Prediction of lymphoma response to CAR T cells by deep learning-based image analysis.

Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas. We therefore tested the feasibility of a deep-learning (DL)-based image analysis methodology on pre-treatment diagnostic computed tomography (dCT), low-dose CT (lCT), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images and rule-based reasoning to predict treatment response to chimeric antigen receptor (CAR) T-cell therapy in B-cell lymphomas. Pre-treatment images of 770 lymph node lesions from 39 adult patients with B-cell lymphomas treated with CD19-directed CAR T-cells were analyzed. Transfer learning using a pre-trained neural network model, then retrained for a specific task, was used to predict lesion-level treatment responses from separate dCT, lCT, and FDG-PET images. Patient-level response analysis was performed by applying rule-based reasoning to lesion-level prediction results. Patient-level response prediction was also compared to prediction based on the international prognostic index (IPI) for diffuse large B-cell lymphoma. The average accuracy of lesion-level response prediction based on single whole dCT slice-based input was 0.82+0.05 with sensitivity 0.87+0.07, specificity 0.77+0.12, and AUC 0.91+0.03. Patient-level response prediction from dCT, using the "Majority 60%" rule, had accuracy 0.81, sensitivity 0.75, and specificity 0.88 using 12-month post-treatment patient response as the reference standard and outperformed response prediction based on IPI risk factors (accuracy 0.54, sensitivity 0.38, and specificity 0.61 (p = 0.046)). Prediction of treatment outcome in B-cell lymphomas from pre-treatment medical images using DL-based image analysis and rule-based reasoning is feasible. This approach can potentially provide clinically useful prognostic information for decision-making in advance of initiating CAR T-cell therapy.

Assessment of Regional Functional Effects of Surgical Treatment in Thoracic Insufficiency Syndrome via Dynamic Magnetic Resonance Imaging.

BACKGROUND: Quantitative regional assessment of thoracic function would enable clinicians to better understand the regional effects of therapy and the degree of deviation from normality in patients with thoracic insufficiency syndrome (TIS). The purpose of this study was to determine the regional functional effects of surgical treatment in TIS via quantitative dynamic magnetic resonance imaging (MRI) in comparison with healthy children. METHODS: Volumetric parameters were derived via 129 dynamic MRI scans from 51 normal children (November 2017 to March 2019) and 39 patients with TIS (preoperatively and postoperatively, July 2009 to May 2018) for the left and right lungs, the left and right hemi-diaphragms, and the left and right hemi-chest walls during tidal breathing. Paired t testing was performed to compare the parameters from patients with TIS preoperatively and postoperatively. Mahalanobis distances between parameters of patients with TIS and age-matched normal children were assessed to evaluate the closeness of patient lung function to normality. Linear regression functions were utilized to estimate volume deviations of patients with TIS from normality, taking into account the growth of the subjects. RESULTS: The mean Mahalanobis distances for the right hemi-diaphragm tidal volume (RDtv) were -1.32 ± 1.04 preoperatively and -0.05 ± 1.11 postoperatively (p = 0.001). Similarly, the mean Mahalanobis distances for the right lung tidal volume (RLtv) were -1.12 ± 1.04 preoperatively and -0.10 ± 1.26 postoperatively (p = 0.01). The mean Mahalanobis distances for the ratio of bilateral hemi-diaphragm tidal volume to bilateral lung tidal volume (BDtv/BLtv) were -1.68 ± 1.21 preoperatively and -0.04 ± 1.10 postoperatively (p = 0.003). Mahalanobis distances decreased after treatment, suggesting reduced deviations from normality. Regression results showed that all volumes and tidal volumes significantly increased after treatment (p < 0.001), and the tidal volume increases were significantly greater than those expected from normal growth for RDtv, RLtv, BDtv, and BLtv (p < 0.05). CONCLUSIONS: Postoperative tidal volumes of bilateral lungs and bilateral hemi-diaphragms of patients with TIS came closer to those of normal children, indicating positive treatment effects from the surgical procedure. Quantitative dynamic MRI facilitates the assessment of regional effects of a surgical procedure to treat TIS. LEVEL OF EVIDENCE: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Four methylation-driven genes detected by linear discriminant analysis model from early-stage colorectal cancer and their methylation levels in cell-free DNA.

The process of colorectal cancer (CRC) formation is considered a typical model of multistage carcinogenesis in which aberrant DNA methylation plays an important role. In this study, 752 methylation-driven genes (MDGs) were identified by the MethylMix package based on methylation and gene expression data of CRC in The Cancer Genome Atlas (TCGA). Iterative recursive feature elimination (iRFE) based on linear discriminant analysis (LDA) was used to determine the minimum MDGs (iRFE MDGs), which could distinguish between cancer and cancer-adjacent tissues. Further analysis indicated that the changes in methylation levels of the four iRFE MDGs, ADHFE1-Cluster1, CNRIP1-Cluster1, MAFB, and TNS4, occurred in adenoma tissues, while changes did not occur until stage IV in cell-free DNA. Furthermore, the methylation levels of iRFE MDGs were correlated with the genes involved in the reprogramming process of somatic cells to pluripotent stem cells, which is considered the common signature of cancer cells and embryonic stem cells. The above results indicated that the four iRFE MDGs may play roles in the early stage of colorectal carcinogenesis and highlighted the complicated relationship between tissue DNA and cell-free DNA (cfDNA).

A Retrospective Analysis of Internet-Based Sharing Nursing Service Appointment Data.

Aims: To investigate the historical data of the "Internet+ Nursing" service platform and provide a theoretical basis to optimize the "Internet+ Nursing" service model by analyzing a population in need of nursing care services, service prices, services in demand, willingness to place orders, and feedback on use. Methods: A retrospective analysis of data related to home care services on the "Jiuzhou Nursing Care" platform from April 2020 to August 2021, a total of 279 person-times, relevant information about the research subjects, and the status of home care services was conducted. SPSS 24.0 software was used for data analyses, such as calculating frequencies and percentages and conducting chi-square tests. Results: The "Jiuzhou Online Nurse" primarily serves elderly patients, and the majority of these patients have lost their ability to care for themselves. The average cost of nursing services was ¥183.45, and the unit cost of services had no effect on the number of service items. This particular internet-based home nursing service has a high level of satisfaction. Patients aged 60 to 74 have the highest number of Internet-based home care service orders (χ 2 = 11.791, P < 0.05). Patients who reuse the platform are more willing to assign people to provide services (χ 2 = 238.078, P < 0.05). Patients who were unable to care for themselves had a higher rate of repeat order (χ 2 = 10.877, P < 0.05). Conclusion: The "Internet+ Nursing" service platform specifically meets the individual needs of elderly patients, provides them with home nursing services, and improves local medical treatment and door-to-door services. This platform also provides convenience for elderly individuals who cannot care for themselves so that they can receive prompt treatment and assistance to improve their quality of life.

Remote Sensing Image Recognition Based on LOG-T-SSA-LSSVM and AE-ELM Network.

Aiming at the influence of different working conditions on recognition accuracy in remote sensing image recognition, this paper adopts hierarchical strategy to construct a network. Firstly, in order to establish the classification relationship between different samples, labeled samples are used for classification. A Logistic-T-distribution-Sparrow Search Algorithm-Least Squares Support Vector Machines (LOG-T-SSA-LSSVM) classification network is proposed. LOG-T-SSA algorithm is used to optimize parameters in LSSVM to establish a better network to achieve accurate classification between sample sets and then identify according to different categories. Through UCI dataset test, the accuracy of LOG-T-SSA-LSSVM network classification is significantly improved compared with that of contrast network. The autoencoder is integrated with Extreme Learning Machine, and the autoencoder is used to realize data compression. The advantages of Extreme Learning Machine (ELM) network, such as less training parameters, fast learning speed, and strong generalization ability, are fully utilized to realize efficient and supervised recognition. Experiments verify that the autoencoder-extreme learning machine (AE-ELM) network has a good recognition effect when the sigmoid activation function is selected and the number of hidden layer neurons are 2000. Finally, after image recognition under different working conditions, it is proved that the recognition accuracy of AE-ELM based on LOG-T-SSA-LSSVM classification is significantly improved compared with traditional ELM network and Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) network.

Construction of a Necroptosis-Related lncRNA Signature for Predicting Prognosis and Immune Response in Kidney Renal Clear Cell Carcinoma.

Necroptosis is a new type of programmed cell death and involves the occurrence and development of various cancers. Moreover, the aberrantly expressed lncRNA can also affect tumorigenesis, migration, and invasion. However, there are few types of research on the necroptosis-related lncRNA (NRL), especially in kidney renal clear cell carcinoma (KIRC). In this study, we analyzed the sequencing data obtained from the TGCA-KIRC dataset, then applied the LASSO and COX analysis to identify 6 NRLs (AC124854.1, AL117336.1, DLGAP1-AS2, EPB41L4A-DT, HOXA-AS2, and LINC02100) to construct a risk model. Patients suffering from KIRC were divided into high- and low-risk groups according to the risk score, and the patients in the low-risk group had a longer OS. This signature can be used as an indicator to predict the prognosis of KIRC independent of other clinicopathological features. In addition, the gene set enrichment analysis showed that some tumor and immune-associated pathways were more enriched in a high-risk group. We also found significant differences between the high and low-risk groups in the infiltrating immune cells, immune functions, and expression of immune checkpoint molecules. Finally, we use the "pRRophetic" package to complete the drug sensitivity prediction, and the risk score could reflect patients' response to 8 small molecule compounds. In general, NRLs divided KIRC into two subtypes with different risk scores. Furthermore, this signature based on the 6 NRLs could provide a promising method to predict the prognosis and immune response of KIRC patients. To some extent, our findings helped give a reference for further research between NRLs and KIRC and find more effective therapeutic drugs for KIRC.

A Minimally Interactive Method for Labeling Respiratory Phases in Free-Breathing Thoracic Dynamic MRI for Constructing 4D Images.

OBJECTIVE: Determination of end-expiration (EE) and end-inspiration (EI) time points in the respiratory cycle in free-breathing slice image acquisitions of the thorax is one key step needed for 4D image construction via dynamic magnetic resonance imaging. The purpose of this paper is to realize the automation of the labeling process. METHODS: The diaphragm is used as a surrogate for tracking respiratory motion and determining the state of breathing. Regions of interest (ROIs) containing the hemi-diaphragms are set by human interaction to compute the optical flow matrix between two adjacent 2D time slices. Subsequently, our approach examines the diaphragm speed and direction and by considering the change in the optical flow matrix, the EE or EI points are detected. RESULTS AND CONCLUSION: The labeling accuracy for the lateral aspect of the left lung and the lateral aspect of the right lung (0.63±0.71) is significantly lower (P < 0.05) than the accuracy for other positions (0.42±0.44), but the error in almost all scenarios is less than 1 time point. By comparing between automatic and manual labeling in 12 scenarios, we found out that 9 scenarios showed no significant difference (P > 0.05) between two methods. Overall, our method is found to be highly agreeable with manual labeling and greatly shortens the labeling time, requiring less than 8 minutes/ study compared to 4 hours/ study for manual labeling. SIGNIFICANCE: Our method achieves automatic labeling of EE and EI points without the need for use of patientinternal or external markers.

A Tent Marine Predators Algorithm with Estimation Distribution Algorithm and Gaussian Random Walk for Continuous Optimization Problems.

The marine predators algorithm (MPA) is a novel population-based optimization method that has been widely used in real-world optimization applications. However, MPA can easily fall into a local optimum because of the lack of population diversity in the late stage of optimization. To overcome this shortcoming, this paper proposes an MPA variant with a hybrid estimation distribution algorithm (EDA) and a Gaussian random walk strategy, namely, HEGMPA. The initial population is constructed using cubic mapping to enhance the diversity of individuals in the population. Then, EDA is adapted into MPA to modify the evolutionary direction using the population distribution information, thus improving the convergence performance of the algorithm. In addition, a Gaussian random walk strategy with medium solution is used to help the algorithm get rid of stagnation. The proposed algorithm is verified by simulation using the CEC2014 test suite. Simulation results show that the performance of HEGMPA is more competitive than other comparative algorithms, with significant improvements in terms of convergence accuracy and convergence speed.

Thoracic Quantitative Dynamic MRI to Understand Developmental Changes in Normal Ventilatory Dynamics.

BACKGROUND: A database of normative quantitative measures of regional thoracic ventilatory dynamics, which is essential to understanding better thoracic growth and function in children, does not exist. RESEARCH QUESTION: How to quantify changes in the components of ventilatory pump dynamics during childhood via thoracic quantitative dynamic MRI (QdMRI)? STUDY DESIGN AND METHODS: Volumetric parameters were derived via 51 dynamic MRI scans for left and right lungs, hemidiaphragms, and hemichest walls during tidal breathing. Volume-based symmetry and functional coefficients were defined to compare left and right sides and to compare contributions of the hemidiaphragms and hemichest walls with tidal volumes (TVs). Statistical analyses were performed to compare volume components among four age-based groups. RESULTS: Right thoracic components were significantly larger than left thoracic components, with average ratios of 1.56 (95% CI, 1.41-1.70) for lung TV, 1.81 (95% CI, 1.60-2.03) for hemidiaphragm excursion TV, and 1.34 (95% CI, 1.21-1.47) for hemichest wall excursion TV. Right and left lung volumes at end-expiration showed, respectively, a 44% and 48% increase from group 2 (8 ≤ age < 10) to group 3 (10 ≤ age < 12). These numbers from group 3 to group 4 (12 ≤ age ≤ 14) were 24% and 28%, respectively. Right and left hemichest wall TVs exhibited, respectively, 48% and 45% increases from group 3 to group 4. INTERPRETATION: Normal right and left ventilatory volume components have considerable asymmetry in morphologic features and dynamics and change with age. Chest wall and diaphragm contributions vary in a likewise manner. Thoracic QdMRI can provide quantitative data to characterize the regional function and growth of the thorax as it relates to ventilation.

Automatic labeling of respiratory phases and detection of abnormal respiratory signals in free-breathing thoracic dynamic MR image acquisitions based on deep learning.

4D thoracic images constructed from free-breathing 2D slice acquisitions based on dynamic magnetic resonance imaging (dMRI) provide clinicians the capability of examining the dynamic function of the left and right lungs, left and right hemi-diaphragms, and left and right chest wall separately for thoracic insufficiency syndrome (TIS) treatment [1]. There are two shortcomings of the existing 4D construction methods [2]: a) the respiratory phase corresponding to end expiration (EE) and end inspiration (EI) need to be manually identified in the dMRI sequence; b) abnormal breathing signals due to non-tidal breathing cannot be detected automatically which affects the construction process. Since the typical 2D dynamic MRI acquisition contains ~3000 slices per patient, handling these tasks manually is very labor intensive. In this study, we propose a deep-learning-based framework for addressing both problems via convolutional neural networks (CNNs) [3] and Long Short-Term Memory (LSTM) [4] models. A CNN is used to extract the motion characteristics from the respiratory dMRI sequences to automatically identify contiguous sequences of slices representing exhalation and inhalation processes. EE and EI annotations are subsequently completed by comparing the changes in the direction of motion of the diaphragm. A LSTM network is used for detecting abnormal respiratory signals by exploiting the non-uniform motion feature sequence of abnormal breathing motions. Experimental results show the mean error of labeling EE and EI is ~0.3 dMRI time point unit (much less than one time point). The accuracy of abnormal cycle detection reaches 80.0%. The proposed approach achieves results highly comparable to manual labeling in accuracy but with close to full automation of the whole process. The framework proposed here can be readily adapted to other modalities and dynamic imaging applications.

Segmentation of 4D images via space-time neural networks.

Medical imaging techniques currently produce 4D images that portray the dynamic behaviors and phenomena associated with internal structures. The segmentation of 4D images poses challenges different from those arising in segmenting 3D static images due to different patterns of variation of object shape and appearance in the space and time dimensions. In this paper, different network models are designed to learn the pattern of slice-to-slice change in the space and time dimensions independently. The two models then allow a gamut of strategies to actually segment the 4D image, such as segmentation following just the space or time dimension only, or following first the space dimension for one time instance and then following all time instances, or vice versa, etc. This paper investigates these strategies in the context of the obstructive sleep apnea (OSA) application and presents a unified deep learning framework to segment 4D images. Because of the sparse tubular nature of the upper airway and the surrounding low-contrast structures, inadequate contrast resolution obtainable in the magnetic resonance (MR) images leaves many challenges for effective segmentation of the dynamic airway in 4D MR images. Given that these upper airway structures are sparse, a Dice coefficient (DC) of ~0.88 for their segmentation based on our preferred strategy is similar to a DC of >0.95 for large non-sparse objects like liver, lungs, etc., constituting excellent accuracy.

Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ segmentation framework is presented. The framework utilized convolution neural networks (CNN) to classify pixels. To reduce the redundant inputs, the simple linear iterative clustering (SLIC) of super-pixels and the support vector machine (SVM) classifier are introduced. To establish the perfect boundary of organs in one-pixel-level, the pixels need to be classified step-by-step. First, the SLIC is used to cut an image into grids and extract respective digital signatures. Next, the signature is classified by the SVM, and the rough edges are acquired. Finally, a precise boundary is obtained by the CNN, which is based on patches around each pixel-point. The framework is applied to abdominal CT scans of livers and high-resolution computed tomography (HRCT) scans of lungs. The experimental CT scans are derived from two public datasets (Sliver 07 and a Chinese local dataset). Experimental results show that the proposed method can precisely and efficiently detect the organs. This method consumes 38 s/slice for liver segmentation. The Dice coefficient of the liver segmentation results reaches to 97.43%. For lung segmentation, the Dice coefficient is 97.93%. This finding demonstrates that the proposed framework is a favorable method for lung segmentation of HRCT scans.

Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs.

This paper presents a novel, fully automatic approach based on a fully convolutional network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a multi-channel fully convolutional network (MC-FCN) to segment liver tumors from multiphase contrast-enhanced CT images. Because each phase of contrast-enhanced data provides distinct information on pathological features, we trained one network for each phase of the CT images and fused their high-layer features together. The proposed approach was validated on CT images taken from two databases: 3Dircadb and JDRD. In the case of 3Dircadb, using the FCN, the mean ratios of the volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root mean square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSSD) were 15.6±4.3%, 5.8±3.5%, 2.0±0.9%, 2.9±1.5mm, 7.1±6.2mm, respectively. For JDRD, using the MC-FCN, the mean ratios of VOE, RVD, ASD, RMSD, and MSSD were 8.1±4.5%, 1.7±1.0%, 1.5±0.7%, 2.0±1.2mm, 5.2±6.4mm, respectively. The test results demonstrate that the MC-FCN model provides greater accuracy and robustness than previous methods.

Molecular mechanism of fluoroquinolones resistance in Mycoplasma hominis clinical isolates.

To evaluate the molecular mechanism of fluoroquinolones resistance in Mycoplasma hominis (MH) clinical strains isolated from urogenital specimens. 15 MH clinical isolates with different phenotypes of resistance to fluoroquinolones antibiotics were screened for mutations in the quinolone resistance-determining regions (QRDRs) of DNA gyrase (gyrA and gyrB) and topoisomerase IV (parC and parE) in comparison with the reference strain PG21, which is susceptible to fluoroquinolones antibiotics. 15 MH isolates with three kinds of quinolone resistance phenotypes were obtained. Thirteen out of these quinolone-resistant isolates were found to carry nucleotide substitutions in either gyrA or parC. There were no alterations in gyrB and no mutations were found in the isolates with a phenotype of resistance to Ofloxacin (OFX), intermediate resistant to Levofloxacin (LVX) and Sparfloxacin (SFX), and those susceptible to all three tested antibiotics. The molecular mechanism of fluoroquinolone resistance in clinical isolates of MH was reported in this study. The single amino acid mutation in ParC of MH may relate to the resistance to OFX and LVX and the high-level resistance to fluoroquinolones for MH is likely associated with mutations in both DNA gyrase and the ParC subunit of topoisomerase IV.

[Detection of Rickettsia prowazekii by quantitative real-time PCR].

OBJECTIVE: To develop a quantitative real-time polymerase chain reaction (PCR) for detecting Rickettsia prowazekii. METHODS: Primers and TaqMan-MGB probes designed based on ompB gene of R. prowazekii, were used to develop this method. RESULTS: For the quantitative real-time PCR, the relationship between the values of threshold cycle (Ct) and the DNA copy number was linear (r = 0.999) and the sensitivity was about 100 times higher than that of the nested PCR for detecting the same DNA sample. The results of the genomic DNA samples of other rickettsial and bacterial agents detected by real-time PCR were all negative. DNAs extracted from blood samples of guinea pig infected with R. prowazekii were examined by real-time PCR and the positive results were obtained from some of these samples. However, the results of some samples in nested PCR assay were all negative. CONCLUSION: These results suggested that the real-time PCR was highly specific and sensitive for detection of R. prowazekii that was useful for the detection of tiny DNA of R. prowazekii in blood samples from patients suspected of having epidemic typhus.