Resective, Ablative and Radiosurgical Interventions for Drug Resistant Mesial Temporal Lobe Epilepsy: A Systematic Review and Meta-Analysis of Outcomes

Objectives: One-third of individuals with focal epilepsy do not achieve seizure freedom despite best medical therapy. Mesial temporal lobe epilepsy (MTLE) is the most common form of drug resistant focal epilepsy. Surgery may lead to long-term seizure remission if the epileptogenic zone can be defined and safely removed or disconnected. We compare published outcomes following open surgical techniques, radiosurgery (SRS), laser interstitial thermal therapy (LITT) and radiofrequency ablation (RF-TC). / Methods: PRISMA systematic review was performed through structured searches of PubMed, Embase and Cochrane databases. Inclusion criteria encompassed studies of MTLE reporting seizure-free outcomes in ≥10 patients with ≥12 months follow-up. Due to variability in open surgical approaches, only comparative studies were included to minimize the risk of bias. Random effects meta-analysis was performed to calculate effects sizes and a pooled estimate of the probability of seizure freedom per person-year. A mixed effects linear regression model was performed to compare effect sizes between interventions. / Results: From 1,801 screened articles, 41 articles were included in the quantitative analysis. Open surgery included anterior temporal lobe resection as well as transcortical and trans-sylvian selective amygdalohippocampectomy. The pooled seizure-free rate per person-year was 0.72 (95% CI 0.66–0.79) with trans-sylvian selective amygdalohippocampectomy, 0.59 (95% CI 0.53–0.65) with LITT, 0.70 (95% CI 0.64–0.77) with anterior temporal lobe resection, 0.60 (95% CI 0.49–0.73) with transcortical selective amygdalohippocampectomy, 0.38 (95% CI 0.14–1.00) with RF-TC and 0.50 (95% CI 0.34–0.73) with SRS. Follow up duration and study sizes were limited with LITT and RF-TC. A mixed-effects linear regression model suggests significant differences between interventions, with LITT, ATLR and SAH demonstrating the largest effects estimates and RF-TC the lowest. / Conclusions: Overall, novel “minimally invasive” approaches are still comparatively less efficacious than open surgery. LITT shows promising seizure effectiveness, however follow-up durations are shorter for minimally invasive approaches so the durability of the outcomes cannot yet be assessed. Secondary outcome measures such as Neurological complications, neuropsychological outcome and interventional morbidity are poorly reported but are important considerations when deciding on first-line treatments

Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imaging Concordance

Background: Epilepsy affects 50 million people worldwide and a third are refractory to medication. If a discrete cerebral focus or network can be identified, neurosurgical resection can be curative. Most excisions are in the temporal-lobe, and are more likely to result in seizure-freedom than extra-temporal resections. However, less than half of patients undergoing surgery become entirely seizure-free. Localizing the epileptogenic-zone and individualized outcome predictions are difficult, requiring detailed evaluations at specialist centers. Methods: We used bespoke natural language processing to text-mine 3,800 electronic health records, from 309 epilepsy surgery patients, evaluated over a decade, of whom 126 remained entirely seizure-free. We investigated the diagnostic performances of machine learning models using set-of-semiology (SoS) with and without hippocampal sclerosis (HS) on MRI as features, using STARD criteria. Findings: Support Vector Classifiers (SVC) and Gradient Boosted (GB) decision trees were the best performing algorithms for temporal-lobe epileptogenic zone localization (cross-validated Matthews correlation coefficient (MCC) SVC 0.73 ± 0.25, balanced accuracy 0.81 ± 0.14, AUC 0.95 ± 0.05). Models that only used seizure semiology were not always better than internal benchmarks. The combination of multimodal features, however, enhanced performance metrics including MCC and normalized mutual information (NMI) compared to either alone (p < 0.0001). This combination of semiology and HS on MRI increased both cross-validated MCC and NMI by over 25% (NMI, SVC SoS: 0.35 ± 0.28 vs. SVC SoS+HS: 0.61 ± 0.27). Interpretation: Machine learning models using only the set of seizure semiology (SoS) cannot unequivocally perform better than benchmarks in temporal epileptogenic-zone localization. However, the combination of SoS with an imaging feature (HS) enhance epileptogenic lobe localization. We quantified this added NMI value to be 25% in absolute terms. Despite good performance in localization, no model was able to predict seizure-freedom better than benchmarks. The methods used are widely applicable, and the performance enhancements by combining other clinical, imaging and neurophysiological features could be similarly quantified. Multicenter studies are required to confirm generalizability. Funding: Wellcome/EPSRC Center for Interventional and Surgical Sciences (WEISS) (203145Z/16/Z)

Body Composition, Symptoms, and Survival in Advanced Cancer Patients Referred to a Phase I Service

Background: Body weight and body composition are relevant to the outcomes of cancer and antineoplastic therapy. However, their role in Phase I clinical trial patients is unknown. Methods: We reviewed symptom burden, body composition, and survival in 104 patients with advanced cancer referred to a Phase I oncology service. Symptom burden was analyzed using the MD Anderson Symptom Assessment Inventory(MDASI); body composition was evaluated utilizing computerized tomography(CT) images. A body mass index (BMI)$25 kg/m 2 was considered overweight. Sarcopenia, severe muscle depletion, was assessed using CT-based criteria. Results: Most patients were overweight (n = 65, 63$25 kg/m 2. Sarcopenic patients were older and less frequently African-American. Symptom burden did not differ among patients classified according to BMI and presence of sarcopenia. Median (95% confidence interval) survival (days) varied according to body composition: 215 (71–358) (BMI,25 kg/m 2; sarcopenic), 271 (99–443) (BMI,25 kg/m 2; non-sarcopenic), 484 (286–681) (BMI$25 kg/m 2; sarcopenic); 501 d (309–693) (BMI$25 kg/m 2; non-sarcopenic). Higher muscle index and gastrointestinal cancer diagnosis predicted longer survival in multivariate analysis after controlling for age, gender, performance status, and fat index. Conclusions: Patients referred to a Phase I clinic had a high frequency of sarcopenia and a BMI$25 kg/m 2, independent o

Mechanism-Based Screen Establishes Signalling Framework for DNA Damage-Associated G1 Checkpoint Response

DNA damage activates checkpoint controls which block progression of cells through the division cycle. Several different checkpoints exist that control transit at different positions in the cell cycle. A role for checkpoint activation in providing resistance of cells to genotoxic anticancer therapy, including chemotherapy and ionizing radiation, is widely recognized. Although the core molecular functions that execute different damage activated checkpoints are known, the signals that control checkpoint activation are far from understood. We used a kinome-spanning RNA interference screen to delineate signalling required for radiation-mediated retinoblastoma protein activation, the recognized executor of G1 checkpoint control. Our results corroborate the involvement of the p53 tumour suppressor (TP53) and its downstream targets p21CIP1/WAF1 but infer lack of involvement of canonical double strand break (DSB) recognition known for its role in activating TP53 in damaged cells. Instead our results predict signalling involving the known TP53 phosphorylating kinase PRPK/TP53RK and the JNK/p38MAPK activating kinase STK4/MST1, both hitherto unrecognised for their contribution to DNA damage G1 checkpoint signalling. Our results further predict a network topology whereby induction of p21CIP1/WAF1 is required but not sufficient to elicit checkpoint activation. Our experiments document a role of the kinases identified in radiation protection proposing their pharmacological inhibition as a potential strategy to increase radiation sensitivity in proliferating cancer cells

An accurate computation method based on artificial neural networks with different learning algorithms for resonant frequency of annular ring microstrip antennas

An annular ring compact microstrip antenna (ARCMA) constructed by loading a circular slot in the center of the circular patch antenna is a popular microstrip antenna due to its favorable properties. In this study, a method based artificial neural networks (ANNs) has been firstly applied for the computing the resonant frequency of ARCMAs. Multilayered perceptron model based on feed forward back propagation ANN has been utilized, and the constructed model have been separately trained with 8 different learning algorithms to achieve the best results regarding the resonant frequency of ARCMAs at dominant mode. To this end, the resonant frequencies of 80 ARCMAs with varied dimensions and electrical parameters in accordance with UHF band covering GSM, LTE, WLAN and WiMAX applications were simulated with a robust numerical electromagnetic computational tool, IE3D (TM), which is based on method of moment. Then, ANN model was constructed with the simulation data, by using 70 ARCMAs for training and the remaining 10 for test. As the performances of the 8 learning algorithms are compared with each other, the best result is obtained with Levenberg-Marquardt algorithm. The proposed ANN model were confirmed by comparing with the suggestions reported elsewhere via measurement data published earlier in the literature, and they have further validated on an ARCMA fabricated in this study. The results achieved in this study show that ANN model learning with LM algorithm can be successfully used to compute the resonant frequency of ARCMAs without involving any sophisticated methods