Other Cancers

Microfluidic Biochip-Based Multiplexed Profiling of Small Extracellular Vesicles Proteins Integrated with Machine Learning for Early Disease Diagnosis.

Accurate early diagnosis is essential for preventing diseases and improving cure and survival rates. There are no reliable early-diagnosis biomarkers for most major diseases. Here, esophageal squamous cell carcinoma (ESCC) is used as a disease model to develop a platform for detecting a panel of proteomic biomarkers for accurate early diagnosis by integrating a barcode immunoassay biochip with mac...

From combination early detection to multicancer testing: shifting cancer care toward proactive prevention and interception.

Identifying the presence of tumors at a very early stage or deciphering the process underlying their development can enable the interception of pro-malignant mechanisms underpinning cancer emergence, facilitating more effective prevention. Advances in molecular profiling allow the detection of genetic, epigenetic, immune, and microenvironmental alterations associated with cancer risk. Liquid biops...

The evolving therapeutic landscape of Graves' disease in adults: present and future.

The therapeutic landscape of Graves' hyperthyroidism has been rapidly evolving in the last few years. There has been a shift worldwide towards antithy...

AG-MS3D-CNN multiscale attention guided 3D convolutional neural network for robust brain tumor segmentation across MRI protocols.

Accurate segmentation of brain tumors from multimodal Magnetic Resonance Imaging (MRI) plays a critical role in diagnosis, treatment planning, and dis...

Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center study.

OBJECTIVES: The purpose of this study is to mainly develop a predictive model based on clinicoradiological and radiomics features from preoperative ga...

Whole‑exome evolutionary profiling of osteosarcoma uncovers metastasis‑related driver mutations and generates an independently validated predictive classifier.

BACKGROUND: Osteosarcoma is the most common primary malignant bone tumor, with high invasiveness and metastatic potential and a poor prognosis in pati...

Multicenter validation of a cholangioscopy artificial intelligence for evaluation of biliary tract disease.

INTRODUCTION: Clinicians struggle to accurately classify biliary strictures as benign or malignant. Current ERCP-based sampling modalities including b...

Improving risk assessment of local failure in brain metastases patients using vision transformers - A multicentric development and validation study.

BACKGROUND AND PURPOSE: This study investigates the use of Vision Transformers (ViTs) to predict Freedom from Local Failure (FFLF) in patients with br...

A comprehensive targeted panel of 295 genes: Unveiling key disease initiating and transformative biomarkers in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a hematological malignancy that progresses from a benign precursor stage known as Monoclonal Gammopathy of Undete...

The Impact of Machine Learning Mortality Risk Prediction on Clinician Prognostic Accuracy and Decision Support: A Randomized Vignette Study.

BackgroundMachine learning (ML) algorithms may improve the prognosis for serious illnesses such as cancer, identifying patients who may benefit from e...

Evolving Role of Artificial Intelligence in Endoscopic Management of Inflammatory Bowel Disease: Diagnosis, Surveillance, and Assessment.

Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, presents substantial diagnostic and management challenges because ...

Computational modelling of aggressive B-cell lymphoma.

Decades of research into the molecular signalling determinants of B cell fates, and recent progress in characterising the genetic drivers of lymphoma,...

A robust machine learning approach to predicting remission and stratifying risk in rheumatoid arthritis patients treated with bDMARDs.

Rheumatoid arthritis (RA) is a chronic autoimmune disease affecting millions worldwide, leading to inflammation, joint damage, and reduced quality of ...

AI assistance enhances histopathologic distinction between sebaceous and squamous cell carcinoma of the eyelid.

Sebaceous gland carcinoma (SGC) and some poorly differentiated squamous cell carcinomas (SC) of the eyelid may have overlapping clinical and histopath...

Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death globally, characterized by high morbidity and poor prognosis. The complex mo...

Integrating MobileNetV3 and SqueezeNet for Multi-class Brain Tumor Classification.

Brain tumors pose a critical health threat requiring timely and accurate classification for effective treatment. Traditional MRI analysis is labor-int...

A novel hybrid vision UNet architecture for brain tumor segmentation and classification.

This paper focuses on designing and developing novel architectures termed Hybrid Vision UNet-Encoder Decoder (HVU-ED) segmenter and Hybrid Vision UNet...

Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter study.

OBJECTIVES: This study aimed to develop an interpretable, domain-generalizable deep learning model for microvascular invasion (MVI) assessment in hepa...

Identification of key genes as diagnostic biomarkers for IBD using bioinformatics and machine learning.

BACKGROUND: The pathogenesis of inflammatory bowel disease (IBD) involves complex molecular mechanisms, and achieving clinical remission remains chall...

Advancing breast cancer prediction using blockchain-secured hybrid genetic algorithm.

Feature selection using evolutionary algorithms-a well-liked technique for choosing pertinent characteristics in huge datasets is explored. In machine...