Dynamic radiomics based on contrast-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma.

OBJECTIVE: To exploit the improved prediction performance based on dynamic contrast-enhanced (DCE) MRI by using dynamic radiomics for microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS: We retrospectively included 175 and 75 HCC patients who underwent preoperative DCE-MRI from September 2019 to August 2022 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Static radiomics features were extracted from the mask, arterial, portal venous, and equilibrium phase images and used to construct dynamic features. The static, dynamic, and dynamic-static radiomics (SR, DR, and DSR) signatures were separately constructed based on the feature selection method of LASSO and classification algorithm of logistic regression. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each signature. RESULTS: In the three radiomics signatures, the DSR signature performed the best. The AUCs of the SR, DR, and DSR signatures in the training set were 0.750, 0.751 and 0.805, respectively, while in the external validation set, the corresponding AUCs were 0.706, 0756 and 0.777. The DSR signature showed significant improvement over the SR signature in predicting MVI status (training cohort: P = 0.019; validation cohort: P = 0.044). After external validation, the AUC value of the SR signature decreased from 0.750 to 0.706, while the AUC value of the DR signature did not show a decline (AUCs: 0.756 vs. 0.751). CONCLUSIONS: The dynamic radiomics had an improved effect on the MVI prediction in HCC, compared with the static DCE MRI-based radiomics models.

Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study.

OBJECTIVES: To establish and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI), and to predict microsatellite instability (MSI) status in rectal cancer patients. METHODS: A total of 199 patients with pathologically confirmed rectal cancer were included. The MSI status was confirmed by immunohistochemistry (IHC) staining. Clinical factors and laboratory data associated with MSI status were analyzed. The imaging data of 100 patients from one of the hospitals were used as the training set. The remaining 99 patients from the other two hospitals were used as the external validation set. The regions of interest (ROIs) were delineated from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI) sequence to extract the radiomics features. The Tree-based approach was used for feature selection. The models were constructed based on the four single sequences and a combination of the four sequences using the random forest (RF) algorithm. The external validation set was used to verify the generalization ability of each model. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each model. RESULTS: In the four single-series models, the CE-T1WI model performed the best. The AUCs of the T1WI, T2WI, DWI, and CE-T1WI prediction models in the training set were 0.74, 0.71, 0.71, and 0.78, respectively, while in the external validation set, the corresponding AUCs were 0.67, 0.66, 0.70, and 0.77. The prediction and generalization performance of the combined model of multi-sequences was comparable to that of the CE-T1WI model and it was better than that of the remaining three single-series models, with AUC values of 0.78 and 0.78 in the training and validation sets, respectively. CONCLUSION: The established radiomics models based on CE-T1WI or multiparametric MRI have similar predictive performance. They have the potential to predict MSI status in rectal cancer patients. KEY POINTS: • A radiomics model for the prediction of MSI status in patients with rectal cancer was established and validated using external validation. • The models based on CE-T1WI or multiparametric MRI have better predictive performance than those based on single unenhanced sequence images. • The radiomics model has the potential to suggest MSI status in rectal cancer patients; however, it is not yet a substitute for histological confirmation.

Disrupted intrinsic functional brain topology in patients with major depressive disorder.

Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.

Impaired robust interhemispheric function integration of depressive brain from REST-meta-MDD database in China.

BACKGROUND: Recently, functional homotopy (FH) architecture, defined as robust functional connectivity (FC) between homotopic regions, has been frequently reported to be altered in MDD patients (MDDs) but with divergent locations. METHODS: In this study, we obtained resting-state functional magnetic resonance imaging (R-fMRI) data from 1004 MDDs (mean age, 33.88 years; age range, 18-60 years) and 898 matched healthy controls (HCs) from an aggregated dataset from 20 centers in China. We focused on interhemispheric function integration in MDDs and its correlation with clinical characteristics using voxel-mirrored homotopic connectivity (VMHC) devised to inquire about FH patterns. RESULTS: As compared with HCs, MDDs showed decreased VMHC in visual, motor, somatosensory, limbic, angular gyrus, and cerebellum, particularly in posterior cingulate gyrus/precuneus (PCC/PCu) (false discovery rate [FDR] q < 0.002, z = -7.07). Further analysis observed that the reduction in SMG and insula was more prominent with age, of which SMG reflected such age-related change in males instead of females. Besides, the reduction in MTG was found to be a male-special abnormal pattern in MDDs. VMHC alterations were markedly related to episode type and illness severity. The higher Hamilton Depression Rating Scale score, the more apparent VMHC reduction in the primary visual cortex. First-episode MDDs revealed stronger VMHC reduction in PCu relative to recurrent MDDs. CONCLUSIONS: We confirmed a significant VMHC reduction in MDDs in broad areas, especially in PCC/PCu. This reduction was affected by gender, age, episode type, and illness severity. These findings suggest that the depressive brain tends to disconnect information exchange across hemispheres.

Reduced default mode network functional connectivity in patients with recurrent major depressive disorder.

Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

ProgPerm: Progressive permutation for a dynamic representation of the robustness of microbiome discoveries.

BACKGROUND: Identification of features is a critical task in microbiome studies that is complicated by the fact that microbial data are high dimensional and heterogeneous. Masked by the complexity of the data, the problem of separating signals (differential features between groups) from noise (features that are not differential between groups) becomes challenging and troublesome. For instance, when performing differential abundance tests, multiple testing adjustments tend to be overconservative, as the probability of a type I error (false positive) increases dramatically with the large numbers of hypotheses. Moreover, the grouping effect of interest can be obscured by heterogeneity. These factors can incorrectly lead to the conclusion that there are no differences in the microbiome compositions. RESULTS: We translate and represent the problem of identifying differential features, which are differential in two-group comparisons (e.g., treatment versus control), as a dynamic layout of separating the signal from its random background. More specifically, we progressively permute the grouping factor labels of the microbiome samples and perform multiple differential abundance tests in each scenario. We then compare the signal strength of the most differential features from the original data with their performance in permutations, and will observe a visually apparent decreasing trend if these features are true positives identified from the data. Simulations and applications on real data show that the proposed method creates a U-curve when plotting the number of significant features versus the proportion of mixing. The shape of the U-Curve can convey the strength of the overall association between the microbiome and the grouping factor. We also define a fragility index to measure the robustness of the discoveries. Finally, we recommend the identified features by comparing p-values in the observed data with p-values in the fully mixed data. CONCLUSIONS: We have developed this into a user-friendly and efficient R-shiny tool with visualizations. By default, we use the Wilcoxon rank sum test to compute the p-values, since it is a robust nonparametric test. Our proposed method can also utilize p-values obtained from other testing methods, such as DESeq. This demonstrates the potential of the progressive permutation method to be extended to new settings.

aPCoA: covariate adjusted principal coordinates analysis.

SUMMARY: In fields, such as ecology, microbiology and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide adjusted principal coordinates analysis as an easy-to-use tool, available as both an R package and a Shiny app, to improve data visualization in this context, enabling enhanced presentation of the effects of interest. AVAILABILITY AND IMPLEMENTATION: The R package 'aPCoA' and Shiny app can be accessed at https://cran.r-project.org/web/packages/aPCoA/index.html and https://biostatistics.mdanderson.org/shinyapps/aPCoA/.

Bayesian compositional regression with structured priors for microbiome feature selection.

The microbiome plays a critical role in human health and disease, and there is a strong scientific interest in linking specific features of the microbiome to clinical outcomes. There are key aspects of microbiome data, however, that limit the applicability of standard variable selection methods. In particular, the observed data are compositional, as the counts within each sample have a fixed-sum constraint. In addition, microbiome features, typically quantified as operational taxonomic units, often reflect microorganisms that are similar in function, and may therefore have a similar influence on the response variable. To address the challenges posed by these aspects of the data structure, we propose a variable selection technique with the following novel features: a generalized transformation and z-prior to handle the compositional constraint, and an Ising prior that encourages the joint selection of microbiome features that are closely related in terms of their genetic sequence similarity. We demonstrate that our proposed method outperforms existing penalized approaches for microbiome variable selection in both simulation and the analysis of real data exploring the relationship of the gut microbiome to body mass index.

A dependent Dirichlet process model for survival data with competing risks.

In this paper, we first propose a dependent Dirichlet process (DDP) model using a mixture of Weibull models with each mixture component resembling a Cox model for survival data. We then build a Dirichlet process mixture model for competing risks data without regression covariates. Next we extend this model to a DDP model for competing risks regression data by using a multiplicative covariate effect on subdistribution hazards in the mixture components. Though built on proportional hazards (or subdistribution hazards) models, the proposed nonparametric Bayesian regression models do not require the assumption of constant hazard (or subdistribution hazard) ratio. An external time-dependent covariate is also considered in the survival model. After describing the model, we discuss how both cause-specific and subdistribution hazard ratios can be estimated from the same nonparametric Bayesian model for competing risks regression. For use with the regression models proposed, we introduce an omnibus prior that is suitable when little external information is available about covariate effects. Finally we compare the models' performance with existing methods through simulations. We also illustrate the proposed competing risks regression model with data from a breast cancer study. An R package "DPWeibull" implementing all of the proposed methods is available at CRAN.

Gut Microbiome Signatures Are Predictive of Infectious Risk Following Induction Therapy for Acute Myeloid Leukemia.

BACKGROUND: The majority of studies that provide insights into the influence of the microbiome on the health of hematologic malignancy patients have concentrated on the transplant setting. Here, we sought to assess the predictive capacity of the gastrointestinal microbiome and its relationship to infectious outcomes in patients with acute myeloid leukemia (AML). METHODS: 16s rRNA-based analysis was performed on oral swabs and stool samples obtained biweekly from baseline until neutrophil recovery following induction chemotherapy (IC) in 97 AML patients. Microbiome characteristics were correlated with clinical outcomes both during and after IC completion. RESULTS: At the start of IC, higher stool Shannon diversity (hazard ratio [HR], 0.36; 95% confidence interval [CI], .18-.74) and higher relative abundance of Porphyromonadaceae (HR, 0.36; 95% CI, .18-.73) were associated with increased probability of remaining infection-free during neutropenia. A baseline stool Shannon diversity cutoff of <2 had optimal operating characteristics for predicting infectious complications during neutropenia. Although 56 patients received therapy >72 hours with a carbapenem, none of the patients had an infection with an extended spectrum ß-lactamase-producing organism. Patients who received carbapenems for >72 hours had significantly lower α-diversity at neutrophil recovery (P = .001) and were approximately 4 times more likely to have infection in the 90 days following neutrophil recovery (HR, 4.55; 95% CI, 1.73-11.93). CONCLUSIONS: Our results suggest that gut microbiome evaluation could assist with infectious risk stratification and that improved targeting of antibiotic administration during IC could decrease subsequent infectious complications in AML patients.Baseline microbiome diversity is a strong independent predictor of infection during acute myeloid leukemia induction chemotherapy (IC) among clinical and microbiome covariates. Higher baseline levels of Porphyromonadaceae appear protective against infection, while carbapenem use is associated with consequences to the microbiome and infection susceptibility post-IC.

Width determination for deep grooves based on a variable point spread function imaging model.

In three-dimensional confocal microscopy, two-dimensional width measurement can be significantly influenced by the groove height. The groove height not only results in deformation of the input light field due to the effect of edge occlusions, but also introduces a defocus error to the detection plane. This paper proposes a new, to the best of our knowledge, edge-setting method to determine groove width, which engineers the point spread function to correct for the groove edge obstruction effect and develops an edge obstruction imaging model (EOIM) based on the variable point spread function. This model gives a relationship between the groove height and the normalized intensity at the groove edge and can use this relationship to determine the groove edge position that would result from focusing at the groove's lower surface. Experimental results show that an EOIM-based width determination method is more accurate than the traditional 1/4 edge-setting method. Compared to the 1/4 edge-setting method, the deviation from a reference width measured with traceable scanning electron microscopy is reduced by a factor of 2.1 with a 1.3 times smaller standard deviation.

Uncertainty-aware prototypical learning for anomaly detection in medical images.

Anomalous object detection (AOD) in medical images aims to recognize the anomalous lesions, and is crucial for early clinical diagnosis of various cancers. However, it is a difficult task because of two reasons: (1) the diversity of the anomalous lesions and (2) the ambiguity of the boundary between anomalous lesions and their normal surroundings. Unlike existing single-modality AOD models based on deterministic mapping, we constructed a probabilistic and deterministic AOD model. Specifically, we designed an uncertainty-aware prototype learning framework, which considers the diversity and ambiguity of anomalous lesions. A prototypical learning transformer (Pformer) is established to extract and store the prototype features of different anomalous lesions. Moreover, Bayesian neural uncertainty quantizer, a probabilistic model, is designed to model the distributions over the outputs of the model to measure the uncertainty of the model's detection results for each pixel. Essentially, the uncertainty of the model's anomaly detection result for a pixel can reflect the anomalous ambiguity of this pixel. Furthermore, an uncertainty-guided reasoning transformer (Uformer) is devised to employ the anomalous ambiguity, encouraging the proposed model to focus on pixels with high uncertainty. Notably, prototypical representations stored in Pformer are also utilized in anomaly reasoning that enables the model to perceive diversities of the anomalous objects. Extensive experiments on five benchmark datasets demonstrate the superiority of our proposed method. The source code will be available in github.com/umchaohuang/UPformer.

Expanding the Reach of Personalized Medicine in Cancer Care: Current Progress and Future Directions of JCO Precision Oncology.

ASCO Editorial Fellows share insights into publication trends and future directions for JCO Precision Oncology.

Electrochemical/photoelectrochemical dual-mode aptasensor for sensitive aflatoxin B1 assay based on distance-modulation strategy using Au NPs/PCZIF-8-ZnO as sensing substrate.

Aflatoxin B1 (AFB1), a hepatotoxic and carcinogenic food contaminant, is commonly found in agricultural food. Herein, Au NPs anchored ZIF-8-derived porous carbon-ZnO (Au NPs/PCZIF-8-ZnO) was firstly synthesized to act as the sensing substrate. Then, a ratiometric electrochemical (EC) and "off-on" photoelectrochemical (PEC) dual-mode paper-based aptasensor was presented for AFB1 detection based on a distance-modulation sensing strategy. The independent signal transduction mechanisms and output mode not only broaden the dynamic detection range but also provide a self-verification to assay results, improving the sensitivity and reliability. The wide detection ranges of 0.1 pg/mL-100 ng/mL (EC mode) and 0.02 pg/mL-100 ng/mL (PEC mode) were obtained using dual-mode aptasensor, with detection limits of 36.7 and 9.3 fg/mL, respectively. The fabricated aptasensor exhibited excellent selectivity, reproducibility and stability. Furthermore, it exhibited good practicability for AFB1 assays in real samples, demonstrating great potential applications for food safety evaluation.

Green, pH-Sensitive, Highly Stretchable, and Hydrogen Bond-Dominated Ionogel for Wound Healing Activity.

Traditional hydrogel dressings generally have poor mechanical properties and stability when subjected to external stress due to the undesirable chain entanglement structure of their single valence bond compositions. Therefore, it is particularly important to develop a type of gel dressing with good mechanical strength, stability, and environment-friendly monitoring. In this work, a transparent, pH-sensitive, highly stretchable, and biocompatible anthocyanidin ionogel dressing was prepared, realizing green and accurate detection. Attributed to the antibacterial activity of the ionic liquid, the biocompatibility of the pectin, and the ability to scavenge free radicals of the anthocyanidin, the ionogel dressing exhibited excellent re-epithelialization in the 14 day wound healing process. Besides, changes in pH values monitoring of the ionogel over 3 days coincided with normal wound exudate. The obtained ionogel also showed good water retention, swelling properties, mechanical stretchability, and 5 week stability, illustrating great potential in wound dressings.

Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer.

There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches, such as the Dirichlet multinomial mixture model, in three key respects: we incorporate feature selection, learn the appropriate number of clusters from the data, and integrate information on the tree structure relating the observed features. We compare the performance of our proposed method to existing methods on simulated data designed to mimic real microbiome data. We then illustrate results obtained for our motivating data set, a clinical study aimed at characterizing the tumor microbiome of pancreatic cancer patients.

Development and testing of a polygenic risk score for breast cancer aggressiveness.

Aggressive breast cancers portend a poor prognosis, but current polygenic risk scores (PRSs) for breast cancer do not reliably predict aggressive cancers. Aggressiveness can be effectively recapitulated using tumor gene expression profiling. Thus, we sought to develop a PRS for the risk of recurrence score weighted on proliferation (ROR-P), an established prognostic signature. Using 2363 breast cancers with tumor gene expression data and single nucleotide polymorphism (SNP) genotypes, we examined the associations between ROR-P and known breast cancer susceptibility SNPs using linear regression models. We constructed PRSs based on varying p-value thresholds and selected the optimal PRS based on model r2 in 5-fold cross-validation. We then used Cox proportional hazards regression to test the ROR-P PRS's association with breast cancer-specific survival in two independent cohorts totaling 10,196 breast cancers and 785 events. In meta-analysis of these cohorts, higher ROR-P PRS was associated with worse survival, HR per SD = 1.13 (95% CI 1.06-1.21, p = 4.0 × 10-4). The ROR-P PRS had a similar magnitude of effect on survival as a comparator PRS for estrogen receptor (ER)-negative versus positive cancer risk (PRSER-/ER+). Furthermore, its effect was minimally attenuated when adjusted for PRSER-/ER+, suggesting that the ROR-P PRS provides additional prognostic information beyond ER status. In summary, we used integrated analysis of germline SNP and tumor gene expression data to construct a PRS associated with aggressive tumor biology and worse survival. These findings could potentially enhance risk stratification for breast cancer screening and prevention.

Reduced housing density improves statistical power of murine gut microbiota studies.

The gut microbiome of humans and animals is critical to host health. Mice are used to investigate the microbiome and its influences; however, the predictive value of such studies is hindered by cage effects due to coprophagy. Our objectives were to evaluate the influence of cage density on the statistical power to detect treatment-dependent effects of a selective pressure on microbiome composition. C57BL/6 mice were separated into groups of 2 or 4 mice per cage and then assigned to groups receiving enrofloxacin, broad-spectrum antibiotics, or control. Fecal samples were collected at weeks 0, 1, and 4, along with contents of the jejunum and cecum. Bacterial DNA analysis examined microbiome richness, diversity, and variability within and between cages. Statistical analyses reveal that reduced housing density consistently results in comparable susceptibility to antibiotics, reduced cage effects, and increased statistical power to detect treatment-associated effects, justifying the practice of reduced housing density.

Performance determinants of unsupervised clustering methods for microbiome data.

BACKGROUND: In microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically compared beta diversity and clustering methods commonly used in microbiome analyses. We applied these to four published datasets where highly distinct microbiome profiles could be seen between sample groups, as well a clinical dataset with less clear separation between groups. RESULTS: Although no single method outperformed the others consistently, we did identify the key scenarios where certain methods can underperform. Specifically, the Bray Curtis (BC) metric resulted in poor clustering in a dataset where high-abundance OTUs were relatively rare. In contrast, the unweighted UniFrac (UU) metric clustered poorly on dataset with a high prevalence of low-abundance OTUs. To explore these hypotheses about BC and UU, we systematically modified the properties of the poorly performing datasets and found that this approach resulted in improved BC and UU performance. Based on these observations, we rationally combined BC and UU to generate a novel metric. We tested its performance while varying the relative contributions of each metric and also compared it with another combined metric, the generalized UniFrac distance. The proposed metric showed high performance across all datasets. CONCLUSIONS: Our systematic evaluation of clustering performance in these five datasets demonstrates that there is no existing clustering method that universally performs best across all datasets. We propose a combined metric of BC and UU that capitalizes on the complementary strengths of the two metrics. Video abstract.

Transfer efficiency and impact on disease phenotype of differing methods of gut microbiota transfer.

To test causal relationships between complex gut microbiota (GM) and host outcomes, researchers frequently transfer GM between donor and recipient mice via embryo transfer (ET) rederivation, cross-fostering (CF), and co-housing. In this study, we assess the influence of the transfer method and the differences in baseline donor and recipient microbiota richness, on transfer efficiency. Additionally, recipient mice were subjected to DSS-induced chronic colitis to determine whether disease severity was affected by GM transfer efficiency or features within the GM. We found that the recipient's genetic background, the baseline richness of donor and recipient GM, and the transfer method all influenced the GM transfer efficiency. Recipient genetic background and GM both had significant effects on DSS colitis severity and, unexpectedly, the transfer method was strongly associated with differential disease severity regardless of the other factors.