Oncology/Hematology

Breast cancer early detection and molecular subtype prediction by combination of Raman spectroscopy with deep learning.

Breast cancer is one of the most common tumors in women, and early screening can significantly reduce mortality rates. Meanwhile, accurately identifying HER2-positive and HER2-negative subtypes of breast cancer is critical for helping doctors determine treatment options and prognosis strategies for patients. The goal of this study was to develop a computationally efficient, end-to-end model capabl...

NABP-LSTM-Att: Nanobody-Antigen binding prediction using bidirectional LSTM and soft attention mechanism.

In vertebrates, antibody-mediated immunity is a vital component of the immune system, and antibodies have become a rapidly expanding class of therapeutic agents. Nanobodies, a distinct type of antibody, have recently emerged as a stable and cost-effective alternative to traditional antibodies. Their small size, high target specificity, notable solubility, and stability make nanobodies promising ca...

Machine learning and explainable artificial intelligence reveals the MicroRNAs associated with survival of head and neck squamous cell carcinoma patients.

Dysregulated microRNAs (miRNAs) play a significant role in cancer development and metastasis. In literature, miRNAs have been used for the survival pr...

Crosstalk of glutamine metabolism between cancer-associated fibroblasts and cancer cells.

Glutamine (Gln), a critical metabolic substrate, fuels the uncontrolled proliferation of cancer cells. Cancer-associated fibroblasts (CAFs), essential...

Improved two-view interactional fuzzy learning based on mutual-rectification and knowledge-mergence.

Nasopharyngeal carcinoma (NPC) is a malignant tumor that originates from the back of the nasal canal from above the soft palate to the upper larynx. B...

Machine learning driven prediction of drug efficacy in lung cancer: based on protein biomarkers and clinical features.

Currently, chemotherapy drugs are the first-line treatment for lung cancer patients, and evaluating their efficacy is of utmost significance. However,...

The emerging role of artificial intelligence applied to exosome analysis: from cancer biology to other biomedical fields.

In recent years, exosomes versatility has prompted their study in the biomedical field for diagnostic, prognostic, and therapeutic applications. Exoso...

Deep learning predicts the effect of neoadjuvant chemotherapy for patients with triple negative breast cancer.

BACKGROUND: Triple negative breast cancer (TNBC) is an aggressive subcategory of breast cancer with poor prognosis and high risk of recurrence after t...

Evaluating the added value of salivary hormones in the context of menstrual cycle staging: A machine learning approach and app-implementation.

OBJECTIVE: Salivary hormone assessment is commonly used in menstrual cycle studies, but its validity for accurate menstrual cycle staging has been que...

PD-L1 expression assessment in Angiosarcoma improves with artificial intelligence support.

Tumoral PD-L1 expression is assessed to weigh immunotherapy options in the treatment of various types of cancer. To determine PD-L1 expression, each t...

Perception and sentiment analysis of palliative care in Chinese social media: Qualitative studies based on machine learning.

BACKGROUND: Traditional Chinese culture makes death a sensitive and taboo topic, leading patients and family members to refuse to choose palliative ca...

A systematic review of AI as a digital twin for prostate cancer care.

Artificial Intelligence (AI) and Digital Twin (DT) technologies are rapidly transforming healthcare, offering the potential for personalized, accurate...

Application of Fourier transform infrared (FTIR) spectroscopy in liquid biopsy to predict the response to the first-line immunotherapy in non-small-cell lung cancer (NSCLC) patients.

The direction of anticancer therapies has changed in recent years, including the increasing use of immunotherapy. However, around 50 % of non-small-ce...

Exosomal Gene Biomarkers in Osteosarcoma: Mifepristone as a Targeted Therapeutic Revealed by Multi-Omics Analysis.

Osteosarcoma (OS) is an aggressive bone cancer that mainly occurs in children and adolescents. OS patients are mainly treated with neoadjuvant chemoth...

Metabolic pathway alterations in cerebrospinal fluid as diagnostic biomarkers for primary central nervous system lymphoma.

Primary Central Nervous System Lymphoma (PCNSL) is a rare and aggressive type of hematological malignancy that can pose diagnostic challenges. Early d...

Development and validation of a machine learning model based on complete blood counts to predict clinical outcomes in urothelial carcinoma patients.

Urothelial carcinoma (UC) is a highly malignant disease with significant public health implications. Despite advancements in oncology, early diagnosis...

Prediction of remission of pharmacologically treated psychotic depression: A machine learning approach.

BACKGROUND: The combination of antidepressant and antipsychotic medication is an effective treatment for major depressive disorder with psychotic feat...

Real-Time Dose-Guided Radiation Therapy.

Dramatic strides have been made in real-time adaptive radiation therapy, where treating single tumors as dynamic but rigid bodies has demonstrated a h...

Photoacoustic-Integrated Multimodal Approach for Colorectal Cancer Diagnosis.

Colorectal cancer remains a major global health challenge, emphasizing the need for advanced diagnostic tools that enable early and accurate detection...

A Multi-Modal Fusion Framework for Brain Tumor Segmentation Based on 3D Spatial-Language-Vision Integration and Bidirectional Interactive Attention Mechanism

This study aims to develop a novel multi-modal fusion framework for brain tumor segmentation that integrates spatial-language-vision information thr...