Other Cancers

Automated vs. manual segmentation for small renal mass imaging.

INTRODUCTION: Automated segmentation using artificial intelligence (AI) has the potential to rapidly perform three-dimensional (3D) segmentation of small renal masses (SRM). The objective of this study was to test for clinically and statistically significant differences in time spent segmenting, accuracy, and reliability when comparing manual and automated segmentation of computed tomography (CT) ...

Radiomics-Based Machine Learning versus the MAP Score for Predicting Haematocrit Decline after Laparoscopic Partial Nephrectomy.

OBJECTIVE: To evaluate and compare the ability of the Mayo Adhesive Probability (MAP) score and radiomics-based machine learning approaches to predict the degree of postoperative haematocrit (Hct) reduction using preoperative computed tomography (CT) images in patients with renal cell carcinoma (RCC) treated with laparoscopic partial nephrectomy (LPN). STUDY DESIGN: An observational retrospective ...

Organ-Specific Cancer-Associated Fibroblast Subtypes Across the Digestive System Linked to Cancer Hallmarks.

Cancer-associated fibroblasts (CAFs) are major stromal components of the tumor microenvironment (TME) and play diverse roles in gastrointestinal (GI) ...

Artificial intelligence-assisted urine cytology based on the Paris System for Reporting Urothelial Carcinoma.

BACKGROUND: Urine cytology is a noninvasive and valuable tool for detecting urothelial carcinoma but suffers from variable sensitivity and observer de...

The role of machine learning, deep learning, and MRI findings in the classification of pediatric posterior fossa tumors.

PURPOSE: Pediatric posterior fossa tumors represent a major subset of childhood central nervous system neoplasms; however, overlapping MRI features of...

Predicting Metastatic Potential from Histopathological Whole Slide Images of Primary Tumors in Head and Neck Squamous Cell Carcinoma Using Attention-Based Deep Learning and Signaling Pathway Alterations.

BACKGROUND: Head and Neck Squamous Cell Carcinoma (HNSCC) ranks as the 6th most prevalent cancer worldwide, imposing a significant burden on global he...

Integrative multi-omics analysis identifies a circadian rhythm-associated gene signature for prognosis and therapeutic stratification in lung adenocarcinoma.

BACKGROUND/OBJECTIVES: Circadian rhythm disruption is increasingly implicated in tumor progression and therapy resistance. However, its prognostic val...

Interpretable machine learning model for predicting operative difficulty in robotic total mesorectal excision for mid-low rectal cancer.

Precise preoperative prediction of surgical complexity in robot-assisted total mesorectal excision (R-TME) is essential for optimizing surgical strate...

A risk stratification framework based on ammonia-induced cell death signatures for prognostic assessment and therapeutic insights in colorectal cancer.

BACKGROUND: Ammonia, which was traditionally regarded as a metabolic by-product, has more recently emerged as a crucial regulator of tumor metabolism ...

Unveiling the Impact of Copper Metabolism on Epithelial-Mesenchymal Transition of Triple-Negative Breast Cancer: Identification of Therapeutic Targets.

Triple-negative breast cancer (TNBC) is a biologically aggressive subtype of breast cancer marked by high heterogeneity and poor prognosis. Copper met...

Supervised deep learning with gene functional annotation for cell classification.

Gene-by-gene differential expression analysis is a widely used supervised approach for interpreting single-cell RNA-sequencing (scRNA-seq) data. Howev...

Population-Level Raman Biochemical Staging of Malaria in Human Red Blood Cells Using Interpretable Machine Learning.

Accurate malaria staging is vital for treatment decisions and monitoring of transmission. Because mature Plasmodium falciparum parasites sequester in ...

Toward Autonomous Histotripsy: Integrating Deep Learning Segmentation With Robotic Control for Glioblastoma.

OBJECTIVE: Glioblastoma multiforme (GBM) is an aggressive brain tumor in which incomplete margin delineation during surgery can contribute to residual...

An artificial intelligence model for prediction of hepatocellular carcinoma risk in patients with chronic hepatitis C.

Hepatocellular carcinoma (HCC) still occurs in patients with hepatitis C who achieved sustained virologic response (SVR) after direct-acting antiviral...

Conditional survival patterns and individualized prognostic prediction in malignant peritoneal mesothelioma.

Malignant peritoneal mesothelioma (MPM) is a rare, aggressive cancer with limited treatment options and extremely poor survival outcomes. Due to the d...

Application of delta radiomics based on cone-beam computed tomography in predicting radiotherapy efficacy for nasopharyngeal carcinoma.

PURPOSE: To investigate the value of cone-beam computed tomography (CBCT)-based delta radiomics for predicting short-term radiotherapy (RT) response i...

Direct Quantification of Uncertainty in Deep Learning-Based Automatic Sleep Staging.

OBJECTIVE: To evaluate and compare different methods for quantifying uncertainty in deep learning-based automatic sleep staging, thereby enhancing tra...

Molecular Pathology of Soft-tissue Neoplasms.

Soft-tissue tumors are rare mesenchymal neoplasms characterized by extensive morphologic and genetic heterogeneity. Advances in molecular pathology ha...

From Theory to Practice: Which Biomarkers Are Ready for Predicting Response in Advanced HCC?

Systemic therapies for advanced hepatocellular carcinoma (HCC) have expanded considerably with the advent of tyrosine kinase inhibitors, immune checkp...

Enhancing urothelial carcinoma diagnosis with artificial intelligence-integrated urine cytology: Biopsy-validated accuracy and efficiency gain.

BACKGROUND: Urine cytology is a noninvasive tool for detecting urothelial carcinoma, yet its performance depends heavily on expert cytologists and tim...