Other Cancers

Minimally invasive circulating MicroRNA signatures for breast cancer detection and neoadjuvant therapy response prediction in a multicenter MENA cohort.

BACKGROUND: Early detection and treatment stratification of breast cancer remain major clinical challenges, particularly in the Middle East and North Africa (MENA) region, where population-specific circulating biomarkers are poorly defined. Circulating microRNAs (miRNAs) represent promising minimally invasive biomarkers; however, comprehensive multicenter studies in diverse MENA populations with l...

Large language models enable prognostic stratification of cancer patients using real-world clinical notes.

In medical documentation, vast amounts of unstructured text are generated that are still underutilized in current prognostic models. We investigate the potential of self-hosted large language models (LLM) to extract clinically meaningful, patient-specific information from routine clinical notes for personalized risk stratification in cancer care. We collected real-world medical notes from 2,708 no...

Identification of the oxidation stress-related gene signatures and functional verification of MINK1 in prostate cancer cells.

BACKGROUND: A major challenge facing prostate cancer (PCa) cells is oxidative stress, yet the precise role and underlying mechanisms remain inadequate...

Identification and verification of immunogenic cell death-related signatures derived from bone metastasis of prostate cancer based on multi-omics and machine learning algorithms.

BACKGROUND: Prostate cancer (PCa) is a prevalent urological malignancy in men, with bone metastasis occurring in the majority of patients, often leadi...

A Comparative Cross-sectional Study Assessing the Readability of ChatGPT and UpToDate Content on Multiple Endocrine Neoplasia Syndromes.

BACKGROUND: With growing reliance on digital health tools, the readability of online medical content is increasingly vital in patient-centered care. A...

Automatic Sleep Staging Using Cardiorespiratory Signals: A Systematic Review of Methodologies and Performance.

Cardiorespiratory-based methods offer promising alternatives to traditional PSG for longitudinal sleep monitoring, holding significant systemic medica...

Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications.

Endovascular thrombectomy (EVT) has transformed the treatment of acute ischemic stroke (AIS). However, a substantial proportion of AIS patients experi...

Spatial transcriptome and single-cell reveal the role of sorbitol metabolism in hepatocellular carcinoma progression and tumor microenvironment.

BACKGROUND: Metabolic reprogramming represents a hallmark feature of hepatocellular carcinoma (HCC). As a crucial branch of the polyol pathway, the bi...

A Predictive Nomogram Integrating AI-Assisted Morphological Feature Extraction with Clinical and Ultrasound Parameters for Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer.

OBJECTIVE: To develop and validate a nomogram integrating artificial intelligence (AI)-extracted ultrasound features with clinic pathologic data for n...

Development and validation of a machine learning model to preoperatively predict very early recurrence in patients with hepatitis B virus-related hepatocellular carcinoma.

Very early recurrence (within one year) after curative hepatectomy significantly impairs long-term survival in patients with hepatitis B virus (HBV)-r...

HyCANet: a hybrid convolutional-attention network with cross-attention fusion for lung cancer histopathological classification.

Histopathology interpretation of lung cancer involves careful examination of tissue morphology, especially differentiating benign lung tissue, adenoca...

An Interpretable AI System for Oral Leukoplakia Progression: From Early Screening to Lesion Delineation.

Oral potentially malignant disorders (OPMDs), particularly leukoplakia, represent critical precursors to oral cancer requiring systematic monitoring. ...

Comparative outcomes of proton and photon therapy in the management of malignant peripheral nerve sheath tumors.

BACKGROUND: Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are aggressive sarcomas often arising in the spine, characterized by high metastatic pot...

A predictive tool incorporating frailty scores for perioperative risk assessment in patients with spinal metastasis: a national retrospective cohort study.

BACKGROUND: Surgical treatment for spinal metastases is associated with high perioperative risk due to tumor burden, neurologic compromise, and limite...

A Systematic Review and Meta-analysis of Two-Dimensional Breast Ultrasound Radiomics: Implications for Lesion Characterization and Diagnostic Accuracy.

Radiomics applied to two-dimensional breast ultrasound has emerged as a potential noninvasive approach for differentiating benign from malignant breas...

Advanced Deep Learning Architectures in MRI-Based Brain Tumor Classification: A Systematic Review Focused on Meningiomas.

Deep learning (DL) is increasingly applied to automate brain tumor classification from magnetic resonance imaging (MRI), yet meaningful clinical deplo...

MRI Radiomics for Preoperative Microvascular Invasion Stratification in Hepatocellular Carcinoma: Comparative Analysis of Intratumoral, Peritumoral, and Combined Intratumoral-Peritumoral Approaches-A Systematic Review and Meta-analysis.

Microvascular invasion (MVI) represents a critical prognostic determinant in hepatocellular carcinoma (HCC), yet preoperative prediction remains chall...

Scleral image analysis via artificial intelligence to evaluate inflammatory status in inflammatory bowel disease.

PURPOSE: Among the extraintestinal ocular manifestations of patients with inflammatory bowel disease (IBD), episcleritis is associated with the inflam...

Pilot feasibility study of deep learning-based detection of malignant liver lesions in intraoperative ultrasound imaging.

BACKGROUND: Intraoperative ultrasound plays a central role in hepatobiliary surgery for the detection of liver lesions, but image interpretation is ch...

MRI radiomics combined with clinical factors to predict vessels encapsulating tumor clusters and early recurrence in hepatocellular carcinoma (≤ 3 cm).

OBJECTIVES: This study aims to develop a predictive model that combines MRI radiomics features with clinical factors to anticipate the preoperative pr...