Oncology/Hematology

Doctor-patient communication in Chinese cancer care: a qualitative exploration.

Patient-centred care (PCC) receives less attention in developing countries like China. The aim of our study was to qualitatively explore how doctors in China communicate with patients and family in the cancer context and the extent to which they can do this within a model of patient-centred care. We conducted 10 in-depth, semi-structured interviews with doctors (across different cancer specialitie...

Fairness aware subset selection for advancing equity in skin cancer detection.

OBJECTIVES: Skin cancer is the most common malignancy in the United States, with more than five million cases diagnosed annually among 3.3 million individuals. Melanoma, the deadliest form of skin cancer, accounts for roughly 200 000 new diagnoses each year and nearly 10 000 deaths. AI-based skin cancer detection is being developed and tested in laboratory and academic settings as a promising appr...

The prevalence and clinical significance of clonal monocytosis.

The terms clonal monocytosis of undetermined significance (CMUS) and clonal cytopenia and monocytosis of undetermined significance (CCMUS) were introd...

Combined hepatocellular-cholangiocarcinoma: a contemporary pathologic and molecular perspective.

Combined hepatocellular cholangiocarcinoma (cHCC-CCA) is a rare primary liver carcinoma characterized by the unequivocal coexistence of hepatocytic an...

Integrative network pharmacology and machine learning identify potential targets of indole-3-lactic acid in colorectal cancer.

The treatment of colorectal cancer (CRC) remains challenging due to chemotherapy resistance and genetic heterogeneity. Indole-3-lactic acid (ILA), a t...

Identification of core genes mediating the association between obesity and hepatocellular carcinoma: A bioinformatics study based on mitochondrial metabolism and immune pathways.

PURPOSE: Obesity is strongly associated with hepatocellular carcinoma (HCC), yet the molecular mechanisms linking them remain unclear. This study aime...

Epitranscriptomic signatures of malignancy: how RNA modifications shape breast and ovarian tumor progression.

Breast and ovarian cancers are still one of the most prevalent causes of cancer death among the women in all parts of the world, mostly occurring at a...

Profiling extracellular matrix-driven heterogeneity of single cell migration and morphology.

Cell migration and morphology are fundamental to various biological processes, including tissue development, wound healing, and cancer progression. In...

Tumor burden in metastatic colorectal cancer quantified using deep learning models: Prognostic value and maintenance treatment benefit in the CAIRO3 trial.

INTRODUCTION: Total tumor number (TTN) and total tumor volume (TTV) reflect tumor burden and have been linked to outcomes in metastatic colorectal can...

Increased Prevalence of Extrathymic Neoplasms in Myasthenia Gravis Patients-A Population-Based, Matched Case-Control Study.

INTRODUCTION/AIMS: Myasthenia gravis (MG) is associated with thymic neoplasms. However, an increased prevalence of extrathymic neoplasms has also been...

Deep Learning Reconstruction on Quantitative Analysis in Brain Tumors With Diffusion-Weighted Imaging and Dynamic Susceptibility Contrast Imaging.

BACKGROUND: Although deep learning reconstruction (DLR) has been shown to improve image quality in MRI, its impact on quantitative physiologic paramet...

Survival prediction for bladder cancer using multimodal data with quantum neural networks and transformer architectures.

To address the challenges of cross-modal information fusion in high-dimensional multimodal medical data for cancer prognosis, this study presents a hy...

A manta ray-bayesian optimization approach for hyperparameter-tuned convolutional neural networks in lung cancer classification.

Lung cancer remains a global health challenge that is unavoidable. Despite the advances in lung cancer classification using deep learning models, the ...

Adaptive diagnostic reasoning framework for pathology with multimodal large language models.

BACKGROUND: Artificial intelligence enhances pathology screening efficiency, yet clinical adoption remains limited because most systems operate as opa...

Integrating spatial lymph node patterns and multimodal clinicopathological features to predict post-neoadjuvant recurrence in gastric adenocarcinoma: a machine learning nomogram.

Multimodal Deep Learning-Based Screening of Degenerative Temporomandibular Joint Disease Using 2D Radiography: A Cost-Effective and Low-Radiation Approach.

OBJECTIVES: This study aimed to develop and preliminarily validate a multimodal deep learning model based on two-dimensional maxillofacial imaging for...

Combining AI to reveal CCDC3-mediated pathways of colorectal cancer liver metastasis.

In colorectal cancer liver metastases, chromosomal instability (CIN) serves as a critical hallmark linked to tumor aggressiveness and poor prognosis. ...

Role of the gut microbiome in shaping drug response in immunocompromised hosts.

BACKGROUND: There is an increasing amount of evidence on microbiome-drug interactions in several clinical settings, including in immunocompromised pat...

Integrating single-cell atlases and machine learning to construct 'in silico patients' for predicting individualized drug responses.

Intratumoral cellular heterogeneity is a core challenge that drives drug resistance and hinders the advancement of precision oncology. Single-cell RNA...

Pharmacokinetic modeling to optimize development and therapeutic potential of mAb-based therapies.

Monoclonal antibody (mAb)-based therapies have revolutionized modern medicine, offering highly specific and effective treatments for a wide range of d...