Other Cancers

An interpretable deep learning biomarker for prognostication and prediction of adjuvant chemotherapy benefit in gastric cancer.

Prognostic stratification in gastric cancer (GC) currently relies on the tumour-node-metastasis (TNM) staging system, which incompletely captures tumour heterogeneity. Routine haematoxylin and eosin (H&E)-stained whole-slide images (WSIs) contain additional prognostic information that is not routinely quantified. We developed an interpretable deep learning framework using a weakly supervised Trans...

Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence.

OBJECTIVES: To characterize the capabilities of CE-marked AI products for lung nodule analysis in lung cancer screening (LCS), quantify their coverage of tasks defined in nodule management recommendations, and assess their peer-reviewed evidence. MATERIALS AND METHODS: Six core tasks in LCS (nodule detection, classification, measurement, growth assessment, malignancy risk estimation, and structure...

Machine learning-based prognostic signature integrating mitochondrial function and programmed cell death patterns in pancreatic adenocarcinoma.

The interplay between mitochondria and programmed cell deaths (PCD) is associated with tumor pathogenesis. However, the specific roles of genes relate...

Integrated multi-omics and single-cell analyses identify metabolic heterogeneity and therapeutic vulnerabilities in medullary thyroid cancer.

BACKGROUND: Medullary thyroid cancer (MTC) is a heterogeneous and aggressive malignancy with limited therapeutic options. Metabolic reprogramming, a h...

Integrating single-cell multi-omics and machine learning to reveal triaptosis heterogeneity in clear cell renal cell carcinoma.

Triaptosis, an emerging form of cell death, remains poorly characterized in terms of its heterogeneity within clear cell renal cell carcinoma (ccRCC)....

Next-generation gene editing strategies in cancer: Integrating CRISPR, PROTACs, and advanced molecular technologies.

Cancer is a significant therapeutic problem as tumors are heterogeneous, multidrug-resistant, and oncogenic drivers are undruggable. Genome editing an...

The value of the serum cytokine-to-immune ratio model in predicting the progression-free survival after interventional therapy for hepatocellular carcinoma.

OBJECTIVE: To develop and validate a prognostic nomogram for predicting progression-free survival (PFS) in patients with hepatocellular carcinoma (HCC...

Modulation of tumor-derived exosomes and reprogramming of cancer-associated fibroblasts for colorectal cancer therapy.

Hypoxia is pervasive within the solid tumor microenvironment (TME), reshaping it through exosome release. As the main component of the tumor stroma, f...

Artificial intelligence in ovarian pathophysiology and management: a systematic review and meta-analysis.

BACKGROUND: The integration of artificial intelligence (AI) into reproductive medicine and gynecologic oncology has driven transformative advances in ...

Expanding the Therapeutic Window in Oncology: Modern Strategies and Future Directions.

Background:The enhancement of the therapeutic window (TW) in oncology remains a significant challenge, as the majority of anticancer treatments face d...

Single-cell and spatial transcriptomic profiling of POU5F1 in Lung Adenocarcinoma: Dynamics, spatial niche, and prognosis via multi-algorithm ML.

BACKGROUND: POU5F1 (OCT4), a core regulator of pluripotency, plays an important role in tumor stemness and immune microenvironment remodeling, yet its...

Automatic Extraction of PET RANO Criteria with an Externally Validated Deep Learning Model: Application to [18F]FDOPA PET Imaging.

BACKGROUND: Automatic segmentation of gliomas on amino acid PET is essential for quantitative tumor assessment, a pillar in monitoring gliomas under t...

Deep Learning for Synthetic Postcontrast T1-Weighted MRI: A Systematic Review With Targeted Meta-Analysis of Brain Tumor Studies.

Background. Gadolinium-based contrast agents remain essential for MRI but carry risks. Deep learning (DL) methods have emerged as a potential approach...

Deep learning-based dose prediction in proton beam therapy for hepatocellular carcinoma: comparison of network architectures and loss functions.

In proton beam therapy (PBT) for hepatocellular carcinoma (HCC), deep learning (DL)-based dose prediction offers clinical value by providing immediate...

Automated Delineation of Couinaud Segments on CT for Future Liver Remnant Volumetry.

Purpose To develop a deep learning model that automatically delineates the eight liver Couinaud segments and the spleen on CT for future liver remnant...

Histology-defined activated cancer-associated fibroblasts are associated with poor survival in diffuse-type gastric adenocarcinoma.

Activated cancer-associated fibroblasts (aCAFs), characterized by distinct histological features including fibroblast proliferation and extensive desm...

Alignment between AI clinical decision tools and multidisciplinary tumor board decisions in prostate cancer.

PURPOSE: This study aimed to evaluate the concordance between treatment recommendations generated by LLMs and decisions made by a multidisciplinary ur...

Predicting the risk of venous thromboembolism events in older adults with rheumatoid arthritis after initiating targeted disease-modifying antirheumatic drugs: a comparison of the random survival forest and regularized cox regression models.

OBJECTIVES: To develop a random survival forest (RSF) machine learning (ML) model for predicting venous thromboembolism (VTE) risk in rheumatoid arthr...

Testing Near-Field Radio-Frequency Sensors to Predict Respiratory Distress in Patients Living With Dementia: A Pilot Feasibility and Acceptability Study.

BackgroundPeople living with dementia (PLWD) with advanced illness are prone to respiratory distress yet often cannot self-report dyspnea, delaying re...

Diagnostic Proficiency of Large Language Models in the Interpretation of Cervical Cytopathology.

To evaluate the diagnostic proficiency of well-established multimodal Large Language Models (LLMs)-specifically Gemini, Claude, and Copilot-in interpr...