Oncology/Hematology

Developing transferable and universal IR biomarkers for intraoperative colorectal cancer diagnosis via FTIR spectroscopy.

Histological staining has long been the gold standard for cancer detection, but it is limited by subjectivity and delayed results. Fourier Transform Infrared Spectroscopy (FTIR) has emerged as a promising technique, offering the advantages of objectivity and real-time analysis. Despite its potential, IR biomarkers developed in different studies are based on varying experimental conditions, includi...

Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.

Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of tumor regions in PBTs from MRI scans. Two pipelines were developed for segmenting enhanced tumor (ET), tumor core (TC), and whole tumor (WT) in pedia...

Combining multi-parametric MRI radiomics features with tumor abnormal protein to construct a machine learning-based predictive model for prostate cancer.

This study aims to investigate the diagnostic value of integrating multi-parametric magnetic resonance imaging (mpMRI) radiomic features with tumor ab...

Artificial Intelligence-Powered Insights into Polyclonality and Tumor Evolution.

Recent studies have revealed that polyclonality-where multiple distinct subclones cooperate during early tumor development-is a critical feature of tu...

Treatment Response Evaluation in Prostate Cancer Using PSMA PET/CT.

In recent years, there has been a headlong rush into the use of prostate-specific membrane antigen (PSMA)-targeted PET for the staging and restaging o...

Driving Multifunctional Nanomedicine Design for Non-Inflammatory Tumor Therapy with Integrated Machine Learning and Density Functional Theory.

Due to the promotive role of inflammation in tumor progression, designing multifunctional nanomedicines that synergistically combine anti-tumor and an...

Circulating Proteomic Panels for Noninvasive Diagnosis and Prognostication of Thromboangiitis Obliterans.

Thromboangiitis obliterans (TAO) is often diagnosed late and characterized by high amputation rates. TAO-specific early diagnostic and disease-staging...

Therapeutic horizons in metabolic dysfunction-associated steatohepatitis.

Metabolic dysfunction-associated steatohepatitis (MASH), the progressive inflammatory form of MASLD, is now a leading cause of chronic liver disease w...

Rhaponticin inhibits the proliferation, migration, and invasion of head and neck squamous cell carcinoma (HNSCC) cells through modulation of the IL6/STAT3 signaling pathway.

BACKGROUND: Rhaponticin, a bioactive compound derived from rhubarb, has been demonstrated anti-tumor effects in various types of cancer. However, its ...

Identification of key biomarkers and potential therapeutic drugs in nasopharyngeal carcinoma based on comprehensive bioinformatics analysis.

Nasopharyngeal carcinoma (NPC) is the most prevalent type of head- and -neck cancer, and its diagnosis and treatment are currently facing significant ...

A manganese metabolism-related gene signature stratifies prognosis and immunotherapy efficacy in kidney cancer.

BACKGROUND: Manganese modulates tumorigenesis and immune regulation. High levels of manganese may promote cancer progression. While manganese toxicity...

Machine learning based obesity and aging related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.

BACKGROUND: Stomach adenocarcinoma (STAD) is one of most common cancers with high invasiveness and poor prognosis. Obesity and aging are correlated wi...

Screening and validation of long non-coding RNAs associated with colorectal cancer based on random forest and LASSO regression algorithm.

OBJECTIVE: Colorectal cancer (CRC) ranks as the third most prevalent contributor to global disease burden and represents the second highest mortality ...

Patient radiation safety in the intensive care unit.

The aim of this commentary review was to summarize the main research evidences on radiation exposure and to underline the best clinical and radiologic...

Automatic cell classification and quantification with machine learning in immunohistochemistry images.

The incidence of lymphoma, a cancer that affects both humans and animals, has witnessed a significant increase. In response, immunohistochemistry (IHC...

A novel artificial intelligence-based system for quality monitoring during esophagogastroduodenoscopy: a multicenter randomized controlled study.

Esophagogastroduodenoscopy (EGD) is the pivotal procedure for diagnosis of upper gastrointestinal (UGI) lesions. However, significant variation in EGD...

Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions.

BACKGROUND: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with early detection playing a crucial role ...

Prognostic model of lung adenocarcinoma from the perspective of cancer-associated fibroblasts using single-cell and bulk RNA-sequencing.

Cancer-associated fibroblasts (CAFs) play important roles in the progression of lung adenocarcinoma (LUAD). We examined CAF subgroups via gene ontolog...

Ultrasound-based classification of follicular thyroid Cancer using deep convolutional neural networks with transfer learning.

This study aimed to develop and validate convolutional neural network (CNN) models for distinguishing follicular thyroid carcinoma (FTC) from follicul...

Unveiling the mechanisms and promising molecular targets of curcumin in pancreatic cancer through multi-dimensional data.

Pancreatic cancer (PC) is a highly aggressive and fatal malignancy, primarily affecting older males. Curcumin, a potential anti-cancer agent, has been...