Oncology/Hematology

AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial.

Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagno...

Machine learning-based CT radiomics enhances bladder cancer staging predictions: A comparative study of clinical, radiomics, and combined models.

BACKGROUND: Predicting the accurate preoperative staging of bladder cancer (BLCA), which markedly af...

F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer.

PURPOSE: This study was designed to develop and validate a machine learning-based, multimodality fus...

Evaluation of online chat-based artificial intelligence responses about inflammatory bowel disease and diet.

INTRODUCTION: The USA has the highest age-standardized prevalence of inflammatory bowel disease (IBD...

Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.

Deep-learning tools that extract prognostic factors derived from multi-omics data have recently cont...

Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep Learning Framework.

Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both ...

Combined expert-in-the-loop-random forest multiclass segmentation U-net based artificial intelligence model: evaluation of non-small cell lung cancer in fibrotic and non-fibrotic microenvironments.

BACKGROUND: The tumor microenvironment (TME) plays a key role in lung cancer initiation, proliferati...

Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform.

The sensitivity and specificity of current breath biomarkers are often inadequate for effective can...

Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning.

BACKGROUND AND AIMS: The recurrence of papillary thyroid carcinoma (PTC) is not unusual and associat...

Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network.

Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis fo...

A powerful machine learning approach to identify interactions of differentially abundant gut microbial subsets in patients with metastatic and non-metastatic pancreatic cancer.

Pancreatic cancer has a dismal prognosis, as it is often diagnosed at stage IV of the disease and is...

Cancer Immunotherapies Ignited by a Thorough Machine Learning-Based Selection of Neoantigens.

Identification of neoantigens, derived from somatic DNA alterations, emerges as a promising strategy...

Machine learning-based decision support model for selecting intra-arterial therapies for unresectable hepatocellular carcinoma: A national real-world evidence-based study.

IMPORTANCE: Intra-arterial therapies(IATs) are promising options for unresectable hepatocellular car...

Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto's thyroiditis through bioinformatics analysis and machine learning.

There is a growing body of evidence suggesting that Hashimoto's thyroiditis (HT) may contribute to a...

CHNet: A multi-task global-local Collaborative Hybrid Network for KRAS mutation status prediction in colorectal cancer.

Accurate prediction of Kirsten rat sarcoma (KRAS) mutation status is crucial for personalized treatm...

Artificial intelligence chatbots: Are they a reliable source for patients and families in pediatric neuro-oncology?

Artificial Intelligence and Cancer Clinical Research: III Risk Prediction Models for Febrile Neutropenia in Patients Receiving Cancer Chemotherapy.

Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023.

BACKGROUND: Recently, the application of deep learning (DL) has made great progress in various field...

Modified osprey algorithm for optimizing capsule neural network in leukemia image recognition.

The diagnosis of leukemia is a serious matter that requires immediate and accurate attention. This r...

Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation.

OBJECTIVES: This work aims to explore the impact of multicenter data heterogeneity on deep learning ...

A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained ...