Risk as a pattern over time: Delineation of time-dependent risk factors in biological,psychological, and social variables in cancer patients

Background

Survival in cancer patients is associated with a multitude of biological, social, and psychological factors. Although it is well established that all these factors add to overall mortality, it is not well understood how the predictive power of these parameters changes in a comprehensive model and over time.

Methods

Patients who attended the authors’ outpatient clinic were invited to participate. The authors followed 5180 mixed cancer patients (51.1% female; mean age, 59.1 years [SD = 13.8]) for up to 16 years and analyzed biological (age, sex, cancer site, anemia), psychological (anxiety, depression), and social variables (marital status, education, employment status) potentially predicting overall survival in a Cox proportional hazards model.

Results

The median survival time for the entire sample was 4.3 years (95% confidence interval, 4.0–4.7). The overall survival probabilities for 1 and 10 years were 76.8% and 38.0%, respectively. Following an empirical approach, the authors split the time interval into five periods: acute, subacute, short-term, medium-term, and long-term. A complex pattern of variables predicted overall survival differently in the five periods. Biological parameters were important throughout most of the time, social parameters were either time-independent predictors or tended to be more important in the longer term. Of the psychological parameters, only depression was a significant predictor and lost its predictive power in the long-term.

Conclusions

The findings of this study allow the development of comprehensive patient-specific models of risk and resilience factors addressing biopsychosocial needs of cancer patients, paving the way for a personalized treatment plan that goes beyond biomedical cancer care.     

Anästhesiologische Risikoevaluation und rechtssichere Aufklärung per Telemedizin – Sind wir bereit für einen Paradigmenwechsel?

Die Anaesthesiologie - In Deutschland werden pro Jahr 17 Millionen Narkosen und damit auch ungefähr so viele Narkosevorgespräche durchgeführt. Bis dato finden diese praktisch...     

Application of computer-generated images to train pattern recognition used in semiquantitative immunohistochemistry scoring

This study aimed to clarify whether the pattern recognition involved in scoring proliferation fractions can be trained by abstract computerized images of virtual tissues. Twenty computer-generated images with randomly distributed blue or red dots were scored by 12 probands (all co-workers or collaborators of the Institute of Pathology, University of Bonn). Afterward, the probands underwent a training phase during which they received an immediate feedback on the actual rate of positivity after each image. Finally, the initial testing series was rescored. In a second round with 15 different probands, 20 Ki-67 immunohistochemistry images of tonsil tissue were scored, followed by the same training phase with computer-generated images, before the immunohistochemistry slides were scored again. Paired t-tests were used to compare the differences in mean rates pre- and post-training. Concerning computerized images, untrained probands scored the percentages of positive dots with a mean deviation from the true rates of 8.2%. Following training, the same testing series was scored significantly better with a mean deviation of 4.9% (mean improvement 3.3%, p < 0.001). Scoring real immunohistochemistry slides, the training with computerized images also improved correct estimations, albeit to a lesser degree (mean improvement 1%, p = 0.03). Abstract computerized images of virtual tissues may be a useful tool to train and improve the accuracy of pattern recognition involved in semiquantitative scoring of immunohistochemistry slides. As a side results, this study highlights the value of computer-generated images to verify the performance of image-analysis software.     

Repeated hepatic resection for colorectal liver metastases: is this concept safe and feasible?

European Surgery - The beneficial outcomes of hepatectomy in patients with colorectal metastases have encouraged the attempts of repeated hepatectomy in patients with recurrent disease. Although...     

Research campaigns in the UK National Health Service: patient recruitment and questions of valuation

The National Institute for Health Research (NIHR) aims to improve national ‘health and wealth' by providing infrastructural support to enable clinical research in National Health Service settings in England and Wales. Cognisant of the consequences of studies' failure to achieve required numbers of participants, it also actively campaigns to promote patient awareness of research, and willingness to participate in trials. In this paper, we analyse recent NIHR campaigns and policies designed to encourage patients to participate in clinical research to interrogate how they are implicated in the national bioeconomy. In doing so we expand the notion of ‘clinical labour' to include the work of patient recruitment and highlight an emergent obligation on patients to contribute to research processes. Whereas once patient knowledge and experience may have been devalued, here we draw on the concept of ‘assetisation' (Birch 2012) to explore the emergent relationship between healthcare system and patient as research participant. We consider how patients' contribution goes beyond the provision of standardised objects of valuation so that patients themselves may be perceived as assets to, not only recipients of, the national healthcare system.     

Germline variant burden in multidrug resistance transporters is a therapy-specific predictor of survival in breast cancer patients

Multidrug resistance due to facilitated drug efflux mediated by ATP-binding cassette (ABC) transporters is a main cause for failure of cancer therapy. Genetic polymorphisms in ABC genes affect the disposition of chemotherapeutics and constitute important biomarkers for therapeutic response and toxicity. Here we correlated germline variability in ABC transporters with disease-specific survival (DSS) in 960 breast cancer (BRCA), 314 clear cell renal cell carcinoma and 325 hepatocellular carcinoma patients. We find that variant burden in ABCC1 is a strong predictor of DSS in BRCA patients, whereas candidate polymorphisms are not associated with DSS. This association is highly drug-specific for subgroups treated with the MRP1 substrates cyclophosphamide (log-rank p = 0.0011) and doxorubicin (log-rank p = 0.0088) independent of age and tumor stage, whereas no association was found in individuals treated with tamoxifen (log-rank p = 0.13). Structural mapping of significant variants revealed multiple variants at residues involved in protein stability, cofactor stabilization or substrate binding. Our results demonstrate that BRCA patients with high variant burden in ABCC1 are less prone to respond appropriately to pharmacological therapy with MRP1 substrates, thus incentivizing the consideration of genomic germline data for precision cancer medicine.     

Correction to: Diagnostic performance of four SARS-CoV-2 antibody assays in patients with COVID-19 or with bacterial and non-SARS-CoV-2 viral respiratory infections

European Journal of Clinical Microbiology & Infectious Diseases -     

Compressed sensing with signal averaging for improved sensitivity and motion artifact reduction in fluorine‐19 MRI

Fluorine‐19 (¹⁹F) MRI of injected perfluorocarbon emulsions (PFCs) allows for the non‐invasive quantification of inflammation and cell tracking, but suffers from a low signal‐to‐noise ratio and extended scan time. To address this limitation, we tested the hypotheses that a ¹⁹F MRI pulse sequence that combines a specific undersampling regime with signal averaging has both increased sensitivity and robustness against motion artifacts compared with a non‐averaged fully sampled pulse sequence, when both datasets are reconstructed with compressed sensing. As a proof of principle, numerical simulations and phantom experiments were performed on selected variable ranges to characterize the point spread function of undersampling patterns, as well as the vulnerability to noise of undersampling and reconstruction parameters with paired numbers of x signal averages and acceleration factor x (NAx ‐AFx ). The numerical simulations demonstrated that a probability density function that uses 25% of the samples to fully sample the k‐space central area allowed for an optimal balance between limited blurring and artifact incoherence. At all investigated noise levels, the Dice similarity coefficient (DSC) strongly depended on the regularization parameters and acceleration factor. In phantoms, the motion robustness of an NA8‐AF8 undersampling pattern versus NA1‐AF1 was evaluated with simulated and real motion patterns. Differences were assessed with the DSC, which was consistently higher for the NA8‐AF8 compared with the NA1‐AF1 strategy, for both simulated and real cyclic motion patterns (P < 0.001). Both strategies were validated in vivo in mice (n = 2) injected with perfluoropolyether. Here, the images displayed a sharper delineation of the liver with the NA8‐AF8 strategy than with the NA1‐AF1 strategy. In conclusion, we validated the hypotheses that in ¹⁹F MRI the combination of undersampling and averaging improves both the sensitivity and the robustness against motion artifacts.