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Radiology Radiology

L-distance ratio: a new distance ratio-based evaluation method for the diagnosis of cirrhosis using enhanced computed tomography.

In Quantitative imaging in medicine and surgery

BACKGROUND : Early detection of liver cirrhosis is of great significance to the formulation of treatment plans and improving prognosis. Computed tomography (CT) is commonly used in the assessment of patients with chronic liver disease. In this study, we proposed a new distance ratio method for accurate diagnosis of cirrhosis using CT images.

METHODS : This was a retrospective study of a consecutive series of patients in Guangdong Provincial People's Hospital. Sixty-two patients with pathologically diagnosed cirrhosis but whose morphologic changes were insufficient to diagnose cirrhosis were included in the cirrhosis group. Those who were pathologically confirmed to be free of cirrhosis and fibrosis and without a history of chronic hepatic were classified as the control group. A total of 124 patients underwent abdominal dynamic enhanced CT. Both the L-distance ratio-the ratio of the distance from the right portal vein bifurcation point to the anterior and posterior edges of the liver-and the caudate-right lobe ratio were measured by two independent radiologists. Intraclass correlation coefficients (ICCs) were used to assess the agreement between the radiologists. Binary logistic regression was performed for univariate analysis, and the odds ratio (OR) was also calculated. The discrimination ability of the two methods was evaluated by the area under the receiver operating characteristic curve (AUC).

RESULTS : For both the L-distance ratio and the caudate-right lobe ratio, high agreement was observed between the two radiologists, although the ICC value of the L-distance ratio was slightly higher than that of the caudate-right lobe ratio (0.916 vs. 0.907). Binary logistic regression suggested that higher ratios were correlated with cirrhosis [the L-distance ratio, high vs. low OR =4.41, 95% confidence interval (CI): 2.08-9.36, P<0.001; the caudate-right lobe ratio, high vs. low OR =2.19, 95% CI: 1.07-4.49, P=0.031]. The AUCs of the L-distance ratio and the caudate-right lobe ratio were 0.823 (95% CI: 0.752-0.894) and 0.663 (95% CI: 0.569-0.757), respectively.

CONCLUSIONS : The L-distance ratio method proposed in this paper is more simple, accurate, and reliable than the caudate-right lobe ratio method in the diagnosis of cirrhosis.

Ye Huifen, Wang Qiushi, Huang Haitao, Zhao Ke, Li Pinxiong, Liu Zaiyi, Wang Guangyi, Liang Changhong

2023-Mar-01

The L-distance ratio, cirrhosis, diagnosis, liver, the caudate-right lobe ratio

General General

Fast calculation of hydrogen-bond strengths and free energy of hydration of small molecules.

In Scientific reports ; h5-index 158.0

Hydrogen bonding is an interaction of great importance in drug discovery and development as it may significantly affect chemical and biological processes including the interaction of small molecules with other molecules, proteins, and membranes. In particular, hydrogen bonding can impact drug-like properties such as target affinity and oral availability which are critical to developing effective pharmaceuticals, and therefore, numerous methods for the calculation of properties such as hydrogen-bond strengths, free energy of hydration, or water solubility have been proposed over time. However, the accessibility to efficient methods for the predictions of such properties is still limited. Here, we present the development of Jazzy, an open-source tool for the prediction of hydrogen-bond strengths and free energies of hydration of small molecules. Jazzy also allows the visualisation of hydrogen-bond strengths with atomistic resolution to support the design of compounds with desired properties and the interpretation of existing data. The tool is described in its implementation, parameter fitting, and validation against two data sets of experimental hydration free energies. Jazzy is also applied against two chemical series of bioactive compounds to show that hydrogen-bond strengths can be used to understand their structure-activity relationships. Results from the validations highlight the strengths and limitations of Jazzy, and suggest its suitability for interactive design, screening, and machine-learning featurisation.

Ghiandoni Gian Marco, Caldeweyher Eike

2023-Mar-13

General General

Benchmarking machine learning robustness in Covid-19 genome sequence classification.

In Scientific reports ; h5-index 158.0

The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome-millions of sequences and counting. This amount of data, while being orders of magnitude beyond the capacity of traditional approaches to understanding the diversity, dynamics, and evolution of viruses, is nonetheless a rich resource for machine learning (ML) approaches as alternatives for extracting such important information from these data. It is of hence utmost importance to design a framework for testing and benchmarking the robustness of these ML models. This paper makes the first effort (to our knowledge) to benchmark the robustness of ML models by simulating biological sequences with errors. In this paper, we introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show from experiments on a wide array of ML models that some simulation-based approaches with different perturbation budgets are more robust (and accurate) than others for specific embedding methods to certain noise simulations on the input sequences. Our benchmarking framework may assist researchers in properly assessing different ML models and help them understand the behavior of the SARS-CoV-2 virus or avoid possible future pandemics.

Ali Sarwan, Sahoo Bikram, Zelikovsky Alexander, Chen Pin-Yu, Patterson Murray

2023-Mar-13

General General

Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records

ArXiv Preprint

Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain. Recent work presented a promising framework that embeds entire features in raw EHR data regardless of its form and medical code standards. The framework, however, only focuses on encoding EHR with minimal preprocessing and fails to consider how to learn efficient EHR representation in terms of computation and memory usage. In this paper, we search for a versatile encoder not only reducing the large data into a manageable size but also well preserving the core information of patients to perform diverse clinical tasks. We found that hierarchically structured Convolutional Neural Network (CNN) often outperforms the state-of-the-art model on diverse tasks such as reconstruction, prediction, and generation, even with fewer parameters and less training time. Moreover, it turns out that making use of the inherent hierarchy of EHR data can boost the performance of any kind of backbone models and clinical tasks performed. Through extensive experiments, we present concrete evidence to generalize our research findings into real-world practice. We give a clear guideline on building the encoder based on the research findings captured while exploring numerous settings.

Eunbyeol Cho, Min Jae Lee, Kyunghoon Hur, Jiyoun Kim, Jinsung Yoon, Edward Choi

2023-03-15

General General

High-resolution and high-speed 3D tracking of microrobots using a fluorescent light field microscope.

In Quantitative imaging in medicine and surgery

BACKGROUND : Imaging and tracking are crucial for microrobots which navigate through complex 3D environments. Fluorescent imaging (FI) by microscope offers a high-resolution and high-sensitive imaging method to study the property of microrobots. However, conventional microscope suffers from shallow depth of field (DOF) and lacks 3D imaging capability.

METHODS : We proposed a high-resolution and high-speed 3D tracking method for microrobots based on a fluorescent light field microscope (FLFM). We designed the FLFM system according to the size of a representative helical microrobot (150 μm body length, 50 μm screw diameter), and studied the system's performance. We also proposed a 3D tracking algorithm for microrobots using digital refocusing.

RESULTS : We validated the method by simulations and built an FLFM system to perform the tracking experiments of microrobots with representative size. Our 3D tracking method achieves a 30 fps data acquisition rate, 10 μm lateral resolution and approximately 40 μm axial resolution over a volume of 1,200×1,200×326 μm3. Results indicate that the accuracy of the method can reach about 9 μm.

CONCLUSIONS : Compared with the FI by a conventional microscope, the FLFM-based method gains wider DOF and 3D imaging capability with a single-shot image. The tracking method succeeds in providing the trajectory of the microrobot with a good lateral resolution.

Lv Jiahang, Hu Yao, Zhao Hongyu, Ye Min, Ding Ning, Zhong Jingshan, Wang Xiaopu

2023-Mar-01

3D tracking, Microrobot, digital refocusing, light field microscope (LFM)

Radiology Radiology

Deep learning-based segmentation of epithelial ovarian cancer on T2-weighted magnetic resonance images.

In Quantitative imaging in medicine and surgery

BACKGROUND : Epithelial ovarian cancer (EOC) segmentation is an indispensable step in assessing the extent of disease and guiding the treatment plan that follows. Currently, manual segmentation is the most commonly used method, despite it being tedious, time-consuming and subject to inter- and intra-observer variability. This study aims to assess the feasibility of deep learning methods in the automatic segmentation of EOC on T2-weighted magnetic resonance images.

METHODS : A total of 339 EOC patients from eight different clinical centers were enrolled and divided into 4 groups: training set (n=154), validation set (n=25), internal test set (n=50) and external test set (n=110). Six common-used algorithms, including convolutional neural networks (CNNs) (U-Net, DeepLabv3, U-Net++ and PSPNet) and transformers (TransUnet and Swin-Unet), were used to conduct automatic segmentations. The performances of these automatic segmentation methods were evaluated by means of dice similarity coefficient (DSC), Hausdorff distance (HD), average symmetric surface distance (ASSD), precision and recall.

RESULTS : All the results look promising, which demonstrates the feasibility of using deep learning for EOC segmentation. Overall, CNNs and transformers showed similar performances in both internal and external test sets. Among all the models, U-Net++ performed best with a DSC, HD, ASSD, precision and recall of 0.851, 25.3, 1.75, 0.838, 0.882 and 0.740, 42.5, 4.21, 0.825, 0.725 in internal and external test sets, respectively.

CONCLUSIONS : Fully automated segmentation of EOC is possible with deep learning. The segmentation performance is related to the International Federation of Gynecology and Obstetrics (FIGO) stages and histological types of EOC.

Hu Dingdu, Jian Junming, Li Yongai, Gao Xin

2023-Mar-01

Epithelial ovarian cancer (EOC), deep learning (DL), magnetic resonance imaging, segmentation (MRI)