Other Cancers

Improved tumor-only variant calling and mutation burden estimation with VarNet-T.

Somatic variant calling algorithms typically detect mutations in cancer genomes by comparing sequence data from a tumor sample against a matched normal sample. However, matched normal samples are often unavailable in clinical diagnostics or retrospective analyses of archival tumor samples in biobanks, compromising variant calling accuracy due to the difficulty in distinguishing somatic mutations f...

Non-invasive endometriosis staging prediction using integrated radiomics and spatiotemporal transformer model based on dynamic contrast-enhanced MRI.

Precise staging of endometriosis remains a clinical challenge, as current diagnosis depends almost entirely on laparoscopic visualization-an invasive procedure marked by considerable inter-observer disagreement and diagnostic delays. Existing non-invasive approaches, whether based on conventional machine learning with handcrafted features or end-to-end deep learning architectures, have shown limit...

Federated CT foundation models for multi-center detection of lymph node metastasis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with prognosis strongly influenced by the presence of lymph node ...

Targeting cholesterol metabolism: a core regulator of tumor-associated macrophage plasticity and immunotherapy response.

Tumor-associated macrophages (TAMs) are one of the main immunosuppressive components in the tumor microenvironment (TME). Growing evidence indicates t...

Machine learning-based programmed cell death gene signature for prognosis and drug sensitivity in breast cancer.

Breast cancer (BRCA) heterogeneity necessitates robust prognostic biomarkers. Programmed cell death (PCD) serves a key role in tumor progression and t...

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions.

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chr...

Digital Enabled Diagnosis System for Mammogram Images Classification Using Machine Learning.

Breast tumor is the most commonly detected tumors and remain one of the leading causes of cancer-related mortality among women worldwide. Although mam...

Computerized diagnosis of brain tumor using graph based CNN classification.

Magnetic resonance imaging (MRI) is essential in the accurate diagnosis of brain tumors so that sound treatment planning can be done, however, clinica...

Machine learning-based clustering differentiates bilateral hepatocellular carcinoma characteristics and prognosis.

BACKGROUND: Bilateral hepatocellular carcinoma represents a biologically heterogeneous disease with uncertain optimal surgical selection criteria. Alt...

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...

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...

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 ...

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...

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...