression-free survival (p = 0.0340). VEGF-A on tumor cells was negatively correlated with overall survival (p = 0.0084). The VEGF-A-driven system of tumor angiogenesis might still present a suitable target for adjuvant therapy in malignant meningioma disease. However, its role in malignant tumor progression may not be as crucial as expected. The value of comprehensive testing of the ligand and all receptors prior to administration of anti-angiogenic therapy needs to be evaluated in clinical trials.
Rationale and objectives: The objective of this study was to analyze the role of dynamic magnetic resonance imaging (MRI) in patients who suffered from groin pain and whose physical examination and ultrasound returned inconclusive/indefinite results, as well as in patients receiving an ongoing assessment for a previous herniotomy.
Material and methods: For this study, 25 patients 14 women and 11 men were selected with a mean age of 41.6 years, including clinical complaints, such as groin pain and or a previous herniotomies. These patients underwent dynamic MRI. Reports were created by a radiology resident and a radiology consultant. Clinical and ultrasound documentation were compared to with imaging results from the MRI.
Results: The results of the dynamic MRI were negative for 23 patients (92%) and positive for two patients (8%). One patient suffered from an indirect hernia and one from a femoral hernia. A repeated hernia was an excluding for the preoperated patients with pain and ongoing assessment.
Conclusions: Dynamic MRI shows substantially higher diagnostic performance in exclusion of inguinal hernia, when compared to a physical examination and ultrasound. The examination can also be used in assessments to analyze the operation’s results.
Purpose: To test the effect of anatomic variants of the prostatic apex overlapping the membranous urethra (Lee type classification), as well as median urethral sphincter length (USL) in preoperative multiparametric magnetic resonance imaging (mpMRI) on the very early continence in open (ORP) and robotic-assisted radical prostatectomy (RARP) patients. Methods: In 128 consecutive patients (01/2018–12/2019), USL and the prostatic apex classified according to Lee types A–D in mpMRI prior to ORP or RARP were retrospectively analyzed. Uni- and multivariable logistic regression models were used to identify anatomic characteristics for very early continence rates, defined as urine loss of ≤ 1 g in the PAD-test. Results: Of 128 patients with mpMRI prior to surgery, 76 (59.4%) underwent RARP vs. 52 (40.6%) ORP. In total, median USL was 15, 15 and 10 mm in the sagittal, coronal and axial dimensions. After stratification according to very early continence in the PAD-test (≤ 1 g vs. > 1 g), continent patients had significantly more frequently Lee type D (71.4 vs. 54.4%) and C (14.3 vs. 7.6%, p = 0.03). In multivariable logistic regression models, the sagittal median USL (odds ratio [OR] 1.03) and Lee type C (OR: 7.0) and D (OR: 4.9) were independent predictors for achieving very early continence in the PAD-test. Conclusion: Patients’ individual anatomical characteristics in mpMRI prior to radical prostatectomy can be used to predict very early continence. Lee type C and D suggest being the most favorable anatomical characteristics. Moreover, longer sagittal median USL in mpMRI seems to improve very early continence rates.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.
Linking epigenetic signature and metabolic phenotype in IDH mutant and IDH wildtype diffuse glioma
(2020)
Aims: Changes in metabolism are known to contribute to tumour phenotypes. If and how metabolic alterations in brain tumours contribute to patient outcome is still poorly understood. Epigenetics impact metabolism and mitochondrial function. The aim of this study is a characterisation of metabolic features in molecular subgroups of isocitrate dehydrogenase mutant (IDHmut) and isocitrate dehydrogenase wildtype (IDHwt) gliomas. Methods: We employed DNA methylation pattern analyses with a special focus on metabolic genes, large-scale metabolism panel immunohistochemistry (IHC), qPCR-based determination of mitochondrial DNA copy number and immune cell content using IHC and deconvolution of DNA methylation data. We analysed molecularly characterised gliomas (n = 57) for in depth DNA methylation, a cohort of primary and recurrent gliomas (n = 22) for mitochondrial copy number and validated these results in a large glioma cohort (n = 293). Finally, we investigated the potential of metabolic markers in Bevacizumab (Bev)-treated gliomas (n = 29). Results: DNA methylation patterns of metabolic genes successfully distinguished the molecular subtypes of IDHmut and IDHwt gliomas. Promoter methylation of lactate dehydrogenase A negatively correlated with protein expression and was associated with IDHmut gliomas. Mitochondrial DNA copy number was increased in IDHmut tumours and did not change in recurrent tumours. Hierarchical clustering based on metabolism panel IHC revealed distinct subclasses of IDHmut and IDHwt gliomas with an impact on patient outcome. Further quantification of these markers allowed for the prediction of survival under anti-angiogenic therapy. Conclusion: A mitochondrial signature was associated with increased survival in all analyses, which could indicate tumour subgroups with specific metabolic vulnerabilities.
Background: To assess the potential of radiomic features to quantify components of blood in intraaortic vessels to non-invasively predict moderate-to-severe anemia in non-contrast enhanced CT scans. Methods: One hundred patients (median age, 69 years; range, 19–94 years) who received CT scans of the thoracolumbar spine and blood-testing for hemoglobin and hematocrit levels ± 24 h between 08/2018 and 11/2019 were retrospectively included. Intraaortic blood was segmented using a spherical volume of interest of 1 cm diameter with consecutive radiomic analysis applying PyRadiomics software. Feature selection was performed applying analysis of correlation and collinearity. The final feature set was obtained to differentiate moderate-to-severe anemia. Random forest machine learning was applied and predictive performance was assessed. A decision-tree was obtained to propose a cut-off value of CT Hounsfield units (HU). Results: High correlation with hemoglobin and hematocrit levels was shown for first-order radiomic features (p < 0.001 to p = 0.032). The top 3 features showed high correlation to hemoglobin values (p) and minimal collinearity (r) to the top ranked feature Median (p < 0.001), Energy (p = 0.002, r = 0.387), Minimum (p = 0.032, r = 0.437). Median (p < 0.001) and Minimum (p = 0.003) differed in moderate-to-severe anemia compared to non-anemic state. Median yielded superiority to the combination of Median and Minimum (p(AUC) = 0.015, p(precision) = 0.017, p(accuracy) = 0.612) in the predictive performance employing random forest analysis. A Median HU value ≤ 36.5 indicated moderate-to-severe anemia (accuracy = 0.90, precision = 0.80). Conclusions: First-order radiomic features correlate with hemoglobin levels and may be feasible for the prediction of moderate-to-severe anemia. High dimensional radiomic features did not aid augmenting the data in our exemplary use case of intraluminal blood component assessment.
Simple Summary: Early and accurate diagnosis of breast cancer that has spread to other organs and tissues is crucial, as therapeutic decisions and outcome expectations might change. Computed tomography (CT) is often used to detect breast cancer’s spread, but this method has its weaknesses. The computer-assisted technique “radiomics” extracts grey-level patterns, so-called radiomic features, from medical images, which may reflect underlying biological processes. Our retrospective study therefore evaluated whether breast cancer spread can be predicted by radiomic features derived from iodine maps, an application on a new generation of CT scanners visualizing tissue blood flow. Based on 77 patients with newly diagnosed breast cancer, we found that this approach might indeed predict cancer spread to other organs/tissues. In the future, radiomics may serve as an additional tool for cancer detection and risk assessment.
Abstract: Dual-energy CT (DECT) iodine maps enable quantification of iodine concentrations as a marker for tissue vascularization. We investigated whether iodine map radiomic features derived from staging DECT enable prediction of breast cancer metastatic status, and whether textural differ- ences exist between primary breast cancers and metastases. Seventy-seven treatment-naïve patients with biopsy-proven breast cancers were included retrospectively (41 non-metastatic, 36 metastatic). Radiomic features including first-, second-, and higher-order metrics as well as shape descriptors were extracted from volumes of interest on iodine maps. Following principal component analysis, a multilayer perceptron artificial neural network (MLP-NN) was used for classification (70% of cases for training, 30% validation). Histopathology served as reference standard. MLP-NN predicted metastatic status with AUCs of up to 0.94, and accuracies of up to 92.6 in the training and 82.6 in the validation datasets. The separation of primary tumor and metastatic tissue yielded AUCs of up to 0.87, with accuracies of up to 82.8 in the training, and 85.7 in the validation dataset. DECT iodine map-based radiomic signatures may therefore predict metastatic status in breast cancer patients. In addition, microstructural differences between primary and metastatic breast cancer tissue may be reflected by differences in DECT radiomic features.
Purpose: To stratify differences in visual semantic and quantitative imaging features in intensive care patients with nonspecific mastoid effusions versus patients with acute mastoiditis (AM) requiring surgical treatment. Methods: We included 48 patients (male, 28; female, 20; mean age, 59.5 ± 18.1 years) with mastoid opacification (AM, n = 24; control, n = 24) who underwent clinically indicated cerebral CT between 12/2007 and 07/2018 in this retrospective study. Semantic features described the extend and asymmetry of mastoid and middle-ear cavity opacification and complications like erosive changes. Minimum, maximum and mean Hounsfield unit (HU) values were obtained as quantitative features. We analyzed the features employing univariate testing. Results: Compared to intensive care patients, AM patients revealed asymmetric mastoid or middle-ear cavity opacification (likelihood-ratio (LR) < 0.001). Applying a dedicated threshold of the extent of opacification, AM patients reached significance levels of LR = 0.042 and 0.002 for mastoid and middle-ear cavity opacification. AM cases showed higher maximum and mean HU values (p = 0.009, p = 0.024). Conclusions: We revealed that the extent and asymmetry of mastoid and middle-ear cavity opacification differs significantly between AM patients and intensive care patients. Multicenter research is needed to expand our cohort and possibly pave the way to build a non-invasive predictive model for AM in the future.
Objective: To investigate the value of standard [digital rectal examination (DRE), PSA] and advanced (mpMRI, prostate biopsy) clinical evaluation for prostate cancer (PCa) detection in contemporary patients with clinical bladder outlet obstruction (BOO) scheduled for Holmium laser enucleation of the prostate (HoLEP).
Material and Methods: We retrospectively analyzed 397 patients, who were referred to our tertiary care laser center for HoLEP due to BOO between 11/2017 and 07/2020. Of those, 83 (20.7%) underwent further advanced clinical PCa evaluation with mpMRI and/or prostate biopsy due to elevated PSA and/or lowered PSA ratio and/or suspicious DRE. Logistic regression and binary regression tree models were applied to identify PCa in BOO patients.
Results: An mpMRI was conducted in 56 (66%) of 83 patients and revealed PIRADS 4/5 lesions in 14 (25%) patients. Subsequently, a combined systematic randomized and MRI-fusion biopsy was performed in 19 (23%) patients and revealed in PCa detection in four patients (5%). A randomized prostate biopsy was performed in 31 (37%) patients and revealed in PCa detection in three patients (4%). All seven patients (9%) with PCa detection underwent radical prostatectomy with 29% exhibiting non-organ confined disease. Incidental PCa after HoLEP (n = 76) was found in nine patients (12%) with advanced clinical PCa evaluation preoperatively. In univariable logistic regression analyses, PSA, fPSA ratio, and PSA density failed to identify patients with PCa detection. Conversely, patients with a lower International Prostate Symptom Score (IPSS) and PIRADs 4/5 lesion in mpMRI were at higher risk for PCa detection. In multivariable adjusted analyses, PIRADS 4/5 lesions were confirmed as an independent risk factor (OR 9.91, p = 0.04), while IPSS did not reach significance (p = 0.052).
Conclusion: In advanced clinical PCa evaluation mpMRI should be considered in patients with elevated total PSA or low fPSA ratio scheduled for BOO treatment with HoLEP. Patients with low IPSS or PIRADS 4/5 lesions in mpMRI are at highest risk for PCa detection. In patients with a history of two or more sets of negative prostate biopsies, advanced clinical PCa evaluation might be omitted.
Simple Summary
Seizures are among the most common symptoms of meningioma patients even after surgery. This study sought to identify risk factors for early and late seizures in meningioma patients and to evaluate a modified version of a score to predict postoperative seizures on an independent cohort. The data underline that there are distinct factors identifying patients with a high risk of postoperative seizures following meningioma surgery which has been already shown before. We could further show that the high proportion of 43% of postoperative seizures occur as late seizures which are more dangerous because they may happen out of hospital. The modified STAMPE2 score could predict postoperative seizures when reaching very high scores but was not generally transferable to our independent cohort.
Abstract
Seizures are among the most common symptoms of meningioma. This retrospective study sought to identify risk factors for early and late seizures in meningioma patients and to evaluate a modified STAMPE2 score. In 556 patients who underwent meningioma surgery, we correlated different risk factors with the occurrence of postoperative seizures. A modified STAMPE2 score was applied. Risk factors for preoperative seizures were edema (p = 0.039) and temporal location (p = 0.038). For postoperative seizures preoperative tumor size (p < 0.001), sensomotory deficit (p = 0.004) and sphenoid wing location (p = 0.032) were independent risk factors. In terms of postoperative status epilepticus; sphenoid wing location (p = 0.022), tumor volume (p = 0.045) and preoperative seizures (p < 0.001) were independent risk factors. Postoperative seizures lead to a KPS deterioration and thus an impaired quality of life (p < 0.001). Late seizures occurred in 43% of patients with postoperative seizures. The small sub-cohort of patients (2.7%) with a STAMPE2 score of more than six points had a significantly increased risk for seizures (p < 0.001, total risk 70%). We concluded that besides distinct risk factors, high scores of the modified STAMPE2 score could estimate the risk of postoperative seizures. However, it seems not transferable to our cohort
Impact of Docetaxel on blood-brain barrier function and formation of breast cancer brain metastases
(2019)
Background: Breast cancer (BC) is the most frequent malignant tumor in females and the 2nd most common cause of brain metastasis (BM), that are associated with a fatal prognosis. The increasing incidence from 10% up to 40% is due to more effective treatments of extracerebral sites with improved prognosis and increasing use of MRI in diagnostics. A frequently administered, potent chemotherapeutic group of drugs for BC treatment are taxanes usually used in the adjuvant and metastatic setting, which, however, have been suspected to be associated with a higher incidence of BM. The aim of our study was to experimentally analyze the impact of the taxane docetaxel (DTX) on brain metastasis formation, and to elucidate the underlying molecular mechanism.
Methods: A monocentric patient cohort was analyzed to determine the association of taxane treatment and BM formation. To identify the specific impact of DTX, a murine brain metastatic model upon intracardial injection of breast cancer cells was conducted. To approach the functional mechanism, dynamic contrast-enhanced MRI and electron microscopy of mice as well as in-vitro transendothelial electrical resistance (TEER) and tracer permeability assays using brain endothelial cells (EC) were carried out. PCR-based, immunohistochemical and immunoblotting analyses with additional RNA sequencing of murine and human ECs were performed to explore the molecular mechanisms by DTX treatment.
Results: Taxane treatment was associated with an increased rate of BM formation in the patient cohort and the murine metastatic model. Functional studies did not show unequivocal alterations of blood-brain barrier properties upon DTX treatment in-vivo, but in-vitro assays revealed a temporary DTX-related barrier disruption. We found disturbance of tubulin structure and upregulation of tight junction marker claudin-5 in ECs. Furthermore, upregulation of several members of the tubulin family and downregulation of tetraspanin-2 in both, murine and human ECs, was induced.
Conclusion: In summary, a higher incidence of BM was associated with prior taxane treatment in both a patient cohort and a murine mouse model. We could identify tubulin family members and tetraspanin-2 as potential contributors for the destabilization of the blood-brain barrier. Further analyses are needed to decipher the exact role of those alterations on tumor metastatic processes in the brain.
Despite multidisciplinary local and systemic therapeutic approaches, the prognosis for most patients with brain metastases is still dismal. The role of adaptive and innate anti-tumor response including the Human Leukocyte Antigen (HLA) machinery of antigen presentation is still unclear. We present data on the HLA class II-chaperone molecule CD74 in brain metastases and its impact on the HLA peptidome complexity.
We analyzed CD74 and HLA class II expression on tumor cells in a subset of 236 human brain metastases, primary tumors and peripheral metastases of different entities in association with clinical data including overall survival. Additionally, we assessed whole DNA methylome profiles including CD74 promoter methylation and differential methylation in 21 brain metastases. We analyzed the effects of a siRNA mediated CD74 knockdown on HLA-expression and HLA peptidome composition in a brain metastatic melanoma cell line.
We observed that CD74 expression on tumor cells is a strong positive prognostic marker in brain metastasis patients and positively associated with tumor-infiltrating T-lymphocytes (TILs). Whole DNA methylome analysis suggested that CD74 tumor cell expression might be regulated epigenetically via CD74 promoter methylation. CD74high and TILhigh tumors displayed a differential DNA methylation pattern with highest enrichment scores for antigen processing and presentation. Furthermore, CD74 knockdown in vitro lead to a reduction of HLA class II peptidome complexity, while HLA class I peptidome remained unaffected.
In summary, our results demonstrate that a functional HLA class II processing machinery in brain metastatic tumor cells, reflected by a high expression of CD74 and a complex tumor cell HLA peptidome, seems to be crucial for better patient prognosis.
Aim: The cytokine receptor tumor necrosis factor receptor superfamily member 9 (TNFRSF9) is mainly considered to be a co-stimulatory activation marker in hematopoietic cells. Several preclinical models have shown a dramatic beneficial effect of treatment approaches targeting TNFRSF9 with agonistic antibodies. However, preliminary clinical phase I/II studies were stopped after the occurrence of several severe deleterious side effects. In a previous study, it was demonstrated that TNFRSF9 was strongly expressed by reactive astrocytes in primary central nervous system (CNS) tumors, but was largely absent from tumor or inflammatory cells. The aim of the present study was to address the cellular source of TNFRSF9 expression in the setting of human melanoma brain metastasis, a highly immunogenic tumor with a prominent tropism to the CNS.
Methods: Melanoma brain metastasis was analyzed in a cohort of 78 patients by immunohistochemistry for TNFRSF9 and its expression was correlated with clinicopathological parameters including sex, age, survival, tumor size, number of tumor spots, and BRAF V600E expression status.
Results: Tumor necrosis factor receptor superfamily member 9 was frequently expressed independently on both melanoma and endothelial cells. In addition, TNFRSF9 was also present on smooth muscle cells of larger vessels and on a subset of lymphomonocytic tumor infiltrates. No association between TNFRSF9 expression and patient survival or other clinicopathological parameters was seen. Of note, several cases showed a gradual increase in TNFRSF9 expression on tumor cells with increasing distance from blood vessels, an observation that might be linked to hypoxia-driven TNFRSF9 expression in tumor cells.
Conclusion: The findings indicate that the cellular source of TNFRSF9 in melanoma brain metastasis largely exceeds the lymphomonocytic pool, and therefore further careful (re-) assessment of potential TNFRSF9 functions in cell types other than hematopoietic cells is needed. Furthermore, the hypothesis of hypoxia-driven TNFRSF9 expression in brain metastasis melanoma cells requires further functional testing.
Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.