Oncology/Hematology

Frequency-Domain Object Detection Network for Leukemia Diagnosis in Bone Marrow Microscopy.

Leukemia remains a prevalent hematologic malignancy, and its morphological heterogeneity presents challenges for reliable identification under optical microscopy. To address this, we propose a frequency-domain guided object detection framework to assist leukemia diagnosis using high-resolution bone marrow microscopic images. Specifically, we leverage frequency-based image enhancement and refined f...

Tumoral Skin Invasion Is an Independent Predictor of Rapid Recurrence in Head and Neck Cancer.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is an aggressive malignancy, with 50% of patients recurring. A subset of patients experience rapid recurrence (RR) postoperatively but prior to adjuvant therapy. This study identifies factors associated with RR and additional recurrence intervals: short-interval recurrence (SIR) and standard recurrence (SR). METHODS: Retrospective 10-year r...

Innovative Approaches to Head and Neck Cancer Diagnosis.

Diagnosis, positioned between disease prevention and treatment, is essential for head and neck cancer management. Delays in diagnosis contribute to di...

Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography.

PURPOSE: Small cell lung cancer (SCLC) is an aggressive disease with diverse phenotypes that reflect the heterogeneous expression of tumor-related gen...

Detection of Lymph Node Metastasis in Thyroid Cancer Using Deep Learning and Second Harmonic Generation Imaging.

Papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, with a significant proportion of patients being susceptible to lymph n...

Development and Validation of a Novel Plasma Metabolomic Signature for the Detection of Renal Cell Carcinoma.

BACKGROUND AND OBJECTIVE: Early diagnosis is critical for improving survival in renal cell carcinoma (RCC); yet, effective laboratory tests remain lac...

Extraction, preparation, pharmacological activities, and potential applications of ginsenosides Rk1 and Rg5.

ETHNOPHARMACOLOGICAL RELEVANCE: Panax species (Panax ginseng, Panax quinquefolius L., and Panax notoginseng) are globally recognized as valuable medic...

Radiation Risk in 2D Mammography Screening: A Scoping Review of Modelling Strategies and Emerging AI Applications.

Breast cancer is the most commonly diagnosed cancer among women worldwide, and concerns regarding radiation exposure from mammography screening remain...

Public Preferences for Emerging Cancer Screening Modalities: A Systematic Review of Discrete Choice Experiments.

BACKGROUND: Early detection of cancer reduces mortality and morbidity, but conventional screening methods often face challenges such as invasiveness, ...

Dual-feature cross-fusion network for precise brain tumor classification: a neurocomputational approach.

Brain tumors represent a significant neurological challenge, affecting individuals across all age groups. Accurate and timely diagnosis of tumor types...

Validation of novel low-dose CT methods for quantifying bone marrow in the appendicular skeleton of patients with multiple myeloma: initial results from the [18F]FDG PET/CT sub-study of the Phase 3 GMMG-HD7 Trial.

PURPOSE: The clinical significance of medullary abnormalities in the appendicular skeleton detected by computed tomography (CT) in patients with multi...

Investigation of cervical cell image segmentation technology based on deep learning and non-coding RNAs.

BACKGROUND: Cervical cancer remains a significant health concern worldwide, necessitating effective diagnostic methods such as cervical cell image seg...

Microbiome-transcriptome-histology triad enhances survival risk stratification in multiple cancers.

Accurate prognostic stratification is essential for optimizing postoperative therapeutic strategies in oncology. While deep learning approaches have s...

Evaluation of Context-Aware Prompting Techniques for Classification of Tumor Response Categories in Radiology Reports Using Large Language Model.

Radiology reports are essential for medical decision-making, providing crucial data for diagnosing diseases, devising treatment plans, and monitoring ...

Development of a High-Performance Ultrasound Prediction Model for the Diagnosis of Endometrial Cancer: An Interpretable XGBoost Algorithm Utilizing SHAP Analysis.

OBJECTIVES: To develop and validate an ultrasonography-based machine learning (ML) model for predicting malignant endometrial and cavitary lesions. ME...

A systematic review on deep learning based brain tumor segmentation and detection using MRI: Past insights, present techniques and future trends.

The abnormal growth of cells leads to brain malignancy in humans, which is among the most prevalent causes of fatalities in adults worldwide. Patients...

Role of NNMT and NM23A for diagnosis of RCC: Renal Cell Carcinoma.

OBJECTIVE: This study investigates the diagnostic potential of nicotinamide N-methyltransferase (NNMT) and NM23A as biomarkers for renal cell carcinom...

A deep learning method for diagnosis of oral potentially malignant disorders.

OBJECTIVES: This study aimed to develop and validate a two-stage deep learning method for diagnosing oral potentially malignant disorders (OPMDs). We ...

Liquid biopsy based on multi-targeted capture of urinary tumor DNA combined with machine learning to detect urothelial carcinoma: a multicenter prospective study.

PURPOSE: Diagnostics for urothelial carcinoma have low sensitivity, thereby negatively impacting diagnostic outcomes. Herein, we present BiovueUro, a ...

Preferences for Adopting Artificial Intelligence in Radiation Therapy Treatment: A Discrete Choice Experiment.

OBJECTIVES: The integration of artificial intelligence (AI) in radiation therapy offers significant potential to enhance cancer care by improving diag...