Oncology/Hematology

Real-time identification of malignant breast tissue during electrosurgical resection.

Achieving complete resection of breast cancer with clear margins remains a significant surgical challenge, particularly for infiltrating subtypes, where re-resection rates of up to 45% have been reported. Consequently, a device capable of providing real-time feedback to surgeons regarding the resected breast tissue holds the potential to significantly improve R0 resection rates. This study represe...

M²B-Net: a lightweight multi-scale multi-attention boundary-aware network for liver tumor segmentation from CT images.

Liver tumor segmentation from CT images remains challenging due to large variations in lesion scale, blurred boundaries, low tissue contrast, and the high computational cost of existing deep learning models. This study aims to develop a lightweight yet accurate segmentation network suitable for clinical deployment. We propose a Multi-scale Multi-attention Boundary-aware Network (M²B-Net) based on ...

Transcriptomic profiling identifies immunotherapy-responsive phenotypes in microsatellite-stable metastatic colorectal cancer.

Conventional immune checkpoint inhibitors (ICIs) remain largely ineffective in microsatellite-stable metastatic colorectal cancer (MSS mCRC), where lo...

Non-invasive detection of Oncostatin M and TNF-α in a microphysiological chip with embedded pH tuning capabilities.

The detection of disease biomarkers is crucial in biomedical research, diagnostics and personalized medicine. However, challenges still exist for the ...

Integrating machine learning and retrospective cohort to explore the effects of AEDs on the prognosis of LGG and identify related molecular targets.

Antiepileptic drugs (AEDs) were frequently employed in glioma patients, especially those with low-grade glioma (LGG), in which epilepsy manifested in ...

Gold-Functionalized Multilayer Heterojunction Microarchitectures Enable High-Fidelity Serum Metabolite Profiling for Skin Cancer Subtype Classification.

Skin cancer is among the most prevalent malignancies worldwide, with non-melanoma types ranking among the top five and melanoma characterized by high ...

Metabolic SERS Profiling under Triple-Coupled Hotspots Aided by Machine Learning for Precise Identification of Gastrointestinal Malignancies.

Serum metabolic analysis based on label-free SERS fingerprints represents a highly promising technique for the detection of tumors. However, the insuf...

Dissecting PCD-driven molecular landscapes in AML: a multi-omic framework for prognostication and therapeutic targeting.

Acute myeloid leukemia (AML) remains a molecularly heterogeneous malignancy with poor prognosis, necessitating robust biomarkers for risk stratificati...

Multi-omics analysis identifies GINS1 as a prognostic biomarker in lung adenocarcinoma, linked to macrophage polarization and tumor cell survival.

This study aimed to identify essential genes driving lung adenocarcinoma (LUAD) progression by integrating CRISPR-Cas9 dependency data from the Cancer...

A novel computational framework for tumor-specific T cell antigen identification using a deep neural network.

Identifying tumor-specific T-cell antigens is essential for advancing cancer immunotherapy and enabling precision-driven, AI-assisted discovery. While...

Deep learning-based automated detection of oral squamous cell carcinoma in histopathological images: a comparative study of five CNN architectures.

Oral squamous cell carcinoma (OSCC) is the most common malignancy of the oral cavity, and early diagnosis plays a crucial role in improving patient pr...

Machine learning-based identification of basement membrane-related signature to predict recurrence and immunotherapy benefit in bladder cancer.

The basement membrane (BM) plays a critical role in regulating bladder cancer (BC) progression. However, a BM-related signature for predicting BC recu...

A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.

OBJECTIVE: This study aims to construct a multimodal fusion model (FM) based on CT and hematoxylin and eosin (H&E) stained slices to predict the PD-L1...

A deep learning framework for lesion-level treatment response prediction in hodgkin lymphoma using PET/CT tensor radiomics.

BACKGROUND: Accurate prediction of treatment response in Hodgkin lymphoma (HL) is crucial for personalized therapy. The Tensor Radiomics (TR) paradigm...

Spectral CT-based intratumoral and peritumoral radiomics for predicting invasiveness of ground-glass nodules in lung adenocarcinoma.

OBJECTIVES: To evaluate the potential of spectral detector computed tomography (SDCT) combined with intratumoral and peritumoral radiomics for noninva...

Integrating machine learning-based molecular design with experimental validation for the discovery of EGFR inhibitors in lung cancer.

The emergence of drug resistance and off-target toxicities in epidermal growth factor receptor (EGFR) targeted therapies underscores the urgent need f...

E2E-OCT: end-to-end joint learning model using optical coherence tomography images for vocal cord leukoplakia diagnosis.

In our previous study, a home-built handheld OCT system was used to collect OCT images in vocal cord leukoplakia. First, 383 valid OCT images were col...

DrugDL: Dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery.

MOTIVATION: The accurate and robust representation of drug molecule features, the prediction of drug-target biomacromolecule interactions, and the det...

Navigating the metabolic crossroads: A bibliometric analysis of the SLC7A11/xCT field from oxidative stress to ferroptosis and disulfidptosis.

The cystine/glutamate transporter SLC7A11 is a central node in regulated cell death and tumor metabolism. Here, we performed a bibliometric analysis o...

Heterogeneous developmental trajectories and predictors of self-harm in early adolescence: A machine learning study.

BACKGROUND: Although prior studies have examined developmental trajectories of adolescent self-harm in terms of frequency and severity, a fundamental ...