Epidemiological Trends of Haematological Malignancies in Belgium 2004-2018: Older Patients Show the Greatest Improvement in Survival.

(1) Background: Haematological malignancies (HMs) represent a heterogeneous group of mostly rare cancers that differ in pathophysiology, incidence, and outcome. (2) Methods: Our study aims to understand the epidemiological situation and trends of 24 main types of HMs in Belgium over a 15-year period, with a focus on the impact of age. Age-standardised incidence, average annual percentage change (AAPC), 5- and 10-year relative survival (RS) and RS trends were estimated for all HMs (N = 94,415) diagnosed between 2004 and 2018. (3) Results: Incidence rates of HM increased, mainly in the 70+ age group (AAPC: 3%). RS varied by age and HM type. For each HM type, outcome decreased with age. The greatest decrease with age in 5-year RS is observed for aggressive HM, acute myeloid leukaemia (AML), acute lymphoblastic leukaemia, and Burkitt lymphoma, from 67%, 90%, and 97% below 20 years, to 2%, 12%, and 16% above 80 years of age, respectively. The moderate improvement in 5-year RS over the 2004-2018 period for all HMs, of +5 percentage point (pp), masks highly heterogenous outcomes by HM type and age group. The most impressive improvements are observed in the 80+ group: +45, +33, +28, and +16 pp for Hodgkin lymphoma, immunoproliferative disorders, follicular lymphoma, and chronic myeloid leukaemia, respectively. (4) Conclusions: The increasing incidence and survival over the 2004-2018 period are likely explained by diagnostic and therapeutic innovations, which have spread to populations not targeted by clinical trials, especially older adults. This real-world population-based study highlights entities that need significant improvement, such as AML.

Machine Learning Algorithm to Estimate Distant Breast Cancer Recurrence at the Population Level with Administrative Data.

Purpose: High-quality population-based cancer recurrence data are scarcely available, mainly due to complexity and cost of registration. For the first time in Belgium, we developed a tool to estimate distant recurrence after a breast cancer diagnosis at the population level, based on real-world cancer registration and administrative data. Methods: Data on distant cancer recurrence (including progression) from patients diagnosed with breast cancer between 2009-2014 were collected from medical files at 9 Belgian centers to train, test and externally validate an algorithm (i.e., gold standard). Distant recurrence was defined as the occurrence of distant metastases between 120 days and within 10 years after the primary diagnosis, with follow-up until December 31, 2018. Data from the gold standard were linked to population-based data from the Belgian Cancer Registry (BCR) and administrative data sources. Potential features to detect recurrences in administrative data were defined based on expert opinion from breast oncologists, and subsequently selected using bootstrap aggregation. Based on the selected features, classification and regression tree (CART) analysis was performed to construct an algorithm for classifying patients as having a distant recurrence or not. Results: A total of 2507 patients were included of whom 216 had a distant recurrence in the clinical data set. The performance of the algorithm showed sensitivity of 79.5% (95% CI 68.8-87.8%), positive predictive value (PPV) of 79.5% (95% CI 68.8-87.8%), and accuracy of 96.7% (95% CI 95.4-97.7%). The external validation resulted in a sensitivity of 84.1% (95% CI 74.4-91.3%), PPV of 84.1% (95% CI 74.4-91.3%), and an accuracy of 96.8% (95% CI 95.4-97.9%). Conclusion: Our algorithm detected distant breast cancer recurrences with an overall good accuracy of 96.8% for patients with breast cancer, as observed in the first multi-centric external validation exercise.

Impact of the COVID-19 Pandemic on Incidence and Observed Survival of Malignant Brain Tumors in Belgium.

(1) Background: This study evaluates the impact of the COVID-19 pandemic on the incidence, treatment, and survival of adults diagnosed with malignant brain tumors in Belgium in 2020. (2) Methods: We examined patients aged 20 and older with malignant brain tumors (2004-2020) from the Belgian Cancer Registry database, assessing incidence, WHO performance status, vital status, and treatment data. We compared 2020 incidence rates with projected rates and age-standardized rates to 2015-2019. The Kaplan-Meier method was used to assess observed survival (OS). (3) Results: In 2020, there was an 8% drop in age-specific incidence rates, particularly for those over 50. Incidence rates plunged by 37% in April 2020 during the first COVID-19 peak but partially recovered by July. For all malignant brain tumors together, the two-year OS decreased by four percentage points (p.p.) in 2020 and three p.p. in 2019, compared to that in 2015-2018. Fewer patients (-9 p.p.) with glioblastoma underwent surgery, and the proportion of patients not receiving surgery, radiotherapy, or systemic therapy increased by six percentage points in 2020. (4) Conclusions: The COVID-19 pandemic profoundly impacted the diagnosis, treatment strategies, and survival of brain tumor patients in Belgium during 2020. These findings should guide policymakers in future outbreak responses, emphasizing the need to maintain or adapt (neuro)-oncological care pathways and promote informed decision making when care capacity is limited.

Machine Learning-Based Extraction of Breast Cancer Receptor Status From Bilingual Free-Text Pathology Reports.

As part of its core business of gathering population-based information on new cancer diagnoses, the Belgian Cancer Registry receives free-text pathology reports, describing results of (pre-)malignant specimens. These reports are provided by 82 laboratories and written in 2 national languages, Dutch or French. For breast cancer, the reports characterize the status of estrogen receptor, progesterone receptor, and Erb-b2 receptor tyrosine kinase 2. These biomarkers are related with tumor growth and prognosis and are essential to define therapeutic management. The availability of population-scale information about their status in breast cancer patients can therefore be considered crucial to enrich real-world scientific studies and to guide public health policies regarding personalized medicine. The main objective of this study is to expand the data available at the Belgian Cancer Registry by automatically extracting the status of these biomarkers from the pathology reports. Various types of numeric features are computed from over 1,300 manually annotated reports linked to breast tumors diagnosed in 2014. A range of popular machine learning classifiers, such as support vector machines, random forests and logistic regressions, are trained on this data and compared using their F 1 scores on a separate validation set. On a held-out test set, the best performing classifiers achieve F 1 scores ranging from 0.89 to 0.92 for the four classification tasks. The extraction is thus reliable and allows to significantly increase the availability of this valuable information on breast cancer receptor status at a population level.

Cause of death for patients with breast cancer: discordance between death certificates and medical files, and impact on survival estimates.

BACKGROUND: Registration and coding of cause of death is prone to error since determining the exact underlying condition leading directly to death is challenging. In this study, causes of death from the death certificates were compared to patients' medical files interpreted by experts at University Hospitals Leuven (UHL), to assess concordance between sources and its impact on cancer survival assessment. METHODS: Breast cancer patients treated at UHL (2009-2014) (follow-up until December 31st 2016) were included in this study. Cause of death was obtained from death certificates and expert-reviewed medical files at UHL. Agreement was calculated using Cohen's kappa coefficient. Cause-specific survival (CSS) was calculated using the Kaplan-Meier method and the relative survival probability (RS) using the Ederer II and Pohar Perme method. RESULTS: A total of 2862 patients, of whom 354 died, were included. We found an agreement of 84.7% (kappa-value of 0.69 (95% C.I.: 0.62-0.77)) between death certificates and medical files. Death certificates had 10.7% false positive and 4.5% false negative rates. However, five-year CSS and RS measures were comparable for both sources. CONCLUSION: For breast cancer patients included in our study, fair agreement of cause of death was seen between death certificates and medical files with similar CSS and RS estimations.

A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data.

BACKGROUND: Exact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data. METHODS: The systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy. RESULTS: Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%). CONCLUSIONS: Publications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future.

Disentangling the role of TRPM4 in hippocampus-dependent plasticity and learning: an electrophysiological, behavioral and FMRI approach.

Hippocampal long-term potentiation (LTP) has been extensively studied as a cellular model of learning and memory. Recently, we described a central function of the Transient Receptor Potential M4 (TRPM4) channel in hippocampal LTP in mice in vitro. Here, we used Trpm4 knock-out (Trpm4-/-) rats to scrutinize TRPM4's role in the intact brain in vivo. After having confirmed the previous in vitro findings in mice, we studied hippocampal synaptic plasticity by chronic recordings in freely moving rats, hippocampus-dependent learning by a behavioral battery and hippocampal-cortical connectivity by fMRI. The electrophysiological investigation supports an involvement of TRPM4 in LTP depending on the induction protocol. Moreover, an exhaustive analysis of the LTP kinetics point to mechanistic changes in LTP by trpm4 deletion. General behavior as measured by open field test, light-dark box and elevated plus maze was inconspicuous in Trpm4-/- rats. However, they showed a distinct deficit in spatial working and reference memory associated to the Barnes maze and T-maze test, respectively. In contrast, performance of the Trpm4-/- in the Morris water maze was unaltered. Finally, fMRI investigation of the effects of a strong LTP induction manifested BOLD responses in the ipsilateral and contralateral hippocampus and the prefrontal cortex of both groups. Yet, the initial BOLD response in the stimulated hippocampal area of Trpm4-/- was significantly enhanced compared to WT rats. Our findings at the cellular, behavioral and system level point to a relevant role for TRPM4 in specific types of hippocampal synaptic plasticity and learning but not in hippocampal-prefrontal interaction.

Erratum: Conceptualization and validation of an open-source closed-loop deep brain stimulation system in rat.

This corrects the article DOI: 10.1038/srep09921.

An evaluation of the effect of pulse-shape on grey and white matter stimulation in the rat brain.

Despite the current success of neuromodulation, standard biphasic, rectangular pulse shapes may not be optimal to achieve symptom alleviation. Here, we compared stimulation efficiency (in terms of charge) between complex and standard pulses in two areas of the rat brain. In motor cortex, Gaussian and interphase gap stimulation (IPG) increased stimulation efficiency in terms of charge per phase compared with a standard pulse. Moreover, IPG stimulation of the deep mesencephalic reticular formation in freely moving rats was more efficient compared to a standard pulse. We therefore conclude that complex pulses are superior to standard stimulation, as less charge is required to achieve the same behavioral effects in a motor paradigm. These results have important implications for the understanding of electrical stimulation of the nervous system and open new perspectives for the design of the next generation of safe and efficient neural implants.

Field Potential Oscillations in the Bed Nucleus of the Stria Terminalis Correlate with Compulsion in a Rat Model of Obsessive-Compulsive Disorder.

UNLABELLED: The bed nucleus of the stria terminalis (BNST) is implicated in anxiety and reward processing, both of which are associated with obsessive-compulsive disorder (OCD). Specific neuronal groups in the BNST related to anxiety and reward have been identified, but quantitative data about the information carried by local field potential (LFP) signals in this area during obsession/compulsion are lacking. Here we investigate the BNST LFP in the schedule-induced polydipsia, an animal model of OCD. We implanted electrodes bilaterally in the BNST and random control brain regions in 32 male Wistar rats, and recorded corresponding LFP during compulsive and noncompulsive behavior. We first applied high-frequency (100 Hz) electrical stimulation through the implanted electrodes and analyzed its effects on compulsive behavior. We then performed time-frequency analysis of LFPs and statistically compared the normalized power of δ (1-4 Hz), θ (4-8 Hz), α (8-12 Hz), ß (12-30 Hz), and lower γ (30-45 Hz) bands between different groups. Our data showed that the normalized δ, ß, and γ powers in the right BNST were specifically correlated with compulsive behaviors. δ and γ oscillations increased and decreased during the initiation phase of compulsion, respectively, whereas ß increased after compulsion stopped. Moreover, the effect of BNST electrical stimulation, in terms of suppression of compulsion, was significantly correlated with the percentage change of these bands during compulsion. Our research reveals potential biomarkers and underlying neurophysiological mechanisms of compulsion and warrants further assessment of the use of LFP for closed-loop neuromodulation in OCD. SIGNIFICANCE STATEMENT: Although specific neuronal groups in the bed nucleus of the stria terminalis (BNST) related to anxiety and reward circuitries have been identified, psychopathological information carried by local field potentials in the BNST has not yet been described. We discovered that normalized powers of the right BNST δ, ß, and γ oscillations were highly correlated with compulsion. Specifically, δ and γ oscillations increased and decreased during the initiation phase of compulsion, respectively, whereas ß increased after compulsion stopped. Such correlations were not found in other parts of the brain during compulsion, or in the BNST during noncompulsive behavior. Current findings reveal real-time neurophysiological biomarkers of compulsion and warrant further assessment of the use of local field potentials for closed-loop neuromodulation for OCD.

Conceptualization and validation of an open-source closed-loop deep brain stimulation system in rat.

Conventional deep brain stimulation (DBS) applies constant electrical stimulation to specific brain regions to treat neurological disorders. Closed-loop DBS with real-time feedback is gaining attention in recent years, after proved more effective than conventional DBS in terms of pathological symptom control clinically. Here we demonstrate the conceptualization and validation of a closed-loop DBS system using open-source hardware. We used hippocampal theta oscillations as system input, and electrical stimulation in the mesencephalic reticular formation (mRt) as controller output. It is well documented that hippocampal theta oscillations are highly related to locomotion, while electrical stimulation in the mRt induces freezing. We used an Arduino open-source microcontroller between input and output sources. This allowed us to use hippocampal local field potentials (LFPs) to steer electrical stimulation in the mRt. Our results showed that closed-loop DBS significantly suppressed locomotion compared to no stimulation, and required on average only 56% of the stimulation used in open-loop DBS to reach similar effects. The main advantages of open-source hardware include wide selection and availability, high customizability, and affordability. Our open-source closed-loop DBS system is effective, and warrants further research using open-source hardware for closed-loop neuromodulation.

Interleukin-6 dynamics as a basis for an early-warning monitor for sepsis and inflammation in individual pigs.

Static interleukin-6 (IL-6) levels of pigs contain considerable individual differences, which obstruct the practical use of IL-6 for disease monitoring purposes. It was hypothesised that interleukin-6 (IL-6) dynamics could be used to quantify these individual differences and carries critical information of the individual pig infection status. Time series of IL-6 responses in 25 pigs were analysed before and after infection by Actinobacillus pleuropneumoniae. The results indicated that amplitude increases of IL-6 fluctuations of individual pigs rather than static IL-6 values should be used as indicator of the infection state. This study shows the added value for IL-6 time series analyses of individual pigs. These results are a first step towards the development of objective individualised methods for monitoring and early detection of sepsis and inflammation processes in pigs by integrating animal response dynamics.

Rethinking food anticipatory activity in the activity-based anorexia rat model.

When a rat is on a limited fixed-time food schedule with full access to a running wheel (activity-based anorexia model, ABA), its activity level will increase hours prior to the feeding period. This activity, called food-anticipatory activity (FAA), is a hypothesized parallel to the hyperactivity symptom in human anorexia nervosa. To investigate in depth the characteristics of FAA, we retrospectively analyzed the level of FAA and activities during other periods in ABA rats. To our surprise, rats with the most body weight loss have the lowest level of FAA, which contradicts the previously established link between FAA and the severity of ABA symptoms. On the contrary, our study shows that postprandial activities are more directly related to weight loss. We conclude that FAA alone may not be sufficient to reflect model severity, and activities during other periods may be of potential value in studies using ABA model.

System identification of mGluR-dependent long-term depression.

Recent advances have started to uncover the underlying mechanisms of metabotropic glutamate receptor (mGluR)-dependent long-term depression (LTD). However, it is not completely clear how these mechanisms are linked, and it is believed that several crucial mechanisms remain to be revealed. In this study, we investigated whether system identification (SI) methods can be used to gain insight into the mechanisms of synaptic plasticity. SI methods have been shown to be an objective and powerful approach for describing how sensory neurons encode information about stimuli. However, to our knowledge, it is the first time that SI methods have been applied to electrophysiological brain slice recordings of synaptic plasticity responses. The results indicate that the SI approach is a valuable tool for reverse-engineering of mGluR-LTD responses. We suggest that such SI methods can aid in unraveling the complexities of synaptic function.