Other Cancers

GICAF-Net: A cross-attentional graph-image fusion network for hyperspectral pathological diagnosis of FNH and HCC.

BACKGROUND AND OBJECTIVE: Accurate intraoperative differentiation between focal nodular hyperplasia (FNH) and hepatocellular carcinoma (HCC) remains a major clinical challenge, especially in atypical cases where conventional imaging and histopathology are constrained by turnaround time, cost, or spectral resolution. This study aims to develop a novel deep learning framework to improve the precisio...

Combined dual-phase CT radiomics and deep transfer learning model for oxaliplatin response in unresectable colorectal liver metastases.

OBJECTIVES: Accurate prediction of response to first-line oxaliplatin-based chemotherapy in unresectable colorectal liver metastases (CRLM) is critical for optimizing treatment strategies. This study aimed to develop and validate a dual-phase CT-based Combined model integrating radiomics, deep transfer learning (DTL), and clinical variables to enable individualized response prediction. METHODS: In...

Transformer-based ultrasound radiomics for novel multi-task automated segmentation and classification of ovarian tumors.

OBJECTIVE: To develop and evaluate a comprehensive AI-driven pipeline for automated segmentation and multi-class classification of ovarian tumors in u...

Redefining oropharyngeal cancer in the HPV era: integrating precision medicine and immunotherapeutic frontiers.

Oropharyngeal cancer (OPC) is increasingly driven by human papillomavirus (HPV), particularly HPV16, marking a shift in its epidemiology, prognosis, a...

Interpretable Machine Learning Model for Survival Prediction in Pediatric Adrenocortical Tumors.

PURPOSE: Pediatric adrenocortical tumors (pACTs) are rare and clinically heterogeneous. Existing risk stratification systems rely on fixed thresholds ...

Emerging options and future strategies in immunotherapy for advanced lung squamous-cell carcinoma.

Advanced-stage lung squamous-cell carcinoma (LUSC) remains a therapeutic challenge. Although immune checkpoint inhibitors (ICIs) have revolutionized L...

MiniGPT-Pancreas: Multimodal Large Language Model for Pancreas Cancer Observation and Localization in CT Images.

Pancreatic cancer remains one of the deadliest malignancies, primarily because of its subtle CT appearance and frequent late-stage diagnosis. We intro...

Feasibility of deep learning-based cancer detection in ultrasound microvascular images.

Acoustic angiography is a superharmonic contrast-enhanced ultrasound modality that maps 3-D microvasculature with fine spatial resolutions and has dem...

Artificial intelligence-driven CT radiomics model predicts prognosis in TACE-refractory hepatocellular carcinoma.

PURPOSE: To develop an integrated predictive model combining radiomics, clinical risk factors, and machine learning for prognostic assessment in hepat...

A CT-based 2.5D deep learning model for preoperative T-staging in gastric cancer: a retrospective multicenter study.

PURPOSE: To develop and validate a 2.5D multi-angle deep learning (MADL) model for preoperative T-staging in patients with gastric cancer (GC) and to ...

Combining MEA-Net and LAP-Net for Pneumoconiosis Staging Framework.

OBJECTIVE: Pneumoconiosis is a common and highly hazardous occupational disease. The staging of pneumoconiosis is mainly carried out by experienced do...

A deep learning-based radiomics model for noninvasive diagnosis of melanoma.

To develop a noninvasive diagnostic model integrating deep learning and radiomics for improving the accuracy and clinical utility of early melanoma di...

Noninvasive nasopharyngeal cancer screening via salivary DNA detection using label-free surface-enhanced Raman spectroscopy.

Nasopharyngeal carcinoma (NPC) is a malignant tumor originating from the mucosal epithelium of the nasopharynx, which has a high incidence in southern...

Targeted inhibition of colorectal cancer by nano-dihydroartemisinin liposomes: Insights into the disruption of ABCG2 and BCL2 leading to induction of oxidative stress and metabolic disorder.

PURPOSE: Colorectal cancer is an aggressive malignancy characterized by significant drug resistance and a complex tumor microenvironment. Nano-dihydro...

Deep learning for automatic detection of hepatocellular carcinoma in dynamic contrast-enhanced MRI.

OBJECTIVE: Develop a deep learning model for automatic hepatocellular carcinoma (HCC) detection in T1 weighted imaging (WI) Dynamic Contrast-Enhanced ...

Artificial intelligence-powered signal analysis of loop-mediated isothermal amplification (LAMP) for the screening of Kaposi sarcoma at the point of care.

Unlike the polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP) lacks a consistent thermal cycle, making quantification part...

Noninvasive follicular thyroid neoplasm with papillary-like nuclear features: an update.

PURPOSE OF REVIEW: Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) has been recognized as a diagnostic entity sin...

Resting-state functional connectivity of the fronto-limbic and default mode network as neural correlates of antidepressant response in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a leading cause of disability worldwide, yet antidepressant response remains highly variable, with many...

Ensemble Learning Model: A Novel Technique to Detect Malignancy in Effusion Cytology.

AIMS AND OBJECTIVES: This study applied an ensemble learning model combining six transfer learning architectures to detect malignancy in effusion cyto...

Advancing oral leukoplakia progression recognition: A benchmark with dataset, method, and application.

Oral leukoplakia, a potentially malignant disorder, is a critical precursor to oral squamous cell carcinoma (OSCC), which accounts for 90 % of oral ca...