Oncology/Hematology

Automated cell annotation and classification on histopathology for spatial biomarker discovery.

Histopathology with hematoxylin and eosin (H&E) staining is routinely employed for clinical diagnoses. Single-cell analysis of histopathology provides a powerful tool for understanding the intricate cellular interactions underlying disease progression and therapeutic response. However, existing efforts are hampered by inefficient and error-prone human annotations. Here, we present an experimental ...

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 disease monitoring in neuro-oncology. Traditional methods of tumor segmentation, often manual and labour-intensive, are prone to inconsistencies and inter-observer variability. Recently, deep learning models, particularly Convolutional Neural Networks (...

Multi-omics analysis of the effects of pla2g4a on the prognosis of various cancers and its experimental validation in breast cancer cell lines.

BACKGROUND: Platelet-related exosomes (PREs) are microparticles secreted by platelets into the bloodstream and are implicated in various cancer proces...

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...

Interpretable machine learning models for survival prediction in prostate cancer bone metastases.

Prostate cancer bone metastasis (PCBM) is a highly lethal condition with limited survival. Accurate survival prediction is essential for managing thes...

An integrated approach for key gene selection and cancer phenotype classification: Improving diagnosis and prediction.

The identification of key features and reliable phenotype classification remains pivotal in cancer research, with direct implications for early diagno...

Machine learning models for predicting chemotherapy-induced adverse drug reactions in colorectal cancer patients.

BACKGROUND: Chemotherapy-induced adverse drug reactions (ADRs) are common in patients with colorectal cancer. We developed four machine learning model...

Interpretable artificial intelligence for modulated metasurface antenna design using SHAP and MLP.

Modulated metasurface-based leaky-wave antennas have garnered increasing attention due to their low profile, cost-efficiency, and versatility in appli...

Integrating artificial intelligence in healthcare: applications, challenges, and future directions.

Artificial intelligence (AI) has demonstrated remarkable potential in transforming medical diagnostics across various healthcare domains. This paper e...

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...

Robot-Assisted Retrograde Intrarenal Surgery: A Mini Review of the Literature on Robotic Surgery Platforms for Endoscopic Stone Surgery.

Retrograde intrarenal surgery (RIRS) has become a cornerstone in renal stone management, with robotic platforms recently entering clinical practice. T...

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,...

Multiomic integration reveals subtype-specific predictors of neoadjuvant treatment response in breast cancer.

Neoadjuvant therapy has been widely used in breast cancer, but treatment response varies among individuals. We conducted multiomic profiling on tumor ...

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...