Personalized surveillance and aftercare for non-metastasized breast cancer: the NABOR study protocol of a multiple interrupted time series design.

BACKGROUND: Follow-up of curatively treated primary breast cancer patients consists of surveillance and aftercare and is currently mostly the same for all patients. A more personalized approach, based on patients' individual risk of recurrence and personal needs and preferences, may reduce patient burden and reduce (healthcare) costs. The NABOR study will examine the (cost-)effectiveness of personalized surveillance (PSP) and personalized aftercare plans (PAP) on patient-reported cancer worry, self-rated and overall quality of life and (cost-)effectiveness. METHODS: A prospective multicenter multiple interrupted time series (MITs) design is being used. In this design, 10 participating hospitals will be observed for a period of eighteen months, while they -stepwise- will transit from care as usual to PSPs and PAPs. The PSP contains decisions on the surveillance trajectory based on individual risks and needs, assessed with the 'Breast Cancer Surveillance Decision Aid' including the INFLUENCE prediction tool. The PAP contains decisions on the aftercare trajectory based on individual needs and preferences and available care resources, which decision-making is supported by a patient decision aid. Patients are non-metastasized female primary breast cancer patients (N = 1040) who are curatively treated and start follow-up care. Patient reported outcomes will be measured at five points in time during two years of follow-up care (starting about one year after treatment and every six months thereafter). In addition, data on diagnostics and hospital visits from patients' Electronical Health Records (EHR) will be gathered. Primary outcomes are patient-reported cancer worry (Cancer Worry Scale) and overall quality of life (as assessed with EQ-VAS score). Secondary outcomes include health care costs and resource use, health-related quality of life (as measured with EQ5D-5L/SF-12/EORTC-QLQ-C30), risk perception, shared decision-making, patient satisfaction, societal participation, and cost-effectiveness. Next, the uptake and appreciation of personalized plans and patients' experiences of their decision-making process will be evaluated. DISCUSSION: This study will contribute to insight in the (cost-)effectiveness of personalized follow-up care and contributes to development of uniform evidence-based guidelines, stimulating sustainable implementation of personalized surveillance and aftercare plans. TRIAL REGISTRATION: Study sponsor: ZonMw. Retrospectively registered at ClinicalTrials.gov (2023), ID: NCT05975437.

OPTIMA: a phase II dose and volume de-escalation trial for human papillomavirus-positive oropharyngeal cancer.

BACKGROUND: Patients with HPV+ oropharyngeal squamous cell carcinoma were assigned to dose and volume de-escalated radiotherapy (RT) or chemoradiotherapy (CRT) based on response to induction chemotherapy in an effort to limit treatment-related toxicity while preserving efficacy. PATIENTS AND METHODS: Patients were classified as low-risk (≤T3, ≤N2B, ≤10 pack-year history) or high-risk (T4 or ≥N2C or >10 PYH). After three cycles of carboplatin/nab-paclitaxel, response was assessed using Response Evaluation Criteria in Solid Tumors 1.1. Low-risk patients with ≥50% response received 50 Gray (Gy) RT (RT50) while low-risk patients with 30%-50% response or high-risk patients with ≥50% response received 45 Gy CRT (CRT45). Patients with lesser response received standard-of-care 75 Gy CRT (CRT75). RT/CRT was limited to the first echelon of uninvolved nodes. The primary end point was 2-year progression-free survival compared with a historic control of 85%. Secondary end points included overall survival and toxicity. RESULTS: Sixty-two patients (28 low risk/34 high risk) were enrolled. Of low-risk patients, 71% received RT50 while 21% received CRT45. Of high-risk patients, 71% received CRT45. With a median follow-up of 29 months, 2-year PFS and OS were 95% and 100% for low-risk patients and 94% and 97% for high-risk patients, respectively. The overall 2-year PFS was 94.5% and within the 11% noninferiority margin for the historic control. Grade 3+ mucositis occurred in 30%, 63%, and 91% of the RT50, CRT45, and CRT75 groups, respectively (P = 0.004). Rates of any PEG-tube use were 0%, 31%, and 82% for RT50, CRT45, and CRT75 groups, respectively (P < 0.0001). CONCLUSIONS: Induction chemotherapy with response and risk-stratified dose and volume de-escalated RT/CRT for HPV+ OPSCC is associated with favorable oncologic outcomes and reduced acute and chronic toxicity. Further evaluation of induction-based de-escalation in large multicenter studies is justified. CLINICAL TRIAL REGISTRATION: Clinical trials.gov identifier: NCT02258659.

Burden of disease in children with respiratory tract infections in primary care: diary-based cohort study.

BACKGROUND: Respiratory tract infections (RTIs) are a common reason for children to consult in general practice. Antibiotics are often prescribed, in part due to miscommunication between parents and GPs. The duration of specific respiratory symptoms has been widely studied. Less is known about illness-related symptoms and the impact of these symptoms on family life, including parental production loss. Better understanding of the natural course of illness-related symptoms in RTI in children and impact on family life may improve GP-parent communication during RTI consultations. OBJECTIVE: To describe the general impact of RTI on children and parents regarding illness-related symptoms, absenteeism from childcare, school and work, use of health care facilities, and the use of over-the-counter (OTC) medication. METHODS: Prospectively collected diary data from two randomized clinical trials in children with RTI in primary care (n = 149). Duration of symptoms was analysed using survival analysis. RESULTS: Disturbed sleep, decreased intake of food and/or fluid, feeling ill and/or disturbance at play or other daily activities are very common during RTI episodes, with disturbed sleep lasting longest. Fifty-two percent of the children were absent for one or more days from childcare or school, and 28% of mothers and 20% of fathers reported absence from work the first week after GP consultation. Re-consultation occurred in 48% of the children. OTC medication was given frequently, particularly paracetamol and nasal sprays. CONCLUSION: Appreciation of, and communication about the general burden of disease on children and their parents, may improve understanding between GPs and parents consulting with their child.

Single Atom Detection from Low Contrast-to-Noise Ratio Electron Microscopy Images.

Single atom detection is of key importance to solving a wide range of scientific and technological problems. The strong interaction of electrons with matter makes transmission electron microscopy one of the most promising techniques. In particular, aberration correction using scanning transmission electron microscopy has made a significant step forward toward detecting single atoms. However, to overcome radiation damage, related to the use of high-energy electrons, the incoming electron dose should be kept low enough. This results in images exhibiting a low signal-to-noise ratio and extremely weak contrast, especially for light-element nanomaterials. To overcome this problem, a combination of physics-based model fitting and the use of a model-order selection method is proposed, enabling one to detect single atoms with high reliability.

PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE.

AIM: Identifying the best care for a patient can be extremely challenging. To support the creation of multifactorial Decision Support Systems (DSSs), we propose an Umbrella Protocol, focusing on prostate cancer. MATERIALS & METHODS: The PRODIGE project consisted of a workflow for standardizing data, and procedures, to create a consistent dataset useful to elaborate DSSs. Techniques from classical statistics and machine learning will be adopted. The general protocol accepted by our Ethical Committee can be downloaded from cancerdata.org . RESULTS: A standardized knowledge sharing process has been implemented by using a semi-formal ontology for the representation of relevant clinical variables. CONCLUSION: The development of DSSs, based on standardized knowledge, could be a tool to achieve a personalized decision-making.

What Keeps Them Together? Insights into Sex Offender Couples Using Qualitative Content Analyses.

Research on sex offenders' relationships is scarce. The aim of this qualitative study was to investigate sex offenders' relationships as well as their female partners' adjustment strategies by means of interview analysis. Both partners profit from the relationship in terms of mutual support and acceptance. The sexual offense is a taboo subject, and the female partners were found to demonstrate cognitive distortions. The imbalance of power found in the sex offenders' relationships is discussed, as is the finding that those sexual offenders interviewed live out their need for dominance and sometimes their aggression. The women interviewed were found to cling to their partners as a result of their insecure attachment style. We discuss couples counseling and therapy as possibilities for addressing the imbalance of power and casting light upon the sexual aspect of the relationship.

Vaccination of cattle only is sufficient to stop FMDV transmission in mixed populations of sheep and cattle.

We quantified the transmission of foot-and-mouth disease virus in mixed cattle-sheep populations and the effect of different vaccination strategies. The (partial) reproduction ratios (R) in groups of non-vaccinated and vaccinated cattle and/or sheep were estimated from (published) transmission experiments. A 4 × 4 next-generation matrix (NGM) was constructed using these estimates. The dominant eigenvalue of the NGM, the R for a mixed population, was determined for populations with different proportions of cattle and sheep and for three different vaccination strategies. The higher the proportion of cattle in a mixed cattle-sheep population, the higher the R for the mixed population. Therefore the impact of vaccination of the cattle is higher. After vaccination of all animals R = 0·1 independent of population composition. In mixed cattle-sheep populations with at least 14% of cattle, vaccination of cattle only is sufficient to reduce R to < 1.

An Overview of Real-World Data Infrastructure for Cancer Research.

AIMS: There is increasing interest in the opportunities offered by Real World Data (RWD) to provide evidence where clinical trial data does not exist, but access to appropriate data sources is frequently cited as a barrier to RWD research. This paper discusses current RWD resources and how they can be accessed for cancer research. MATERIALS AND METHODS: There has been significant progress on facilitating RWD access in the last few years across a range of scales, from local hospital research databases, through regional care records and national repositories, to the impact of federated learning approaches on internationally collaborative studies. We use a series of case studies, principally from the UK, to illustrate how RWD can be accessed for research and healthcare improvement at each of these scales. RESULTS: For each example we discuss infrastructure and governance requirements with the aim of encouraging further work in this space that will help to fill evidence gaps in oncology. CONCLUSION: There are challenges, but real-world data research across a range of scales is already a reality. Taking advantage of the current generation of data sources requires researchers to carefully define their research question and the scale at which it would be best addressed.

Federated Learning Survival Model and Potential Radiotherapy Decision Support Impact Assessment for Non-small Cell Lung Cancer Using Real-World Data.

AIMS: The objective of this study was to develop a two-year overall survival model for inoperable stage I-III non-small cell lung cancer (NSCLC) patients using routine radiation oncology data over a federated (distributed) learning network and evaluate the potential of decision support for curative versus palliative radiotherapy. METHODS: A federated infrastructure of data extraction, de-identification, standardisation, image analysis, and modelling was installed for seven clinics to obtain clinical and imaging features and survival information for patients treated in 2011-2019. A logistic regression model was trained for the 2011-2016 curative patient cohort and validated for the 2017-2019 cohort. Features were selected with univariate and model-based analysis and optimised using bootstrapping. System performance was assessed by the receiver operating characteristic (ROC) and corresponding area under curve (AUC), C-index, calibration metrics and Kaplan-Meier survival curves, with risk groups defined by model probability quartiles. Decision support was evaluated using a case-control analysis using propensity matching between treatment groups. RESULTS: 1655 patient datasets were included. The overall model AUC was 0.68. Fifty-eight percent of patients treated with palliative radiotherapy had a low-to-moderate risk prediction according to the model, with survival times not significantly different (p = 0.87 and 0.061) from patients treated with curative radiotherapy classified as high-risk by the model. When survival was simulated by risk group and model-indicated treatment, there was an estimated 11% increase in survival rate at two years (p < 0.01). CONCLUSION: Federated learning over multiple institution data can be used to develop and validate decision support systems for lung cancer while quantifying the potential impact of their use in practice. This paves the way for personalised medicine, where decisions can be based more closely on individual patient details from routine care.

An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection.

BACKGROUND AND OBJECTIVE: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has been proposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained data lacks the influence of different hardware, of servo-controlled algorithms, and different sources of noise. Here, we propose VentGAN, an adversarial learning approach to improve simulated data by learning the ventilator fingerprints from unlabeled clinical data. METHODS: In VentGAN, the loss functions are designed to add characteristics of clinical waveforms to the generated results, while preserving the labels of the simulated waveforms. To validate VentGAN, we compare the performance for detection and classification of PVAs when training a previously developed machine learning algorithm with the original simulated data and with the data generated by VentGAN. Testing is performed on independent clinical data labeled by experts. The McNemar test is applied to evaluate statistical differences in the obtained classification accuracy. RESULTS: VentGAN significantly improves the classification accuracy for late cycling, early cycling and normal breaths (p< 0.01); no significant difference in accuracy was observed for delayed inspirations (p = 0.2), while the accuracy decreased for ineffective efforts (p< 0.01). CONCLUSIONS: Generation of realistic synthetic data with labels by the proposed framework is feasible and represents a promising avenue for improving training of machine learning models.

Iterative linear focal-plane wavefront correction.

We propose an efficient approximation to the nonlinear phase diversity (PD) method for wavefront reconstruction and correction from intensity measurements with potential of being used in real-time applications. The new iterative linear phase diversity (ILPD) method assumes that the residual phase aberration is small and makes use of a first-order Taylor expansion of the point spread function (PSF), which allows for arbitrary (large) diversities in order to optimize the phase retrieval. For static disturbances, at each step, the residual phase aberration is estimated based on one defocused image by solving a linear least squares problem, and compensated for with a deformable mirror. Due to the fact that the linear approximation does not have to be updated with each correction step, the computational complexity of the method is reduced to that of a matrix-vector multiplication. The convergence of the ILPD correction steps has been investigated and numerically verified. The comparative study that we make demonstrates the improved performance in computational time with no decrease in accuracy with respect to existing methods that also linearize the PSF.

The effect of natamycin on the transcriptome of conidia of Aspergillus niger.

The impact of natamycin on Aspergillus niger was analysed during the first 8 h of germination of conidia. Polarisation, germ tube formation, and mitosis were inhibited in the presence of 3 and 10 µM of the anti-fungal compound, while at 10 µM also isotropic growth was affected. Natamycin did not have an effect on the decrease of microviscosity during germination and the concomitant reduction in mannitol and trehalose levels. However, it did abolish the increase of intracellular levels of glycerol and glucose during the 8 h period of germination.Natamycin hardly affected the changes that occur in the RNA profile during the first 2 h of germination. During this time period, genes related to transcription, protein synthesis, energy and cell cycle and DNA processing were particularly up-regulated. Differential expression of 280 and 2586 genes was observed when 8 h old germlings were compared with conidia that had been exposed to 3 µM and 10 µM natamycin, respectively. For instance, genes involved in ergosterol biosynthesis were down-regulated. On the other hand, genes involved in endocytosis and the metabolism of compatible solutes, and genes encoding protective proteins were up-regulated in natamycin treated conidia.

Radiomics biopsy signature for predicting survival in patients with spinal bone metastases (SBMs).

STUDY DESIGN: Retrospective analysis of a registered cohort of patients treated and irradiated for metastases in the spinal column in a single institute. OBJECTIVE: This is the first study to develop and internally validate radiomics features for predicting six-month survival probability for patients with spinal bone metastases (SBM). BACKGROUND DATA: Extracted radiomics features from routine clinical CT images can be used to identify textural and intensity-based features unperceivable to human observers and associate them with a patient survival probability or disease progression. METHODS: A study was conducted on 250 patients treated for metastases in the spinal column irradiated for the first time between 2014 and 2016, at the MAASTRO clinic in Maastricht, the Netherlands. The first 150 available patients were used to develop the model and the subsequent 100 patient were considered as a test set for the model. A bootstrap (B = 400) stepwise model selection, which combines both the forward and backward variable elimination procedure, was used to select the most useful predictive features from the training data based on the Akaike information criterion (AIC). The stepwise selection procedure was applied to the 400 bootstrap samples, and the results were plotted as a histogram to visualize how often each variable was selected. Only variables selected more than 90 % of the time over the bootstrap runs were used to build the final model.A prognostic index (PI) called radiomics score (radscore) and clinical score (clinscore) was calculated for each patient. The prognostic index was not scaled, the original values were used which can be extracted from the model directly or calculated as a linear combination of the variables in the model multiplied by the respective beta value for each patient. RESULTS: The clinical model had a good discrimination power. The radiomics model, on the other hand, had an inferior performance with no added predictive power to the clinical model. The internal imaging characteristics do not seem to have a value in the prediction of survival. However, the Shape features were excluded from further analyses in our study since all biopsies had a standard shape hence no variability.

Phase II trial of pemetrexed-based induction chemotherapy followed by concomitant chemoradiotherapy in previously irradiated patients with squamous cell carcinoma of the head and neck.

BACKGROUND: Concurrent chemoreirradiation therapy (CRRT) offers a therapeutic option for patients with locoregionally recurrent squamous cell carcinoma of the head and neck (SCCHN). We hypothesized that response to induction chemotherapy (IC) would improve outcome and predict increased survival. PATIENTS AND METHODS: Subjects with recurrent SCCHN not amenable to standard therapy were eligible. IC consisted of two 28-day cycles of gemcitabine and pemetrexed on days 1 and 14, followed by surgical resection, if appropriate, and/or CRRT consisting of carboplatin, pemetrexed, and single daily fractionated radiotherapy. RESULTS: Thirty-five subjects were enrolled, 31 were assessable for response, with 11 responders [response rate = 35%; 95% confidence interval (CI) 19.2-54.6]. Among 24 subjects who started CRRT, 11 were assessable for radiographic response, 4 complete response, 2 partial response, and 5 progressive disease. Median progression-free survival and overall survival (OS) were 5.5 months (95% CI 3.6-8.3) and 9.5 months (95% CI 7.2-15.4), respectively. One-year OS was 43% (95% CI 26% to 58%). Subjects who responded to IC had improved survival (P = 0.02). Toxic effects included mucositis, dermatitis, neutropenia, infection, hemorrhage, dehydration, and pain. CONCLUSIONS: The combination of pemetrexed plus gemcitabine was active and well tolerated in recurrent SCCHN. Response to IC may help stratify prognosis and offer an objective and dynamic metric in recurrent SCCHN patients being considered for CRRT.

Estimation of foot and mouth disease transmission parameters, using outbreak data and transmission experiments.

Mathematical models for the spread of foot and mouth disease (FMD) have been developed and used for a number of purposes in the recent literature. One important purpose is predicting the effect of strategies to combat between-farm epidemic spread, in support of decision-making on epidemic control. The authors briefly review the various modelling approaches, discussing the parameters used and how estimates may be obtained for these parameters. They emphasise that, in addition to the estimation of FMD transmission parameters, the choice of model structure (including the number and type of parameters used) is also crucial. Two gaps in the knowledge of FMD transmission, related to model construction and parameter quantification, are identified: transmission between different species and the way in which vaccination affects such transmission, and route-specific FMD transmission properties. In particular, the authors pay attention to the role that small-scale transmission experiments can play in bridging these gaps.

Current applications of deep-learning in neuro-oncological MRI.

PURPOSE: Magnetic Resonance Imaging (MRI) provides an essential contribution in the screening, detection, diagnosis, staging, treatment and follow-up in patients with a neurological neoplasm. Deep learning (DL), a subdomain of artificial intelligence has the potential to enhance the characterization, processing and interpretation of MRI images. The aim of this review paper is to give an overview of the current state-of-art usage of DL in MRI for neuro-oncology. METHODS: We reviewed the Pubmed database by applying a specific search strategy including the combination of MRI, DL, neuro-oncology and its corresponding search terminologies, by focussing on Medical Subject Headings (Mesh) or title/abstract appearance. The original research papers were classified based on its application, into three categories: technological innovation, diagnosis and follow-up. RESULTS: Forty-one publications were eligible for review, all were published after the year 2016. The majority (N = 22) was assigned to technological innovation, twelve had a focus on diagnosis and seven were related to patient follow-up. Applications ranged from improving the acquisition, synthetic CT generation, auto-segmentation, tumor classification, outcome prediction and response assessment. The majority of publications made use of standard (T1w, cT1w, T2w and FLAIR imaging), with only a few exceptions using more advanced MRI technologies. The majority of studies used a variation on convolution neural network (CNN) architectures. CONCLUSION: Deep learning in MRI for neuro-oncology is a novel field of research; it has potential in a broad range of applications. Remaining challenges include the accessibility of large imaging datasets, the applicability across institutes/vendors and the validation and implementation of these technologies in clinical practise.

Repeatability and reproducibility study of radiomic features on a phantom and human cohort.

The repeatability and reproducibility of radiomic features extracted from CT scans need to be investigated to evaluate the temporal stability of imaging features with respect to a controlled scenario (test-retest), as well as their dependence on acquisition parameters such as slice thickness, or tube current. Only robust and stable features should be used in prognostication/prediction models to improve generalizability across multiple institutions. In this study, we investigated the repeatability and reproducibility of radiomic features with respect to three different scanners, variable slice thickness, tube current, and use of intravenous (IV) contrast medium, combining phantom studies and human subjects with non-small cell lung cancer. In all, half of the radiomic features showed good repeatability (ICC > 0.9) independent of scanner model. Within acquisition protocols, changes in slice thickness was associated with poorer reproducibility compared to the use of IV contrast. Broad feature classes exhibit different behaviors, with only few features appearing to be the most stable. 108 features presented both good repeatability and reproducibility in all the experiments, most of them being wavelet and Laplacian of Gaussian features.

Recurrent inference machines as inverse problem solvers for MR relaxometry.

In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T1 and T2 mapping. The RIM is a neural network framework that learns an iterative inference process based on the signal model, similar to conventional statistical methods for quantitative MRI (QMRI), such as the Maximum Likelihood Estimator (MLE). This framework combines the advantages of both data-driven and model-based methods, and, we hypothesize, is a promising tool for QMRI. Previously, RIMs were used to solve linear inverse reconstruction problems. Here, we show that they can also be used to optimize non-linear problems and estimate relaxometry maps with high precision and accuracy. The developed RIM framework is evaluated in terms of accuracy and precision and compared to an MLE method and an implementation of the Residual Neural Network (ResNet). The results show that the RIM improves the quality of estimates compared to the other techniques in Monte Carlo experiments with simulated data, test-retest analysis of a system phantom, and in-vivo scans. Additionally, inference with the RIM is 150 times faster than the MLE, and robustness to (slight) variations of scanning parameters is demonstrated. Hence, the RIM is a promising and flexible method for QMRI. Coupled with an open-source training data generation tool, it presents a compelling alternative to previous methods.

Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy.

PURPOSE: Classic statistical and machine learning models such as support vector machines (SVMs) can be used to predict cancer outcome, but often only perform well if all the input variables are known, which is unlikely in the medical domain. Bayesian network (BN) models have a natural ability to reason under uncertainty and might handle missing data better. In this study, the authors hypothesize that a BN model can predict two-year survival in non-small cell lung cancer (NSCLC) patients as accurately as SVM, but will predict survival more accurately when data are missing. METHODS: A BN and SVM model were trained on 322 inoperable NSCLC patients treated with radiotherapy from Maastricht and validated in three independent data sets of 35, 47, and 33 patients from Ghent, Leuven, and Toronto. Missing variables occurred in the data set with only 37, 28, and 24 patients having a complete data set. RESULTS: The BN model structure and parameter learning identified gross tumor volume size, performance status, and number of positive lymph nodes on a PET as prognostic factors for two-year survival. When validated in the full validation set of Ghent, Leuven, and Toronto, the BN model had an AUC of 0.77, 0.72, and 0.70, respectively. A SVM model based on the same variables had an overall worse performance (AUC 0.71, 0.68, and 0.69) especially in the Ghent set, which had the highest percentage of missing the important GTV size data. When only patients with complete data sets were considered, the BN and SVM model performed more alike. CONCLUSIONS: Within the limitations of this study, the hypothesis is supported that BN models are better at handling missing data than SVM models and are therefore more suitable for the medical domain. Future works have to focus on improving the BN performance by including more patients, more variables, and more diversity.