Oncology/Hematology

Multiparametric MRI-derived Interpretable Habitat Radiomics for Preoperative Assessment of Tumor Budding Status in Breast Cancer.

RATIONALE AND OBJECTIVES: Breast cancer exhibits high biological heterogeneity and variable invasion patterns. Tumor budding (TB) is a key histopathological marker of aggressive behavior and poor prognosis. However, preoperative TB assessment is limited by biopsy sampling issues. This study aims to develop a multiparametric magnetic Resonance Imaging (MRI)-based habitat radiomics model for noninva...

W-AGRU-Net: a dual-stream attention framework for robust brain tumor segmentation in clinical MRI imaging.

Segmenting brain tumors from MRI scans is a challenging aspect of medical image analysis because of anatomical complexity, ambiguous tumor boundaries, and variability of shape. In this paper, we propose W-AGRU-Net, a new dual-stream U-Net framework that combines W-Attention mechanisms with residual connections for strong and accurate glioma segmentation. Our framework uses two asymmetric streams t...

Comment on "Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes".

Xue and Chen et al.'s review connects AI-derived CT features of lung ground-glass nodule adenocarcinoma to driver gene status. Three aspects are missi...

Comparable Predictive Performance of Non-VCTE-Based Machine Learning Model for HBV-Infected Hepatocellular Carcinoma Risk.

BACKGROUND: Accurate risk stratification for hepatocellular carcinoma (HCC) among chronic hepatitis B (CHB) patients remains challenging. Vibration-co...

A Validated, explainable machine learning-based preoperative risk model for microvascular invasion in hepatocellular carcinoma.

PURPOSE: This investigation aimed to develop and validate a diagnostic algorithm for preoperatively assessing the likelihood of microvascular invasion...

Ovarian borderline tumors and minimally invasive surgery: a narrative review.

Borderline ovarian tumors (BOTs) are a distinct subgroup of epithelial ovarian neoplasms that commonly affect women of reproductive age and are associ...

Cutaneous lymphomas - an update.

Primary cutaneous lymphomas (CL) and lymphoproliferative disorders (LPD) are heterogeneous T- and B-cell neoplasms defined by integrated clinical, his...

Machine Learning Prediction of Local Failure in GI Brain Metastases on the Day of Gamma Knife Radiosurgery.

BACKGROUND: Time to local failure after stereotactic radiosurgery (SRS), including Gamma Knife radiosurgery (GKRS), for gastrointestinal (GI) brain me...

LD-CNN19: A Deep Learning Model Integrating Logical Reasoning and Knowledge Expansion for Small-Sample Sleep Staging Tasks.

To address opaque decision-making and performance bottlenecks caused by limited samples and physiological heterogeneity in deep learning-based sleep s...

Semi-Supervised Domain Adaptation with Latent Diffusion for Pathology Image Classification.

Deep learning models in computational pathology often fail to generalize across cohorts and institutions due to domain shift. Existing approaches eith...

Label-free ovarian cancer histopathological diagnosis using two-photon autofluorescence microscopy with a joint denoising and segmentation framework.

Two-photon autofluorescence (TPAF) microscopy is a promising modality for rapid, label-free assessment of unstained tissue, but is fundamentally limit...

Deep molecular profiling of lung neuroendocrine tumours and supra-carcinoids.

BACKGROUND: Lung neuroendocrine tumours (NETs, also known as carcinoids) are rapidly rising in incidence worldwide but have unknown aetiology and limi...

AI-based segmentation of occult cervical lymph node metastases: potential reduction of cytokeratin staining.

BACKGROUND: Detection of occult cervical lymph node metastases is critical for accurate staging and treatment planning in oral cavity squamous cell ca...

Detection of Pancreatic Cancer via Specific Metabolite Markers: A Metabolomics Approach.

BACKGROUND AND AIMS: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies worldwide because most patients are diagnosed...

RIBOTAC-mediated degradation of hsa-microRNA-301a-3p suppresses TNBC bone metastasis.

Triple-negative breast cancer (TNBC) is an aggressive subtype with a high propensity for bone metastasis and limited treatment options. Although many ...

Accurate differentiation of metastatic and ultrasound-atypical reactive hyperplastic lymph nodes using a fusion model integrating ultrasound radiomics and habitatomics: A multicenter study.

BACKGROUND: The significant sonographic overlap between metastatic and ultrasound-atypical reactive hyperplastic lymph nodes remains a challenge in su...

Deep Learning Versus LASSO-Based Machine Learning for 1-Year Survival After Surgery for Spinal Metastases: A JASA Multicenter Prospective Cohort Analysis.

STUDY DESIGN: Multicenter prospective cohort study; secondary analysis. OBJECTIVE: To evaluate predictors associated with 1-year survival after surger...

Performance of Large Language Models in Oral Cancer Patient Education: An Evaluation of Reliability, Readability, and Patient Communication Quality.

BACKGROUND: As society is increasingly depending on large language models (LLMs) for health-related questions, it is essential to objectively evaluate...

Deciphering Post-surgery Gut Microbial Dynamics in Colorectal Cancer through Multi-cohort Machine Learning.

Surgical resection, and its associated bowel preparation, remain the primary treatment for colorectal cancer (CRC), yet the associated effects on post...

Detection of oral potentially malignant disorders and oral cancer with white light through deep learning: a systematic review and meta-analysis.

OBJECTIVES: The research question was: How accurate is artificial intelligence (AI) in diagnosing Oral Potentially Malignant Disorders (OPMD)/oral can...