Oncology/Hematology

A cell-based kinetic framework enables TCR specificity prediction.

The ability to predict T-cell receptor (TCR) specificity from sequences could transform immunotherapy, vaccine development, and our understanding of immune recognition. However, progress has been shaped by "edge cases", in which specificity appears to be captured by simplified descriptors, such as sequence motifs or correlations between binding affinity and functional activation. Although informat...

MoCaPS: a machine learning model for stratification of cancer-associated cachexia based on blood biomarkers.

Identification of minimally invasive biomarkers of cancer-associated cachexia may help to recognize high risk patients for progression to more severe cachectic stages. We developed a machine learning-based Model for Cachectic Patients Stratification (MoCaPS) to determine the sets of blood biomarkers that differentiate between noncachectic (NCa), precachectic (PCa), or cachectic (Ca) patients. The ...

BASG-Net: a background-aware spatial gating network for cervical cell segmentation in pap smear images.

The precise segmentation of cervical cell images is regarded as a fundamental prerequisite for the implementation of computer-aided cervical cancer sc...

Five-miRNA signature for breast cancer detection in a northern Mexico border cohort.

Breast cancer remains the leading malignancy diagnosed in women worldwide and is of particular concern in low- and middle-income countries, such as Me...

RCAF for patient-level thyroid ultrasound malignancy prediction under leakage-free evaluation and calibration.

Accurate differentiation between benign and malignant thyroid nodules on ultrasound remains clinically important, yet interpretation is operator-depen...

Prognostic survival models for diffuse large B-cell lymphoma using statistical and machine learning approaches.

Although diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, existing prognostic tools, including the revised International Prognostic I...

Reversing cancer cell behavior using AI-guided CRISPR and quantum nanobiology: a systems-based approach to epigenetic reprogramming.

Treatment effectiveness is hindered by the phenotypic plasticity of cancer and the genetic complexity of tumors. However, CRISPR-Cas-based medicines f...

Retraction: Automated brain tumor diagnostics: Empowering neuro-oncology with deep learning-based MRI image analysis.

Deep Learning Predicts Imminent Tumor Progression in Advanced Pancreatic Adenocarcinoma Using Serial CT Scans During Chemotherapy.

Advanced pancreatic ductal adenocarcinoma (PDAC) often progresses rapidly during chemotherapy despite initial assessments of stable disease or partial...

Multistage microrobots with pH-responsive release of platelet membrane-coated nanoparticles.

Targeted drug delivery in the gastrointestinal tract remains challenging because therapeutics must overcome multiple hierarchical barriers before reac...

Integrated multi-omics and machine learning identify OGDHL as a key regulator of glioblastoma progression and antitumor immunity via glutamine metabolism and histone lactylation.

BACKGROUND: Glioblastoma (GBM) has an extremely poor prognosis, and its malignant progression is closely associated with glutamine metabolic reprogram...

Smartphone Applications in Dermatology With Special Focus on AI: A European-Continent-Based Review.

Smartphone applications for dermatology are widely available across Europe, yet evidence on their characteristics, validation, and regulatory complian...

MR habitat radiomics for differentiating indeterminate (Grade 3) nasopharyngeal lesions: lymphoid hyperplasia versus early-stage nasopharyngeal carcinoma.

BACKGROUND: To investigate the feasibility of MRI-based habitat radiomics to differentiate indeterminate Grade 3 nasopharyngeal lesions into nasophary...

AI-driven multi-omics deciphering of extrachromosomal circular DNA rescues hidden targets in colorectal cancer.

BACKGROUND: Extrachromosomal circular DNA (eccDNA) is an emerging tumor biomarker notable for its tumor-specific amplification and contribution to gen...

Predicting preoperative lymph node metastasis of hilar cholangiocarcinoma based on deep learning radiomics.

BACKGROUND: The objective of this study was to evaluate whether deep learning radiomic (DLR) models utilizing B-mode ultrasound (BUS) and contrast-enh...

A Tutorial on Dimensionality Reduction and Clustering for Molecular Dynamics Trajectories: From Linear Algorithms to Deep Learning.

This Tutorial aims to help users analyze molecular dynamics (MD) simulation trajectories while requiring only minimal prior knowledge, identify key me...

Identifying Potential Exosome-Derived mRNA Biomarkers for Diagnosis and Prediction of Breast Cancer Using Machine-Learning Approaches.

INTRODUCTION: Breast cancer remains a major global health burden, underscoring the urgent need for reliable early detection strategies. Exosomes, as m...

A prospective development and evaluation of a 2D convolutional neural network-based auto-segmentation model for cervical cancer radiotherapy.

BACKGROUND: Accurate delineation of target volumes and organs at risk (OAR) is essential in radiotherapy planning for cervical cancer. Deep learning (...

Accelerating prostate MRI: a practical review of modern acquisition strategies.

Prostate MRI has become a key tool in the detection, localization, and staging of prostate cancer, as well as in active surveillance. Despite its diag...

Image optimization for low-dose 18F-FDG breast PET/MRI using deep learning: a pilot study.

BACKGROUND: Positron emission tomography with magnetic resonance imaging (PET/MRI) provides noninvasive molecular characterization of breast cancer an...