Other Cancers

Classification of spinal tuberculous infection, pyogenic infection and spinal metastasis from magnetic resonance imaging using machine learning.

BACKGROUND: Differentiating between spinal tuberculosis, pyogenic (bacterial) spondylitis and spinal metastasis remains a major diagnostic challenge because their radiological features often overlap. Delayed or incorrect diagnosis may lead to inappropriate treatment, permanent disability or death. OBJECTIVE: To develop and evaluate deep learning models for automated classification of spinal tuberc...

CAD-SEL: A dual-modal colonoscopy dataset of subepithelial lesion.

Subepithelial lesions (SELs) of the gastrointestinal tract encompass a heterogeneous spectrum of histology, ranging from benign to malignant. Their detection during colonoscopy is critical yet challenging, as endoscopic interpretation is highly operator-dependent with variable diagnostic performance. Artificial intelligence (AI) offers great promise for assisting endoscopists in SEL detection and ...

No modality left behind: Adapting to missing modalities via knowledge distillation for brain tumor segmentation.

Accurate brain tumor segmentation is essential for preoperative evaluation and personalized treatment. Multi-modal MRI is widely used due to its abili...

Engineered artificial antigen-presenting cells for tumor immunotherapy: Design strategies, clinical applications, and translational challenges.

Immunotherapy has long played a pivotal role in cancer treatment, and antigen-presenting cell (APC)-based immunotherapy represents a promising strateg...

From Genomic Instability to Epigenetic Signatures: The Evolving Landscape of Circulating Cell-free DNA in Metabolic Dysfunction-Associated Steatotic Liver Disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a leading global health burden, yet its diagnosis and staging rely heavily...

Fusion attention-based nasopharyngeal carcinoma segmentation model in predicting the clinical outcome of cervical lymph node residue after IMRT.

BACKGROUND: Deep learning methods have made great progress in the automatic segmentation of nasopharyngeal carcinoma, but challenges remain. PURPOSE: ...

Synthetic inducible bacteria normalize the microenvironment of refractory tumors, potentiating checkpoint inhibitor responses.

Immunotherapy has transformed cancer treatment but remains ineffective in many solid tumors, largely due to the immunosuppressive tumor microenvironme...

Overexpression of NMNAT3 suppresses melanoma progression by reprogramming NAD⁺ metabolism.

BACKGROUND: Melanoma represents a highly aggressive and metastatic form of malignant skin cancer. that remains challenging to treat clinically. Tumor ...

Imaging of Small Cell Lung Cancer: An Updated Overview of Current and Emerging Applications.

Small cell lung cancer (SCLC) is an aggressive pulmonary neuroendocrine carcinoma characterized by rapid progression and early metastasis. Despite rec...

Feasibility of No-Code Deep Learning for Diagnosing Bone Metastasis in Bone Scans: A Comparative Study of Teachable Machine and ResNet.

This study explored the feasibility of developing a model that can diagnose positive and negative bone metastasis from bone scan images using Teachabl...

Two-staged CT-based radiomics model in characterising early-stage ovarian carcinoma and benign ovarian masses.

BACKGROUND: Characterisation of CT detected ovarian masses is challenging with overlapping imaging features, unreliable biomarker or clinical presenta...

Deciphering the predictors of endometrial nonbenign lesions in asymptomatic postmenopausal women via explainable machine learning.

OBJECTIVES: Timely identification of endometrial nonbenign lesions led to improved outcomes, but there was a lack of effective predictive models for a...

Interactive active learning for literature screening: finetuning GPT with DeepSeek reasoning for cross-domain generalization.

OBJECTIVE: Automated literature screening in biomedical research is often hindered by domain shifts and scarcity of labeled data, which limit model ac...

Multiparametric MRI-based Deep Learning and Radiomics for Evaluating Lymph Node Metastasis in Early-Stage Cervical Cancer.

Purpose To develop a multiparametric MRI-based radiomics model and deep learning-radiomics (DLR) fusion model for preoperative prediction of lymph nod...

Investigation of the Accuracy and Contributing Factors of AI-Based Diagnosis of Urothelial Carcinoma in Canine Abdominal Radiography.

Urothelial carcinoma (UC) is a highly malignant urinary cancer of the transitional epithelium in dogs. Recent advances in artificial intelligence (AI)...

The Prognostic and Biological Value of PGF-Based H&E Pathomics in Hepatocellular Carcinoma.

PURPOSE: Placental growth factor (PGF) is associated with the progression of hepatocellular carcinoma (HCC), but current research on this relationship...

A transformer-based patient-agnostic tumor motion tracking model (TransTracking): A feasibility study in radiotherapy for lung cancer.

BACKGROUND: Accurate and real-time localization of thoracic tumor targets is essential for effective radiation therapy. Recently, Transformer architec...

Deep Learning Model Based on Tumor and Visceral Adipose Tissue CT Features for Predicting Peritoneal Metastasis Risk after Radical Gastrectomy in Serosa-Invasive Gastric Cancer.

Purpose To develop and validate a deep learning model integrating tumor and visceral adipose tissue (VAT) CT scan features with clinical indicators to...

An XGBoost-Based Multicenter Model for Predicting HBV-Related Hepatocellular Carcinoma: Development and Validation.

BACKGROUND: The 5-year survival rate for hepatocellular carcinoma (HCC) is stage-dependent, yet existing models lack accuracy in predicting hepatitis ...

[Staging of middle ear cholesteatoma surgery and its value for clinical decision-making].

Middle ear cholesteatoma is characterized by squamous epithelial accumulation within the middle ear cavity, which can lead to severe complications suc...