Use of chaos concept in medical image segmentation

Multimodal Drug Target Binding Affinity Prediction Using Graph Local Substructure

Predicting the binding affinity of drug target is essential to reduce drug development costs and cycles. Recently, several deep learning-based methods have been proposed to utilize the structural or sequential information of drugs and targets to predict the drug-target binding affinity (DTA). However, methods that rely solely on sequence features do not consider hydrogen atom data, which may result in information loss. Graph-based methods may contain information that is not directly related to the prediction process. Additionally, the lack of structured division can limit the representation of characteristics. To address these issues, we propose a multimodal DTA prediction model using graph local substructures, called MLSDTA. This model comprehensively integrates the graph and sequence modal information from drugs and targets, achieving multimodal fusion through a cross-attention approach for multimodal features. Additionally, adaptive structure aware pooling is applied to generate graphs containing local substructural information. The model also utilizes the DropNode strategy to enhance the distinctions between different molecules. Experiments on two benchmark datasets have shown that MLSDTA outperforms current state-of-the-art models, demonstrating the feasibility of MLSDTA.

Development and validation of a CT-based nomogram for accurate hepatocellular carcinoma detection in high risk patients

Purpose

To establish and validate a CT-based nomogram for accurately detecting HCC in patients at high risk for the disease.

Methods

A total of 223 patients were divided into training (n=161) and validation (n=62) cohorts between January of 2017 and May of 2022. Logistic analysis was performed, and clinical model and radiological model were developed separately. Finally, a nomogram was established based on clinical and radiological features. All models were evaluated using the area under the curve (AUC). DeLong's test was used to evaluate the differences among these models.

Results

In the multivariate analysis, gender (p = 0.014), increased Alpha-fetoprotein (AFP) (p = 0.017), non-rim arterial phase hyperenhancement (APHE) (p = 0.011), washout (p = 0.011), and enhancing capsule (p = 0.001) were the independent differential predictors of HCC. A nomogram was formed with well-fitted calibration curves based on these five factors. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.961(95%CI: 0.935~0.986) and 0.979 (95% CI: 0.949~1), respectively. The nomogram outperformed the clinical and the radiological models in training and validation cohorts.

Conclusion

The nomogram incorporating clinical and CT features can be a simple and reliable tool for detecting HCC and achieving risk stratification in patients at high risk for HCC.

Development of a flexible liver phantom for hepatocellular carcinoma treatment planning: a useful tool for training & education

PURPOSE

Hepatocellular carcinoma (HCC) is one of the most common types of liver cancer that could potentially be surrounded by healthy arteries or veins that a surgeon would have to avoid during treatment. A realistic 3D liver model is an unmet need for HCC preoperative planning.

METHODS

This paper presents a method to create a soft phantom model of the human liver with the help of a 3D-printed mold, silicone, ballistic gel, and a blender.

RESULTS

For silicone, the elastic modulus of seven different ratios of base silicone and silicone hardener are tested; while for ballistic gel, a model using 20% gelatin and 10% gelatin is created for the tumor and the rest of the liver, respectively. It is found that the silicone modulus of elasticity matches with the real liver modulus of elasticity. It is also found that the 10% gelatin part of the ballistic gel model is an excellent emulation of a healthy human liver.

CONCLUSION

The 3D flexible liver phantom made from a 10% gelatin-to-water mixture demonstrates decent fidelity to real liver tissue in terms of texture and elasticity. It holds significant potential for improving medical training, preoperative planning, and surgical research. We believe that continued development and validation of such models could further enhance their utility and impact in the field of hepatobiliary treatment planning and education.

Hypoxia-based classification and prognostic signature for clinical management of hepatocellular carcinoma

OBJECTIVE

Intratumoral hypoxia is an essential feature of hepatocellular carcinoma (HCC). Herein, we investigated the hypoxia-based heterogeneity and relevant clinical implication in HCC.

METHODS

Three HCC cohorts: TCGA-LIHC, LICA-FR, and LIRI-JP were retrospectively gathered. Consensus clustering analysis was utilized for hypoxia-based classification based upon transcriptome of hypoxia genes. Through LASSO algorithm, a hypoxia-relevant prognostic signature was built. Immunotherapeutic response was inferred through analyzing immune checkpoints, T cell inflamed score, TIDE score, and TMB score. RNF145 expression was measured in normoxic or hypoxic HCC cells. In RNF145-knockout cells, CCK-8, TUNEL, and scratch tests were implemented.

RESULTS

HCC patients were classified into two hypoxia subtypes, with more advanced stages and poorer prognosis in cluster2 than cluster1. The heterogeneity in tumor infiltrating immune cells and genetic mutation was found between subtypes. The hypoxia-relevant prognostic model was proposed, composed of ANLN, CBX2, DLGAP5, FBLN2, FTCD, HMOX1, IGLV1-44, IL33, LCAT, LPCAT1, MKI67, PFN2, RNF145, S100A9, and SPP1). It was predicted that high-risk patients presented worse prognosis with an independent and reliable manner. Based upon high expression of immune checkpoints (CD209, CTLA4, HAVCR2, SIRPA, TNFRSF18, TNFRSF4, and TNFRSF9), high T cell inflamed score, low TIDE score and high TMB score, high-risk patients might respond to immunotherapy. Experimental validation showed that RNF145 was upregulated in hypoxic HCC cells, RNF145 knockdown attenuated proliferation and migration, but aggravated apoptosis in HCC cells.

CONCLUSION

Altogether, the hypoxia-based classification and prognostic signature might be useful for prognostication and guiding treatment of HCC.

Practical utility of liver segmentation methods in clinical surgeries and interventions

Clinical imaging (e.g., magnetic resonance imaging and computed tomography) is a crucial adjunct for clinicians, aiding in the diagnosis of diseases and planning of appropriate interventions. This is especially true in malignant conditions such as hepatocellular carcinoma (HCC), where image segmentation (such as accurate delineation of liver and tumor) is the preliminary step taken by the clinicians to optimize diagnosis, staging, and treatment planning and intervention (e.g., transplantation, surgical resection, radiotherapy, PVE, embolization, etc). Thus, segmentation methods could potentially impact the diagnosis and treatment outcomes. This paper comprehensively reviews the literature (during the year 2012-2021) for relevant segmentation methods and proposes a broad categorization based on their clinical utility (i.e., surgical and radiological interventions) in HCC. The categorization is based on the parameters such as precision, accuracy, and automation.

A review on treatments of hepatocellular carcinoma—role of radio wave ablation and possible improvements

Background

Currently, several treatment options are available for liver cancer depending on various factors such as location, size, shape, and liver function. Image fusion is required for the diagnosis, intervention, and follow-up of certain HCCs. Presently, mental fusion is the only way while diagnosing liver lesions by comparing the ultrasound (US) image with the computed tomography (CT) image. Nevertheless, mental fusion is bound to have errors. The objective of this paper is to study the present treatment options for hepatocellular carcinoma and review the present treatment options, list out their potential limitations, and present a possible alternative solution based on the findings to reduce errors and mistargeting.

Methods

This is a systematic review on the present treatment options for hepatocellular carcinoma, especially radio wave ablation.

Results

It is found that computer fusion is the possible alternative to the present mental registration.

Conclusions

Although computer fusion is the best alternative to use radio wave ablation, there have been a few open-ended questions to further explore.

Nonisotropic chaotic vibrations of a 2D hyperbolic PDE

Little seems to be known about the chaos of the two-dimensional (2D) hyperbolic partial differential equations (PDEs). The objective of this paper is to study the nonisotropic chaotic vibrations of a system governed by a 2D linear hyperbolic PDE with mixed derivative terms (MDTs) and a nonlinear boundary condition (NBC), where the interaction between MDTs and NBC causes the energy of such a system to rise and fall. The 2D hyperbolic system is proved to be topologically conjugate with the corresponding Riemann invariants, which are rigorously proved to be chaotic. Two numerical examples are carried out to demonstrate the theoretical results.