Simulation study to evaluate when Plasmode simulation is superior to parametric simulation in estimating the mean squared error of the least squares estimator in linear regression.

Simulation is a crucial tool for the evaluation and comparison of statistical methods. How to design fair and neutral simulation studies is therefore of great interest for both researchers developing new methods and practitioners confronted with the choice of the most suitable method. The term simulation usually refers to parametric simulation, that is, computer experiments using artificial data made up of pseudo-random numbers. Plasmode simulation, that is, computer experiments using the combination of resampling feature data from a real-life dataset and generating the target variable with a known user-selected outcome-generating model, is an alternative that is often claimed to produce more realistic data. We compare parametric and Plasmode simulation for the example of estimating the mean squared error (MSE) of the least squares estimator (LSE) in linear regression. If the true underlying data-generating process (DGP) and the outcome-generating model (OGM) were known, parametric simulation would obviously be the best choice in terms of estimating the MSE well. However, in reality, both are usually unknown, so researchers have to make assumptions: in Plasmode simulation studies for the OGM, in parametric simulation for both DGP and OGM. Most likely, these assumptions do not exactly reflect the truth. Here, we aim to find out how assumptions deviating from the true DGP and the true OGM affect the performance of parametric and Plasmode simulations in the context of MSE estimation for the LSE and in which situations which simulation type is preferable. Our results suggest that the preferable simulation method depends on many factors, including the number of features, and on how and to what extent the assumptions of a parametric simulation differ from the true DGP. Also, the resampling strategy used for Plasmode influences the results. In particular, subsampling with a small sampling proportion can be recommended.

Statistical plasmode simulations-Potentials, challenges and recommendations.

Statistical data simulation is essential in the development of statistical models and methods as well as in their performance evaluation. To capture complex data structures, in particular for high-dimensional data, a variety of simulation approaches have been introduced including parametric and the so-called plasmode simulations. While there are concerns about the realism of parametrically simulated data, it is widely claimed that plasmodes come very close to reality with some aspects of the "truth" known. However, there are no explicit guidelines or state-of-the-art on how to perform plasmode data simulations. In the present paper, we first review existing literature and introduce the concept of statistical plasmode simulation. We then discuss advantages and challenges of statistical plasmodes and provide a step-wise procedure for their generation, including key steps to their implementation and reporting. Finally, we illustrate the concept of statistical plasmodes as well as the proposed plasmode generation procedure by means of a public real RNA data set on breast carcinoma patients.

Clinical impact of the genomic landscape and leukemogenic trajectories in non-intensively treated elderly acute myeloid leukemia patients.

To characterize the genomic landscape and leukemogenic pathways of older, newly diagnosed, non-intensively treated patients with AML and to study the clinical implications, comprehensive genetics analyses were performed including targeted DNA sequencing of 263 genes in 604 patients treated in a prospective Phase III clinical trial. Leukemic trajectories were delineated using oncogenetic tree modeling and hierarchical clustering, and prognostic groups were derived from multivariable Cox regression models. Clonal hematopoiesis-related genes (ASXL1, TET2, SRSF2, DNMT3A) were most frequently mutated. The oncogenetic modeling algorithm produced a tree with five branches with ASXL1, DDX41, DNMT3A, TET2, and TP53 emanating from the root suggesting leukemia-initiating events which gave rise to further subbranches with distinct subclones. Unsupervised clustering mirrored the genetic groups identified by the tree model. Multivariable analysis identified FLT3 internal tandem duplications (ITD), SRSF2, and TP53 mutations as poor prognostic factors, while DDX41 mutations exerted an exceptionally favorable effect. Subsequent backwards elimination based on the Akaike information criterion delineated three genetic risk groups: DDX41 mutations (favorable-risk), DDX41wildtype/FLT3-ITDneg/TP53wildtype (intermediate-risk), and FLT3-ITD or TP53 mutations (high-risk). Our data identified distinct trajectories of leukemia development in older AML patients and provide a basis for a clinically meaningful genetic outcome stratification for patients receiving less intensive therapies.

Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery.

Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy. While characterization of the tissue perfusion is crucial in various procedures, such as partial nephrectomy, doing so by means of visual inspection remains highly challenging. We developed a laparoscopic real-time multispectral imaging system featuring a compact and lightweight multispectral camera and the possibility to complement the conventional surgical view of the patient with functional information at a video rate of 25 Hz. To enable contrast agent-free ischemia monitoring during laparoscopic partial nephrectomy, we phrase the problem of ischemia detection as an out-of-distribution detection problem that does not rely on data from any other patient and uses an ensemble of invertible neural networks at its core. An in-human trial demonstrates the feasibility of our approach and highlights the potential of spectral imaging combined with advanced deep learning-based analysis tools for fast, efficient, reliable, and safe functional laparoscopic imaging.

Statin-based endothelial prophylaxis and outcome after allogeneic stem cell transplantation.

BACKGROUND: Allogeneic haematopoietic stem cell transplantation (alloSCT) often remains the only curative therapy for hematologic malignancies. Although the management of transplant-associated adverse events considerably improved over the last decades, nonrelapse mortality (NRM) remains a challenge, and endothelial dysfunction was identified as a major contributor to NRM. METHODS: Statin-based endothelial prophylaxis (SEP) has been implemented in the standard of care in our transplant centre to reduce NRM caused by endothelial injury. Here, we retrospectively analysed the impact of SEP on clinical outcome in a cohort of 347 alloSCT patients. RESULTS: SEP (n = 209) was associated with significantly reduced NRM (hazard ratio 0.61, 95% CI 0.38-0.96) and better overall survival (OS) after acute graft-versus-host disease (HR 0.59, 95% CI 0.37-0.93). Subgroup analyses showed that the NRM benefit was mainly found in patients with an intermediate endothelial activation and stress index (EASIX), while relapse risk was not affected. On day 100 post-alloSCT, patients receiving SEP had significantly higher levels of the rate-limiting enzyme of tryptophan metabolism, indoleamine 2,3-dioxygenase (IDO), higher kynurenine to tryptophan ratios as a proxy of IDO activity and tended to have lower levels of the endothelial injury marker ST2 (p = .055). No significant differences in interferon-gamma or IL18 levels were observed. These biomarker signatures suggest that the beneficial effects of SEP might be mediated by both endothelial protection and immunomodulation. CONCLUSIONS: Together, these data suggest that SEP improves NRM and OS post-alloSCT in particular in patients with intermediate endothelial risk and provide first mechanistic clues about its potential mode of action.

Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model.

Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method's current lack of robustness and generalizability. Specifically, the scientific community is lacking a comprehensive spectral tissue atlas, and it is unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3) We show that HSI-based fully-automatic tissue differentiation of 20 organ classes with deep neural networks is possible with high accuracy (> 95%). We conclude from our study that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decisionmaking and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.

EASIX and Severe Endothelial Complications After CD19-Directed CAR-T Cell Therapy-A Cohort Study.

Background: Endothelial dysfunction is associated with two main complications of chimeric antigen receptor T (CAR-T) cell therapy, cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS). This study evaluates the Endothelial Activation and Stress Index (EASIX) as a prognostic marker for high-grade CRS and ICANS in patients treated with CD19-directed CAR-T cells. Methods: In this retrospective study, a training cohort of 93 patients from the ZUMA-1 trial and a validation cohort of 121 patients from two independent centers (University Hospital Heidelberg, Charité University Medicine Berlin) were investigated. The primary objective was to assess the predictive capacity of EASIX measured immediately before the start of lymphodepletion (EASIX-pre) for the occurrence of grade ≥3 CRS and/or ICANS. To explore a possible endothelial link, serum levels of endothelial stress markers (angiopoietin-2, suppressor of tumorigenicity-2, soluble thrombomodulin, and interleukin-8) were determined before lymphodepletion and on day 7 after CART infusion in the validation cohort (n = 47). Results: The prognostic effect of EASIX-pre on grade ≥3 CRS and/or ICANS was significant in the training cohort [OR 2-fold increase 1.72 (1.26-2.46)] and validated in the independent cohort. An EASIX-pre cutoff >4.67 derived from the training cohort associated with a 4.3-fold increased odds ratio of severe CRS/ICANS in the independent cohort. Serum endothelial distress markers measured on day+7 correlated with EASIX-pre and associated with severe complications. Conclusions: EASIX-pre is a powerful predictor of severe CRS/ICANS after CD19-directed CART therapy and might be used as a basis for risk-adapted prevention strategies.

Semantic segmentation of multispectral photoacoustic images using deep learning.

Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectral photoacoustic measurements. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images to facilitate image interpretability. Manually annotated photoacoustic and ultrasound imaging data are used as reference and enable the training of a deep learning-based segmentation algorithm in a supervised manner. Based on a validation study with experimentally acquired data from 16 healthy human volunteers, we show that automatic tissue segmentation can be used to create powerful analyses and visualizations of multispectral photoacoustic images. Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.

Lenalidomide and dexamethasone in relapsed/refractory immunoglobulin light chain (AL) amyloidosis: results from a large cohort of patients with long follow-up.

Lenalidomide and dexamethasone (RD) is a standard treatment in relapsed/refractory immunoglobulin light chain (AL) amyloidosis (RRAL). We retrospectively investigated toxicity, efficacy and prognostic markers in 260 patients with RRAL. Patients received a median of two prior treatment lines (68% had been bortezomib-refractory; 33% had received high-dose melphalan). The median treatment duration was four cycles. The 3-month haematological response rate was 31% [very good haematological response (VGHR) in 18%]. The median follow-up was 56·5 months and the median overall survival (OS) and haematological event-free survival (haemEFS) were 32 and 9 months. The 2-year dialysis rate was 15%. VGHR resulted in better OS (62 vs. 26 months, P < 0·001). Cardiac progression predicted worse survival (22 vs. 40 months, P = 0·027), although N-terminal prohormone of brain natriuretic peptide (NT-proBNP) increase was frequently observed. Multivariable analysis identified these prognostic factors: NT-proBNP for OS [hazard ratio (HR) 1·71; P < 0·001]; gain 1q21 for haemEFS (HR 1·68, P = 0·014), with a trend for OS (HR 1·47, P = 0·084); difference between involved and uninvolved free light chains (dFLC) and light chain isotype for OS (HR 2·22, P < 0·001; HR 1·62, P = 0·016) and haemEFS (HR 1·88, P < 0·001; HR 1·59, P = 0·008). Estimated glomerular filtration rate (HR 0·71, P = 0·004) and 24-h proteinuria (HR 1·10, P = 0·004) were prognostic for renal survival. In conclusion, clonal and organ biomarkers at baseline identify patients with favourable outcome, while VGHR and cardiac progression define prognosis during RD treatment.

Best Prediction of the Additive Genomic Variance in Random-Effects Models.

The additive genomic variance in linear models with random marker effects can be defined as a random variable that is in accordance with classical quantitative genetics theory. Common approaches to estimate the genomic variance in random-effects linear models based on genomic marker data can be regarded as estimating the unconditional (or prior) expectation of this random additive genomic variance, and result in a negligence of the contribution of linkage disequilibrium (LD). We introduce a novel best prediction (BP) approach for the additive genomic variance in both the current and the base population in the framework of genomic prediction using the genomic best linear unbiased prediction (gBLUP) method. The resulting best predictor is the conditional (or posterior) expectation of the additive genomic variance when using the additional information given by the phenotypic data, and is structurally in accordance with the genomic equivalent of the classical additive genetic variance in random-effects models. In particular, the best predictor includes the contribution of (marker) LD to the additive genomic variance and possibly fully eliminates the missing contribution of LD that is caused by the assumptions of statistical frameworks such as the random-effects model. We derive an empirical best predictor (eBP) and compare its performance with common approaches to estimate the additive genomic variance in random-effects models on commonly used genomic datasets.