DISMIR: Deep learning-based noninvasive cancer detection by integrating DNA sequence and methylation information of individual cell-free DNA reads.

Detecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel noninvasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise predictions with low-depth cfDNA sequencing data. Here we propose a novel approach named DISMIR, which can provide ultrasensitive and robust cancer detection by integrating DNA sequence and methylation information in plasma cfDNA whole-genome bisulfite sequencing (WGBS) data. DISMIR introduces a new feature termed as 'switching region' to define cancer-specific differentially methylated regions, which can enrich the cancer-related signal at read-resolution. DISMIR applies a deep learning model to predict the source of every single read based on its DNA sequence and methylation state and then predicts the risk that the plasma donor is suffering from cancer. DISMIR exhibited high accuracy and robustness on hepatocellular carcinoma detection by plasma cfDNA WGBS data even at ultralow sequencing depths. Further analysis showed that DISMIR tends to be insensitive to alterations of single CpG sites' methylation states, which suggests DISMIR could resist to technical noise of WGBS. All these results showed DISMIR with the potential to be a precise and robust method for low-cost early cancer detection.

scGraph: a graph neural network-based approach to automatically identify cell types.

MOTIVATION: Single-cell technologies play a crucial role in revolutionizing biological research over the past decade, which strengthens our understanding in cell differentiation, development and regulation from a single-cell level perspective. Single-cell RNA sequencing (scRNA-seq) is one of the most common single cell technologies, which enables probing transcriptional states in thousands of cells in one experiment. Identification of cell types from scRNA-seq measurements is a fundamental and crucial question to answer. Most previous studies directly take gene expression as input while ignoring the comprehensive gene-gene interactions. RESULTS: We propose scGraph, an automatic cell identification algorithm leveraging gene interaction relationships to enhance the performance of the cell-type identification. scGraph is based on a graph neural network to aggregate the information of interacting genes. In a series of experiments, we demonstrate that scGraph is accurate and outperforms eight comparison methods in the task of cell-type identification. Moreover, scGraph automatically learns the gene interaction relationships from biological data and the pathway enrichment analysis shows consistent findings with previous analysis, providing insights on the analysis of regulatory mechanism. AVAILABILITY AND IMPLEMENTATION: scGraph is freely available at https://github.com/QijinYin/scGraph and https://figshare.com/articles/software/scGraph/17157743. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features.

OBJECTIVES: To quantify intra-tumor heterogeneity (ITH) in non-small cell lung cancer (NSCLC) from computed tomography (CT) images. METHODS: We developed a quantitative ITH measurement-ITHscore-by integrating local radiomic features and global pixel distribution patterns. The associations of ITHscore with tumor phenotypes, genotypes, and patient's prognosis were examined on six patient cohorts (n = 1399) to validate its effectiveness in characterizing ITH. RESULTS: For stage I NSCLC, ITHscore was consistent with tumor progression from stage IA1 to IA3 (p < 0.001) and captured key pathological change in terms of malignancy (p < 0.001). ITHscore distinguished the presence of lymphovascular invasion (p = 0.003) and pleural invasion (p = 0.001) in tumors. ITHscore also separated patient groups with different overall survival (p = 0.004) and disease-free survival conditions (p = 0.005). Radiogenomic analysis showed that the level of ITHscore in stage I and stage II NSCLC is correlated with heterogeneity-related pathways. In addition, ITHscore was proved to be a stable measurement and can be applied to ITH quantification in head-and-neck cancer (HNC). CONCLUSIONS: ITH in NSCLC can be quantified from CT images by ITHscore, which is an indicator for tumor phenotypes and patient's prognosis. KEY POINTS: • ITHscore provides a radiomic quantification of intra-tumor heterogeneity in NSCLC. • ITHscore is an indicator for tumor phenotypes and patient's prognosis. • ITHscore has the potential to be generalized to other cancer types such as HNC.

cfDNApipe: a comprehensive quality control and analysis pipeline for cell-free DNA high-throughput sequencing data.

MOTIVATION: Cell-free DNA (cfDNA) is gaining substantial attention from both biological and clinical fields as a promising marker for liquid biopsy. Many aspects of disease-related features have been discovered from cfDNA high-throughput sequencing (HTS) data. However, there is still a lack of integrative and systematic tools for cfDNA HTS data analysis and quality control (QC). RESULTS: Here, we propose cfDNApipe, an easy-to-use and systematic python package for cfDNA whole-genome sequencing (WGS) and whole-genome bisulfite sequencing (WGBS) data analysis. It covers the entire analysis pipeline for the cfDNA data, including raw sequencing data processing, QC and sophisticated statistical analysis such as detecting copy number variations (CNVs), differentially methylated regions and DNA fragment size alterations. cfDNApipe provides one-command-line-execution pipelines and flexible application programming interfaces for customized analysis. AVAILABILITY AND IMPLEMENTATION: https://xwanglabthu.github.io/cfDNApipe/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A deep metric learning approach for histopathological image retrieval.

To distinguish ambiguous images during specimen slides viewing, pathologists usually spend lots of time to seek guidance from confirmed similar images or cases, which is inefficient. Therefore, several histopathological image retrieval methods have been proposed for pathologists to easily obtain images sharing similar content with the query images. However, these methods cannot ensure a reasonable similarity metric, and some of them need lots of annotated images to train a feature extractor to represent images. Motivated by this circumstance, we propose the first deep metric learning-based histopathological image retrieval method in this paper and construct a deep neural network based on the mixed attention mechanism to learn an embedding function under the supervision of image category information. With the learned embedding function, original images are mapped into the predefined metric space where similar images from the same category are close to each other, so that the distance between image pairs in the metric space can be regarded as a reasonable metric for image similarity. We evaluate the proposed method on two histopathological image retrieval datasets: our self-established dataset and a public dataset called Kimia Path24, on which the proposed method achieves recall in top-1 recommendation (Recall@1) of 84.04% and 97.89% respectively. Moreover, further experiments confirm that the proposed method can achieve comparable performance to several published methods with less training data, which hedges the shortage of annotated medical image data to some extent. Code is available at https://github.com/easonyang1996/DML_HistoImgRetrieval.

hicGAN infers super resolution Hi-C data with generative adversarial networks.

MOTIVATION: Hi-C is a genome-wide technology for investigating 3D chromatin conformation by measuring physical contacts between pairs of genomic regions. The resolution of Hi-C data directly impacts the effectiveness and accuracy of downstream analysis such as identifying topologically associating domains (TADs) and meaningful chromatin loops. High resolution Hi-C data are valuable resources which implicate the relationship between 3D genome conformation and function, especially linking distal regulatory elements to their target genes. However, high resolution Hi-C data across various tissues and cell types are not always available due to the high sequencing cost. It is therefore indispensable to develop computational approaches for enhancing the resolution of Hi-C data. RESULTS: We proposed hicGAN, an open-sourced framework, for inferring high resolution Hi-C data from low resolution Hi-C data with generative adversarial networks (GANs). To the best of our knowledge, this is the first study to apply GANs to 3D genome analysis. We demonstrate that hicGAN effectively enhances the resolution of low resolution Hi-C data by generating matrices that are highly consistent with the original high resolution Hi-C matrices. A typical scenario of usage for our approach is to enhance low resolution Hi-C data in new cell types, especially where the high resolution Hi-C data are not available. Our study not only presents a novel approach for enhancing Hi-C data resolution, but also provides fascinating insights into disclosing complex mechanism underlying the formation of chromatin contacts. AVAILABILITY AND IMPLEMENTATION: We release hicGAN as an open-sourced software at https://github.com/kimmo1019/hicGAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Umbilical cord blood-derived mesenchymal stromal cells promote myeloid-derived suppressor cell proliferation by secreting HLA-G to reduce acute graft-versus-host disease after hematopoietic stem cell transplantation.

BACKGROUND AIMS: Mesenchymal stem cells (MSCs) use multiple mechanisms to constrain both innate and adaptive immune responses to prevent graft-versus-host disease (GVHD). Myeloid-derived suppressor cells (MDSCs), as a heterogeneous population of early myeloid progenitor cells originating from bone marrow, are a naturally occurring immune regulatory population associated with inhibition of ongoing inflammatory responses, indicating their potential for GVHD therapy. There is accumulating evidence that MSCs and MDSCs do not act independently, but rather establish crosstalk. However, the role of MSCs in MDSC expansion and activation in GVHD remains unexplored. METHODS: In vitro experiments included 2 groups: peripheral blood mononuclear cells (PBMCs) after mobilization and human umbilical cord blood-derived MSCs (UCB-MSCs) co-cultured with PBMCs. The number and functional difference of MDSCs in PBMCs were determined by flow cytometry. The culture supernatants of co-cultured cells were analyzed to identify cytokines involved in MDSC proliferation. The relationship between MSCs and MDSCs was clarified in GVHD and graft-versus-leukemia (GVL) animal models. RESULTS: In vitro experiments confirmed that UCB-MSCs secreted HLA-G protein to promote and maintain the proliferation of MDSCs in peripheral blood after granulocyte colony-stimulating factor mobilization, and UCB-MSCs mediated the function of MDSCs to inhibit the proliferation of T cells and promote the proliferation of regulatory T cells. UCB-MSCs overexpressing HLA-G induced MDSC production in recipient mice, improved the ability of MDSCs to suppress T cells and further reduced acute GVHD (aGVHD) symptoms and survival time without influencing GVL effects. CONCLUSIONS: UCB-MSCs expanded MDSCs via HLA-G/Ig-like transcript 4, reducing the severity of aGVHD without affecting GVL. The immunosuppressive potential of MSCs for the treatment of aGVHD significantly affects the development of MDSCs, thereby consolidating the position of MSCs in the prevention and treatment of aGVHD.

Automatic localization and identification of mitochondria in cellular electron cryo-tomography using faster-RCNN.

BACKGROUND: Cryo-electron tomography (cryo-ET) enables the 3D visualization of cellular organization in near-native state which plays important roles in the field of structural cell biology. However, due to the low signal-to-noise ratio (SNR), large volume and high content complexity within cells, it remains difficult and time-consuming to localize and identify different components in cellular cryo-ET. To automatically localize and recognize in situ cellular structures of interest captured by cryo-ET, we proposed a simple yet effective automatic image analysis approach based on Faster-RCNN. RESULTS: Our experimental results were validated using in situ cyro-ET-imaged mitochondria data. Our experimental results show that our algorithm can accurately localize and identify important cellular structures on both the 2D tilt images and the reconstructed 2D slices of cryo-ET. When ran on the mitochondria cryo-ET dataset, our algorithm achieved Average Precision >0.95. Moreover, our study demonstrated that our customized pre-processing steps can further improve the robustness of our model performance. CONCLUSIONS: In this paper, we proposed an automatic Cryo-ET image analysis algorithm for localization and identification of different structure of interest in cells, which is the first Faster-RCNN based method for localizing an cellular organelle in Cryo-ET images and demonstrated the high accuracy and robustness of detection and classification tasks of intracellular mitochondria. Furthermore, our approach can be easily applied to detection tasks of other cellular structures as well.

DeepHistone: a deep learning approach to predicting histone modifications.

MOTIVATION: Quantitative detection of histone modifications has emerged in the recent years as a major means for understanding such biological processes as chromosome packaging, transcriptional activation, and DNA damage. However, high-throughput experimental techniques such as ChIP-seq are usually expensive and time-consuming, prohibiting the establishment of a histone modification landscape for hundreds of cell types across dozens of histone markers. These disadvantages have been appealing for computational methods to complement experimental approaches towards large-scale analysis of histone modifications. RESULTS: We proposed a deep learning framework to integrate sequence information and chromatin accessibility data for the accurate prediction of modification sites specific to different histone markers. Our method, named DeepHistone, outperformed several baseline methods in a series of comprehensive validation experiments, not only within an epigenome but also across epigenomes. Besides, sequence signatures automatically extracted by our method was consistent with known transcription factor binding sites, thereby giving insights into regulatory signatures of histone modifications. As an application, our method was shown to be able to distinguish functional single nucleotide polymorphisms from their nearby genetic variants, thereby having the potential to be used for exploring functional implications of putative disease-associated genetic variants. CONCLUSIONS: DeepHistone demonstrated the possibility of using a deep learning framework to integrate DNA sequence and experimental data for predicting epigenomic signals. With the state-of-the-art performance, DeepHistone was expected to shed light on a variety of epigenomic studies. DeepHistone is freely available in https://github.com/QijinYin/DeepHistone .

Leveraging multiple gene networks to prioritize GWAS candidate genes via network representation learning.

Genome-wide association studies (GWAS) have successfully discovered a number of disease-associated genetic variants in the past decade, providing an unprecedented opportunity for deciphering genetic basis of human inherited diseases. However, it is still a challenging task to extract biological knowledge from the GWAS data, due to such issues as missing heritability and weak interpretability. Indeed, the fact that the majority of discovered loci fall into noncoding regions without clear links to genes has been preventing the characterization of their functions and appealing for a sophisticated approach to bridge genetic and genomic studies. Towards this problem, network-based prioritization of candidate genes, which performs integrated analysis of gene networks with GWAS data, has emerged as a promising direction and attracted much attention. However, most existing methods overlook the sparse and noisy properties of gene networks and thus may lead to suboptimal performance. Motivated by this understanding, we proposed a novel method called REGENT for integrating multiple gene networks with GWAS data to prioritize candidate genes for complex diseases. We leveraged a technique called the network representation learning to embed a gene network into a compact and robust feature space, and then designed a hierarchical statistical model to integrate features of multiple gene networks with GWAS data for the effective inference of genes associated with a disease of interest. We applied our method to six complex diseases and demonstrated the superior performance of REGENT over existing approaches in recovering known disease-associated genes. We further conducted a pathway analysis and showed that the ability of REGENT to discover disease-associated pathways. We expect to see applications of our method to a broad spectrum of diseases for post-GWAS analysis. REGENT is freely available at https://github.com/wmmthu/REGENT.

Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs.

Node importance estimation (NIE) is the task of inferring the importance scores of the nodes in a graph. Due to the availability of richer data and knowledge, recent research interests of NIE have been dedicated to knowledge graphs (KGs) for predicting future or missing node importance scores. Existing state-of-the-art NIE methods train the model by available labels, and they consider every interested node equally before training. However, the nodes with higher importance often require or receive more attention in real-world scenarios, e.g., people may care more about the movies or webpages with higher importance. To this end, we introduce Label Informed ContrAstive Pretraining (LICAP) to the NIE problem for being better aware of the nodes with high importance scores. Specifically, LICAP is a novel type of contrastive learning (CL) framework that aims to fully utilize continuous labels to generate contrastive samples for pretraining embeddings. Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a nontop bin based on node importance scores, and then divide the nodes within the top bin into several finer bins also based on the scores. The contrastive samples are generated from those bins and are then used to pretrain node embeddings of KGs via a newly proposed predicate-aware graph attention networks (PreGATs), so as to better separate the top nodes from nontop nodes, and distinguish the top nodes within the top bin by keeping the relative order among finer bins. Extensive experiments demonstrate that the LICAP pretrained embeddings can further boost the performance of existing NIE methods and achieve new state-of-the-art performance regarding both regression and ranking metrics. The source code for reproducibility is available at https://github.com/zhangtia16/LICAP.

A combination of humanized anti-BCMA and murine anti-CD38 CAR-T cell therapy in patients with relapsed or refractory multiple myeloma.

Chimeric antigen receptor T (CAR-T) cells are a promising approach in hematopoietic malignancies. We evaluated the safety and efficacy of a combination of humanized anti-BCMA and murine anti-CD38 CAR-T cell therapy in patients with relapsed or refractory multiple myeloma (R/RMM). Twenty-two R/RMM patients, with a median age of 56 years and a median number of previous therapies of 8, were included in the study. Both CAR-T cells infusion doses were 2.0 × 106/kg. The overall response rate (ORR) was 90.9%, with 12 patients (54.5%) achieving a strict complete response/complete response (sCR/CR). The 24-month overall survival (OS) rate was 56.6%, and the progression-free survival (PFS) rate was 48.7%. Cytokine release syndrome (CRS) of grades 1-2 occurred in 16 patients (72.7%) and of grade ≥3 in six patients (27.3%). Immune effector cell-associated neurotoxic syndrome (ICANS) of grades 1-2 occurred in three patients (13.6%). The combination therapy is potential in R/RMM patients.Trial registration: The patients were enrolled in clinical trials registered as ChiCTR1800017051.

CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images.

Visual representation extraction is a fundamental problem in the field of computational histopathology. Considering the powerful representation capacity of deep learning and the scarcity of annotations, self-supervised learning has emerged as a promising approach to extract effective visual representations from unlabeled histopathological images. Although a few self-supervised learning methods have been specifically proposed for histopathological images, most of them suffer from certain defects that may hurt the versatility or representation capacity. In this work, we propose CS-CO, a hybrid self-supervised visual representation learning method tailored for H&E-stained histopathological images, which integrates advantages of both generative and discriminative approaches. The proposed method consists of two self-supervised learning stages: cross-stain prediction (CS) and contrastive learning (CO). In addition, a novel data augmentation approach named stain vector perturbation is specifically proposed to facilitate contrastive learning. Our CS-CO makes good use of domain-specific knowledge and requires no side information, which means good rationality and versatility. We evaluate and analyze the proposed CS-CO on three H&E-stained histopathological image datasets with downstream tasks of patch-level tissue classification and slide-level cancer prognosis and subtyping. Experimental results demonstrate the effectiveness and robustness of the proposed CS-CO on common computational histopathology tasks. Furthermore, we also conduct ablation studies and prove that cross-staining prediction and contrastive learning in our CS-CO can complement and enhance each other. Our code is made available at https://github.com/easonyang1996/CS-CO.

The Value of Metagenomic Next-Generation Sequencing in Hematological Malignancy Patients with Febrile Neutropenia After Empiric Antibiotic Treatment Failure.

Background: It was crucial to use empirical antibiotics in febrile neutropenia (FN) patients. However, most patients still died from infection due to poor efficacy. Metagenomic next-generation sequencing (mNGS) is a rapid microbiological diagnostic method. The value of mNGS in patients with FN remains to be studied, especially after empiric antibiotic treatment. Methods: We retrospectively analyzed the differences between mNGS and the traditional methods in 192 patients with hematological malignancies who have received empiric antibiotic treatment. Samples were collected when patient had chills or half an hour before peak body temperature. And we compared the differences between FN and non-FN patients, mainly including types of pathogens and the diagnostic value of different pathogens. Results: Despite receiving empirical treatment, the pathogen detection rate of mNGS was significantly higher than the traditional method (80.21% vs 25.00%, P<0.001). And it has obvious advantages in detecting mixed pathogens infection (80.21% vs 4.17%, P<0.001). Then, we found that mNGS saw more pathogens in the FN than in the non-FN group, especially fungus. 21/33 (63.63%) of FN patients was diagnosed with fungal infections. The fungal detection rate in FN was significantly higher than non-FN group (32.35% vs 12.22%, P=0.001). Besides, the sensitivity of mNGS was higher than the traditional methods in both FN and non-FN group (P<0.001), but no significant difference in specificity (P>0.05). In the FN group, empiric antibiotic treatment of 46/102 (45.10%) patients did not treat all the pathogens detected by mNGS. After adjusting the antimicrobial regimen according to the results of mNGS, the effective rate at 72 hours and 7 days was 22/46 (47.83%) and 24/102 (52.17%), respectively. Conclusion: mNGS had a significant impact on the diagnosis of infection and the second-line antimicrobial therapy in FN. mNGS plays a more important role in FN patients, especially in the diagnosis of fungal infections. Purpose: Firstly, we compared the difference between mNGS and the traditional methods in the diagnosis of infection. Secondly, we assessed the value of mNGS in FN patients by comparing it with non-FN patients, including types of pathogens and the diagnostic value of different pathogens. In order to show that mNGS plays a more important role in FN.

Toward a unified information framework for cell atlas assembly.

This perspective discusses the need and directions for the development of a unified information framework to enable the assembly of cell atlases and a revolution in medical research on the virtual body of assembled cell systems.

hECA: The cell-centric assembly of a cell atlas.

The accumulation of massive single-cell omics data provides growing resources for building biomolecular atlases of all cells of human organs or the whole body. The true assembly of a cell atlas should be cell-centric rather than file-centric. We developed a unified informatics framework for seamless cell-centric data assembly and built the human Ensemble Cell Atlas (hECA) from scattered data. hECA v1.0 assembled 1,093,299 labeled human cells from 116 published datasets, covering 38 organs and 11 systems. We invented three new methods of atlas applications based on the cell-centric assembly: "in data" cell sorting for targeted data retrieval with customizable logic expressions, "quantitative portraiture" for multi-view representations of biological entities, and customizable reference creation for generating references for automatic annotations. Case studies on agile construction of user-defined sub-atlases and "in data" investigation of CAR-T off-targets in multiple organs showed the great potential enabled by the cell-centric ensemble atlas.

DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers.

The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported enhancers in typical cell lines, are still too costly and time-consuming to perform systematic identification of enhancers specific to different cell lines. Existing computational methods, capable of predicting regulatory elements purely relying on DNA sequences, lack the power of cell line-specific screening. Recent studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation, and thus may provide useful information in identifying regulatory elements. Motivated by the aforementioned understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. Benefitting from the well-designed feature extraction mechanism and skip connection strategy, our model not only consistently outperforms existing methods in the imbalanced classification of cell line-specific enhancers against background sequences, but also has the ability to self-adapt to different sizes of datasets. Besides, with the adoption of auto-encoder, our model is capable of making cross-cell line predictions. We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs. We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers. The source code and detailed tutorial of DeepCAPE are freely available at https://github.com/ShengquanChen/DeepCAPE.

Joint retina segmentation and classification for early glaucoma diagnosis.

We propose a joint segmentation and classification deep model for early glaucoma diagnosis using retina imaging with optical coherence tomography (OCT). Our motivation roots in the observation that ophthalmologists make the clinical decision by analyzing the retinal nerve fiber layer (RNFL) from OCT images. To simulate this process, we propose a novel deep model that joins the retinal layer segmentation and glaucoma classification. Our model consists of three parts. First, the segmentation network simultaneously predicts both six retinal layers and five boundaries between them. Then, we introduce a post processing algorithm to fuse the two results while enforcing the topology correctness. Finally, the classification network takes the RNFL thickness vector as input and outputs the probability of being glaucoma. In the classification network, we propose a carefully designed module to implement the clinical strategy to diagnose glaucoma. We validate our method both in a collected dataset of 1004 circular OCT B-Scans from 234 subjects and in a public dataset of 110 B-Scans from 10 patients with diabetic macular edema. Experimental results demonstrate that our method achieves superior segmentation performance than other state-of-the-art methods both in our collected dataset and in public dataset with severe retina pathology. For glaucoma classification, our model achieves diagnostic accuracy of 81.4% with AUC of 0.864, which clearly outperforms baseline methods.

Diagnostic value of magnetic resonance and computed tomography colonography for the diagnosis of colorectal cancer: A systematic review and meta-analysis.

BACKGROUND: Surgical resection is the recommended procedure for colorectal cancer (CRC), but majority of the patients were diagnosed with advanced or metastatic CRC. Currently, there were inconsistent results about the diagnostic value of magnetic resonance colonography (MRC) and computed tomography colonography (CTC) in early CRC diagnosis. Our study conducted this meta-analysis to investigate the diagnostic value of MRC and CTC for CRC surveillance. METHODS: A comprehensive literature search was conducted in PubMed, Embase, and the Cochrane library to select relevant studies. The summary sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curves (AUC) were calculated to evaluate the diagnostic value of MRC and CTC, respectively. RESULT: Twenty-five studies including 2985 individuals were selected in the final analysis. Eight studies evaluated the diagnostic value of MRC, and 17 studies assessed CTC. The summary sensitivity, specificity, PLR, NLR, DOR, and AUC in MRC for early detection of CRC were 0.98 (95% confidence interval, CI: 0.80-1.00), 0.94 (95% CI: 0.85-0.97), 15.48 (95% CI: 6.30-38.04), 0.02 (95% CI: 0.00-0.25), 115.09 (95% CI: 15.37-862.01), and 0.98 (95% CI: 0.97-0.99), respectively. In addition, the sensitivity, specificity, PLR, NLR, DOR, and AUC of CTC for diagnosing CRC were 0.97 (95% CI: 0.88-0.99), 0.99 (95% CI: 0.99-1.00), 154.11 (95% CI: 67.81-350.22), 0.03 (95% CI: 0.01-0.13), 642.51 (95% CI: 145.05-2846.02), and 1.00 (95% CI: 0.99-1.00). No significant differences were found between MRC and CTC for DOR in all the subsets. CONCLUSION: The findings of meta-analysis indicated that MRC and CTC have higher diagnostic values for early CRC diagnosis. However, the DOR for diagnosing CRC between MRC and CTC showed no significance.

Empirical antifungal treatment for diagnosed and undiagnosed invasive fungal disease in patients with hematologic malignancies.

BACKGROUND: Empirical antifungal therapy is effective in some patients with risk factors for invasive fungal disease (IFD) who do not qualify for the EORTC/MSG criteria for IFD, but who fail to respond to anti-bacterial and anti-viral therapy. OBJECTIVE: This retrospective single-center study investigated the epidemiology of IFD and empirical antifungal therapy in patients with hematological malignancies. METHODS: This study recruited 893 patients with hematologic malignancies who had failed to respond to anti-bacterial and anti-viral treatment and received antifungal therapy, but not for antifungal prophylaxis. Antifungal therapy regimens included amphotericin B, voriconazole, itraconazole and caspofungin. A total of 689 patients were diagnosed with proven, probable, or possible IFD, while 159 patients did not meet the EORTC/MSG criteria for IFD diagnosis but recovered with antifungal treatment, and 45 were excluded from having IFD. Effective treatment was defined as the disappearance or resolution of clinical symptoms of IFD. RESULTS: Patients diagnosed with IFD underwent chemotherapy at a higher proportion, and had significantly higher neutrophil counts compared to those who did not qualify for the EORTC/MSG criteria for IFD but responded to antifungals. The mortality due to all causes within 3 months was significantly higher for patients diagnosed with proven IFD, compared with those who did not qualify for the EORTC/MSG criteria for IFD. There was no discontinuation reported due to adverse events of caspofungin. CONCLUSION: Empirical antifungal treatment could help save the lives of some patients with severe infections who are strongly suspected of having IFD.