Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

Background: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates.

Purpose: To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2).

Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation.

MRI image segmentation is widely used in clinical practice as a prerequisite and a key for diagnosing brain tumors. The quest for an accurate automated segmentation method for brain tumor images, aiming to ease clinical doctors' workload, has gained significant attention as a research focal point. Despite the success of fully supervised methods in brain tumor segmentation, challenges remain.

Cell division cycle 42 effector protein 4 inhibits prostate cancer progression by suppressing ERK signaling pathway.

Prostate cancer (PCa) is the most common malignancy among men worldwide. The cell division cycle 42 effector protein 4 (CDC42EP4) functions downstream of CDC42, yet its role and molecular mechanisms in PCa remain unexplored. This study aimed to elucidate the role of CDC42EP4 in the progression of PCa and its underlying mechanisms.

Magnetic Resonance Imaging Features Associated with a High and Low Expression of Tumor-Infiltrating Lymphocytes: A Stratified Analysis According to Molecular Subtypes.

A total of 457 patients, including 241 HR+/HER2- patients, 134 HER2+ patients, and 82 TN patients, were studied. The percentage of TILs in the stroma adjacent to the tumor cells was assessed using a 10% cutoff. The low TIL percentages were 82% in the HR+ patients, 63% in the HER2+ patients, and 56% in the TN patients ( < 0.

Mechanism of balloon burst during transcatheter aortic valve replacement pre-dilatation: Image observation and validation by finite element analysis.

Background: Balloon burst during transcatheter aortic valve replacement (TAVR) is serious complication. This study pioneers a novel approach by combining image observation and computer simulation validation to unravel the mechanism of balloon burst in a patient with bicuspid aortic valve (BAV) stenosis.

Method: A new computational model for balloon pre-dilatation was developed by incorporating the element failure criteria according to the Law of Laplace.

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images.

Background: Gastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements.

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

Background: Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear.

An enhanced grey wolf optimizer boosted machine learning prediction model for patient-flow prediction.

Large and medium-sized general hospitals have adopted artificial intelligence big data systems to optimize the management of medical resources to improve the quality of hospital outpatient services and decrease patient wait times in recent years as a result of the development of medical information technology and the rise of big medical data. However, owing to the impact of several elements, including the physical environment, patient, and physician behaviours, the real optimum treatment effect does not meet expectations. In order to promote orderly patient access, this work provides a patient-flow prediction model that takes into account shifting dynamics and objective rules of patient-flow to handle this issue and forecast patients' medical requirements.

Attention guided neural ODE network for breast tumor segmentation in medical images.

Breast cancer is the most common cancer in women. Ultrasound is a widely used screening tool for its portability and easy operation, and DCE-MRI can highlight the lesions more clearly and reveal the characteristics of tumors. They are both noninvasive and nonradiative for assessment of breast cancer.

Development and evaluation of machine learning models and nomogram for the prediction of severe acute pancreatitis.

Background And Aim: Severe acute pancreatitis (SAP) in patients progresses rapidly and can cause multiple organ failures associated with high mortality. We aimed to train a machine learning (ML) model and establish a nomogram that could identify SAP, early in the course of acute pancreatitis (AP).

Methods: In this retrospective study, 631 patients with AP were enrolled in the training cohort.

Ribosomal protein L22-like1 promotes prostate cancer progression by activating PI3K/Akt/mTOR signalling pathway.

Prostate cancer (PCa) is one of the most common malignancies in men. Ribosomal protein L22-like1 (RPL22L1), a component of the ribosomal 60 S subunit, is associated with cancer progression, but the role and potential mechanism of RPL22L1 in PCa remain unclear. The aim of this study was to investigate the role of RPL22L1 in PCa progression and the mechanisms involved.

Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process.

The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology.

Microcystin‑leucine arginine promotes colorectal cancer cell proliferation by activating the PI3K/Akt/Wnt/β‑catenin pathway.

Microcystin‑leucine arginine (MC‑LR) is an environmental toxin produced by cyanobacteria and is considered to be a potent carcinogen. However, to the best of our knowledge, the effect of MC‑LR on colorectal cancer (CRC) cell proliferation has never been studied. The aim of the present study was to investigate the effect of MC‑LR on CRC cell proliferation and the underlying mechanisms.

Machine learning-based radiomics for multiple primary prostate cancer biological characteristics prediction with F-PSMA-1007 PET: comparison among different volume segmentation thresholds.

Background: PET-based radiomics features could predict the biological characteristics of primary prostate cancer (PCa). However, the optimal thresholds to predict the biological characteristics of PCa are unknown. This study aimed to compare the predictive power of F-PSMA-1007 PET radiomics features at different thresholds for predicting multiple biological characteristics.

Moderate-Severe White Matter Lesion Predicts Delayed Intraventricular Hemorrhage in Intracerebral Hemorrhage.

Background: Most existing studies have focused on the correlation between white matter lesion (WML) and baseline intraventricular hemorrhage (IVH) in patients with intracerebral hemorrhage (ICH), whereas few studies have investigated the relationship between WML severity and delayed IVH after admission. This study aimed to investigate the correlation between WML severity and delayed IVH and to verify the association between WML and baseline IVH.

Methods: A total of 480 patients with spontaneous ICH from February 2018 to October 2020 were selected.

Boosted Sine Cosine Algorithm with Application to Medical Diagnosis.

The sine cosine algorithm (SCA) was proposed for solving optimization tasks, of which the way to obtain the optimal solution is mainly through the continuous iteration of the sine and cosine update formulas. However, SCA also faces low population diversity and stagnation of locally optimal solutions. Hence, we try to eliminate these problems by proposing an enhanced version of SCA, named ESCA_PSO.

Chaotic simulated annealing multi-verse optimization enhanced kernel extreme learning machine for medical diagnosis.

Classification models such as Multi-Verse Optimization (MVO) play a vital role in disease diagnosis. To improve the efficiency and accuracy of MVO, in this paper, the defects of MVO are mitigated and the improved MVO is combined with kernel extreme learning machine (KELM) for effective disease diagnosis. Although MVO obtains some relatively good results on some problems of interest, it suffers from slow convergence speed and local optima entrapment for some many-sided basins, especially multi-modal problems with high dimensions.

Microcystin-LR induced microfilament rearrangement and cell invasion by activating ERK/VASP/ezrin pathway in DU145 cells.

Microcystin-LR (MC-LR) is an environmental toxin that is synthesized by cyanobacteria and considered a potential human carcinogen. However, the role of MC-LR in prostate cancer progression has not been elucidated. The purpose of this study was to investigate the effect of MC-LR on prostate cancer cell invasion and its underlying mechanisms.

Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis.

Preoperative differentiation of complicated and uncomplicated appendicitis is challenging. The research goal was to construct a new intelligent diagnostic rule that is accurate, fast, noninvasive, and cost-effective, distinguishing between complicated and uncomplicated appendicitis. Overall, 298 patients with acute appendicitis from the Wenzhou Central Hospital were recruited, and information on their demographic characteristics, clinical findings, and laboratory data was retrospectively reviewed and applied in this study.

Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems.

There is a new nature-inspired algorithm called salp swarm algorithm (SSA), due to its simple framework, it has been widely used in many fields. But when handling some complicated optimization problems, especially the multimodal and high-dimensional optimization problems, SSA will probably have difficulties in convergence performance or dropping into the local optimum. To mitigate these problems, this paper presents a chaotic SSA with differential evolution (CDESSA).

Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm.

Kernel extreme learning machine (KELM) has been widely used in the fields of classification and identification since it was proposed. As the parameters in the KELM model have a crucial impact on performance, they must be optimized before the model can be applied in practical areas. In this study, to improve optimization performance, a new parameter optimization strategy is proposed, based on a disperse foraging sine cosine algorithm (DFSCA), which is utilized to force some portions of search agents to explore other potential regions.

BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.

Background: A wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions.

Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation.

Breast cancer is one of the most dangerous diseases for women's health, and it is imperative to provide the necessary diagnostic assistance for it. The medical image processing technology is one of the most critical of all complementary diagnostic technologies. Image segmentation is the core step of image processing, where multilevel image segmentation is considered one of the most efficient and straightforward methods.

Enzyme-induced morphological transformation of drug carriers: Implications for cytotoxicity and the retention time of antitumor agents.

Nanocarriers have been widely employed to deliver chemotherapeutic drugs for cancer treatment. However, the insufficient accumulation of nanoparticles in tumors is an important reason for the poor efficacy of nanodrugs. In this study, a novel drug delivery system with a self-assembled amphiphilic peptide was designed to respond specifically to alkaline phosphatase (ALP), a protease overexpressed in cancer cells.