Oncology/Hematology

SleepGPT: A Sleep Stage Language Model for Efficient Sleep Assessment

Accurate and scalable sleep assessment is crucial for diagnosing sleep disorders and advancing personalized medicine. However, current approaches heavily rely on manual scoring of polysomnography (PSG) recordings, which is labor-intensive, costly, and difficult to scale. Here, we introduce SleepGPT, the first sleep-specific language model trained on over 5.8 million sleep stage annotations from 5,...

Interventionally-guided representation learning for robust and interpretable AI models in cancer medicine

Machine learning models hold promise in cancer medicine but often lack robustness and interpretability. We introduce a new class of model for high-dimensional molecular data that incorporate interventional auxiliary information to learn latent representations that are informative and interpretable by design. By using causal signals from genetic loss-of-function screens, our approach generates repr...

A Novel Approach using CapsNet and Deep Belief Network for Detection and Identification of Oral Leukopenia

Oral cancer constitutes a significant global health concern, resulting in 277,484 fatalities in 2023, with the highest prevalence observed in low- a...

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation

Breast cancer lesion segmentation in DCE-MRI remains challenging due to heterogeneous tumor morphology and indistinct boundaries. To address these c...

Integrated single-cell and transcriptome sequencing data reveal the value of IL1RAP in gastric cancer microenvironment and prognosis.

BACKGROUND: Investigating the pivotal role of IL1RAP in the tumor microenvironment of gastric cancer.

Comprehensive analysis of mRNA and lncRNA expression for predicting lymph node metastasis in cervical cancer: a novel seven-gene signature approach.

OBJECTIVE: Lymph node metastasis (LNM) critically determines recurrence and survival in cervical cancer (CC), yet current imaging-based methods lack a...

Development and evaluation of a predictive model for postoperative recurrence and metastasis in breast cancer using an artificial intelligence ultrasound breast system.

OBJECTIVE: To assess the feasibility and efficacy of developing a predictive model for postoperative recurrence and metastasis in breast cancer using ...

Tumor-infiltrating immune cell signature score reveals prognostic biomarkers and therapeutic targets for colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is one of the leading contributors to cancer-related deaths worldwide, with more than 900,000 new diagnoses and re...

Comparative analysis of multi-zone peritumoral radiomics in breast cancer for predicting NAC response using ABVS-based deep learning models.

BACKGROUND: Peritumoral characteristics demonstrate significant predictive value for neoadjuvant chemotherapy (NAC) response in breast cancer (BC) thr...

Prediction of induction chemotherapy efficacy in patients with locally advanced nasopharyngeal carcinoma using habitat subregions derived from multi-modal MRI radiomics.

OBJECTIVE: This study aims to predict the early efficacy of induction chemotherapy (ICT) in patients with locally advanced nasopharyngeal carcinoma (L...

MRI-based 2.5D deep learning radiomics nomogram for the differentiation of benign versus malignant vertebral compression fractures.

OBJECTIVE: Vertebral compression fractures (VCFs) represent a prevalent clinical problem, yet distinguishing acute benign variants from malignant path...

Artificial intelligence based advancements in nanomedicine for brain disorder management: an updated narrative review.

Nanomedicines are nanoscale, biocompatible materials that offer promising alternatives to conventional treatment options for brain disorders. The rece...

Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice.

OBJECTIVE: In non-clinical safety evaluation of drugs, pathological result is one of the gold standards for determining toxic effects. However, pathol...

TMEM132A: a novel susceptibility gene for lung adenocarcinoma combined with venous thromboembolism identified through comprehensive bioinformatic analysis.

BACKGROUND: Mounting evidence indicates that lung adenocarcinoma (LUAD) patients are at elevated risk for venous thromboembolism (VTE), presenting a m...

Can Intra-Operative Ablation-Specific Features Based on Ultrasound Fusion Imaging be Used to Predict Early Recurrence of Hepatocellular Carcinoma After Microwave Ablation: A Proof-of-Concept Study.

PURPOSE: Intra-operative factors are crucial to early recurrence of hepatocellular carcinoma (HCC) after microwave ablation (MWA), but few models have...

AI in optimized cancer treatment: laying the groundwork for interdisciplinary progress.

The molecular complexity of cancer presents significant challenges to traditional therapeutic approaches, necessitating the development of innovative ...

Significance and mechanisms of perineural invasion in malignant tumors.

Cancer remains the second leading cause of death worldwide. Tumor invasion and metastasis pose significant challenges for clinical management. In addi...

Diagnostic accuracy of artificial intelligence based on imaging data for predicting distant metastasis of colorectal cancer: a systematic review and meta-analysis.

BACKGROUND: Colorectal cancer is the third most common malignant tumor with the third highest incidence rate. Distant metastasis is the main cause of ...

Single-cell transcriptomics and machine learning unveil ferroptosis features in tumor-associated macrophages: Prognostic model and therapeutic strategies for lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a major cause of cancer-related mortality worldwide. Tumor-associated macrophages (TAMs) play a crucial role...

A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.

BACKGROUND: Extremely aggressive prostate cancer, including subtypes like small cell carcinoma and neuroendocrine carcinoma, is associated with poor p...