Oncology/Hematology

Real-World Application of a Machine Learning-Based Early Recurrence Model for Guiding Adjuvant Chemotherapy Use in Patients with Gallbladder Cancer.

BACKGROUND/AIMS: Although recent randomized controlled trials have reported the efficacy of adjuvant chemotherapy for resected biliary tract cancer, discrepancies remain between recommendations and real-world practice. Therefore, we aimed to assess the efficacy of adjuvant chemotherapy in patients with gallbladder cancer and used machine learning-based risk stratification to predict recurrence to ...

Condition-Associated Pattern Extraction and Recovery From Multi-Condition Single-Cell RNA-seq Data With CAPER.

A central challenge in multi-condition single-cell RNA sequencing (scRNA-seq) data analysis is the disentanglement of true biological signals from unwanted variations in complex experimental designs. Current statistical and machine learning-based methods struggle with this task, often providing only visualizable embeddings, over-correcting and discarding biological signal, or failing to resolve ce...

A highly interpretable machine learning model for predicting lung cancer bone metastasis: uncovering the synergistic effect of routine biochemical markers.

BACKGROUND: Bone metastasis (BM) significantly impairs lung cancer prognosis and patient quality of life. Conventional imaging modalities often face l...

High-Throughput Olink Proteomics Elucidates the Immuno-Neurological Landscape of Gastric Cancer.

Gastric cancer (GC) ranks as the third leading cause of cancer-related mortality globally. A comprehensive and precise proteomic study of GC tissues c...

Boundary-Aware Spectral and Morphological Guidance Method for Feature-Driven Colorectal Cancer Segmentation.

Precise segmentation of medical images plays a crucial role in modern clinical practice, providing important foundations for the quantitative analysis...

Leveraging Large Language Models to Integrate Clinical Knowledge and Machine Learning Predictions for Lymph Node Metastasis Prediction: Development of a Knowledge-Augmented Framework.

BACKGROUND: Lymph node metastasis (LNM) is a critical clinical indicator for determining the initial treatment strategy for patients with lung cancer....

An innovative deep learning paradigm for automated detection and accurate classification of lung nodules in magnetic resonance imaging.

Lung cancer, the predominant kind of cancer, needs considerable care, since inadequate treatment may lead to fatal outcomes. The integration of comput...

Integrative single-cell and bulk transcriptomics reveal cuproptosis subtypes, prognostic models, and COL5A1 as a key gene in glioblastoma.

Glioblastoma (GBM) remains one of the most aggressive primary brain tumors with limited therapeutic options. Cuproptosis, a recently identified copper...

Multi-Omics Analyses Reveal BPA Accelerates Glioma Progression via Cell Cycle Dysregulation.

Bisphenol A (BPA), a pervasive environmental endocrine disruptor, its role in glioma progression is unclear. We sought to elucidate how BPA influences...

Predicting Renal Tumor Pathology from Intraoperative Gross Appearance: An AI-Based Pilot Study.

OBJECTIVE: To evaluate the feasibility of using convolutional neural networks (CNNs) and vision transformers (ViTs) to predict renal tumor pathology i...

Machine learning-assisted highly efficient thermal management in function-oriented thermochromic smart windows.

Windows contribute significantly to energy inefficiency in electric vehicles and buildings, and the development of smart windows with customized optic...

Effects of breast cancer risk factors on the tumor microenvironment: a morphological deep-learning analysis of three prospective cohorts.

Emerging evidence suggests a critical role of the tumor microenvironment (TME) in breast cancer (BC) development and outcomes, yet factors that modify...

An intelligent MRI-based all-in-one diagnostic strategy for axillary lymph node status in breast cancer.

An integrated diagnostic strategy of preoperative identification of sentinel lymph node (SLN) metastasis, SLN metastatic burden, and non-SLN (NSLN) me...

In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.

Computational medicine uses mathematical modelling, high-performance computing, and the availability of large-scale biomedical data to study multiscal...

Multimodal oral cancer detection with embedding-level oversampling of fused image and clinical data.

Oral cancer is a highly aggressive disease that is often detected too late, resulting in poor survival outcomes. Screening methods are limited by subj...

Radiation shielding and microstructural characteristics of nano-silica and nano-alumina modified cement composites.

The increasing use of nuclear technology in medicine, industry, and energy requires effective durable radiation shielding. This study aimed to develop...

PD-YOLO: a high-precision detection network for fine-grained lung cancer and multi-scale pulmonary nodules in CT images.

Timely and accurate Computed Tomography (CT) screening is crucial for the early clinical treatment of lung cancer and preventing the progression of ma...

Deep learning-enabled single-cell morpholomic atlas of nasal swabs distinguishes chronic inflammation from sinonasal malignancy.

Sinonasal malignancies frequently present with symptoms overlapping chronic inflammatory conditions, complicating early detection and delaying treatme...

Handling missing modalities in multimodal survival prediction for non-small cell lung cancer.

Accurate survival prediction in non-small cell lung cancer (NSCLC) requires integrating clinical, radiological, and histopathological data. Multimodal...

A Computationally Efficient and Improved Brain Tumor Recognition System by MRI-Segmentation Integrated Classification Network.

Brain tumors present a major global health concern, and a precise diagnosis is essential for proper treatment. Many existing MRI-based machine learnin...