Oncology/Hematology

Multiparametric MRI-Based Integrated Analysis of Clinical, Radiomics, Deep Learning, and Machine Learning for Predicting Tumor Proliferation and Prognosis in Locally Advanced Rectal Cancer.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a predictive model integrating clinical, radiomics, deep learning (DL), and machine learning (ML) from multiparametric magnetic resonance imaging (MRI) for predicting tumor cell proliferation status and prognosis in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: A total of 384 LARC patients from three c...

Radiopathomic Graph Deep Learning for Multiscale Spatial-Contextual Modeling of Intratumoral Heterogeneity to Predict Breast Cancer Response to Neoadjuvant Therapy.

Purpose To develop an explainable radio-pathomic graph deep-learning (RPGDL) system for multiscale spatial-contextual modeling of intratumoral heterogeneity (ITH) and evaluate its performance for the prediction of pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer (BC). Materials and Methods The RPGDL system was developed from dual-center retrospective analysis of pat...

Network toxicology and single-cell transcriptomics nominate candidate pyrethroid-associated targets and pathways in clear cell renal cell carcinoma.

Pyrethroid insecticides are widely used in agricultural and domestic settings. Increasing evidence suggests that pyrethroid exposure may harm multiple...

Integrated multi-omics and experimental validation for identifying novel biomarkers of acute myocardial infarction.

Acute myocardial infarction (AMI) remains a leading cause of morbidity and mortality, and early diagnosis and personalized therapy are constrained by ...

Digital and AI-enhanced psychosocial interventions in breast cancer patients: a systematic review and meta-analysis.

PURPOSE: Digital and AI-supported psychosocial interventions are increasingly implemented to address psychological distress among women with breast ca...

Uncertainty-Aware Multi-Class Brain Tumor Segmentation Using Bayesian U-Net Variants.

Accurate segmentation of brain tumors in magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, and surgical guidance. Altho...

Unsupervised clustering of clinical and laboratory features of systemic lupus erythematosus: insights from a multicentre cohort.

OBJECTIVE: This study explored the relationship between clinical phenotypes and immuno-molecular features of systemic lupus erythematosus (SLE) using ...

MSF-VMDNet for multi class segmentation of skin cancer whole slide images using a multi frequency dual encoder network.

Skin cancer has become a global public health issue. Dermoscopy is a routine diagnostic method; however, to improve accuracy, it is often combined wit...

Impact of an AI prognostic tool on clinician performance in colorectal liver metastases.

While thousands of AI prediction models are published annually, few are adopted into routine practice, partly because improved statistical performance...

Multi-omics approaches reveal erythroid progenitor cell in cancer: from passive bystander to active player.

Cancer remains a leading cause of human mortality worldwide, imposing a substantial public health burden. A deep understanding of the tumor microenvir...

YASA automated sleep staging performance across seven nights of normal sleep and sleep restriction.

STUDY OBJECTIVES: This study aimed to compare YASA's automated sleep staging to manual staging in the context of a multi-night experimental sleep rest...

Deep learning for adaptive chemotherapy: A DDPG-based approach to optimizing tumor-immune dynamics.

In this article, we propose a deep reinforcement learning based chemotherapy regulation framework to realize personalized and dynamic optimization of ...

Transforming oncology clinical trial matching through neuro-symbolic, multi-agent AI and an oncology-specific knowledge graph: a prospective evaluation in 3804 patients.

BACKGROUND: Clinical trial enrollment in oncology remains critically low, with fewer than 5% of eligible adults participating, in large part due to th...

Investigating fine-tuning versus zero-shot learning for general large language models when predicting cancer survival from initial oncology consultation documents.

BACKGROUND: Unstructured oncology consultation notes contain rich clinical information that may support survival prediction. Open-weight large languag...

Neural Network Machine Learning for Determining Surgical Appropriateness in Head and Neck Subspecialty Referrals.

OBJECTIVE: Timely, accurate referrals to head and neck cancer surgery are essential for survival but are often delayed or misrouted, contributing to l...

EFSUMB Guidelines on Multiparametric Ultrasound Thyroid Nodule Evaluation: PART I.

Thyroid nodules are common incidental findings, but only a small proportion of cases are malignant (4-6.5%) or symptomatic. Numerous follow-up examina...

EFSUMB Guidelines on Multiparametric Ultrasound Thyroid Nodule Evaluation: PART II.

Thyroid nodules are common incidental findings but only a small proportion of cases are malignant (4-6.5%) or symptomatic. Numerous follow-up examinat...

CT-Free Quantitative Thyroid SPECT Based on Artificial Intelligence: A Prospective Multicenter Noninferiority Clinical Trial.

BACKGROUND: Hybrid single-photon emission computed tomography (SPECT)/computed tomography (CT) is used for the differential diagnosis of thyrotoxicosi...

Medical image pretraining-based transfer learning for generalizable and robust diagnosis of bone tumors on radiographs: a multi-center study.

OBJECTIVES: To develop a generalizable and robust deep learning model for bone tumor classification in radiographs by leveraging domain-specific medic...

MOGANet: A Multi-omics Graph Attention Network for Cancer Diagnosis and Biomarker Identification.

Multi-omics integration holds considerable promise for advancing disease understanding and improving the performance of biomedical classification task...