Other Cancers

Interpretable MRI-Based Deep Learning for Alzheimer's Risk and Progression.

Timely intervention for Alzheimer's disease (AD) requires early detection. The development of immunotherapies targeting amyloid-beta and tau underscores the need for accessible, time-efficient biomarkers for early diagnosis. Here, we directly applied our previously developed MRI-based deep learning model for AD to the large Chinese SILCODE cohort (722 participants, 1,105 brain MRI scans). The mode...

Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data.

OBJECTIVES: To develop and validate a machine learning (ML) model to differentiate malignant from benign thyroid nodules (TNs) based on the routine data and provide diagnostic assistance for medical professionals.

Gradient-driven deep penetration of self-electrophoretic nanoparticles in acidic tumor microenvironments for enhanced antitumor therapy.

Difficulty of nanomedicines to effectively penetrate the tumor core and achieve effective killing of tumor stem cells is an important factor leading t...

Breakthroughs and challenges of organoid models for assessing cancer immunotherapy: a cutting-edge tool for advancing personalised treatments.

Organoid models are powerful tools for evaluating cancer immunotherapy that provide a more accurate representation of the tumour microenvironment (TME...

Integrative bioinformatics and machine learning identify key crosstalk genes and immune interactions in head and neck cancer and Hodgkin lymphoma.

Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive malignancy with complex molecular underpinnings. Hodgkin lymphoma (HL), another d...

Artificial intelligence and colorectal neoplasia detection performances in patients with positive fecal immunochemical test: Meta-analysis and systematic review.

OBJECTIVES: The combination of fecal immunochemical test (FIT) followed by colonoscopy has established itself as one of the preferred population-based...

Multimodal AI model for preoperative prediction of axillary lymph node metastasis in breast cancer using whole slide images.

In breast cancer management, predicting axillary lymph node (ALN) metastasis using whole-slide images (WSIs) of primary tumor biopsies is a challengin...

Progressive Distillation With Optimal Transport for Federated Incomplete Multi-Modal Learning of Brain Tumor Segmentation.

Multi-modal Magnetic Resonance Imaging (MRI) provide sufficient complementary information for brain tumor segmentation, however, most current approach...

MHFNet: A Multimodal Hybrid-Embedding Fusion Network for Automatic Sleep Staging.

Scoring sleep stages is essential for evaluating the status of sleep continuity and comprehending its structure. Despite previous attempts, automating...

Artificial Intelligence Measured Tumor Burden and Pre-Treatment Circulating Tumor DNA in Human Papilloma Virus-Associated Oropharynx Cancer.

BACKGROUND: Artificial intelligence (AI)-based imaging analysis and circulating tumor-associated DNA (ctDNA) are both being used diagnostically in HPV...

Artificial intelligence-based muscle analysis risk assessment of treatment-related toxicity in metastatic colorectal cancer.

IntroductionUp to 60% of adults with metastatic colorectal cancer (mCRC) receiving combination cytotoxic chemotherapy may experience loss of skeletal ...

PSMA PET/CT for prostate cancer diagnosis: current applications and future directions.

Prostate cancer (PCa) requires improved diagnostic strategies beyond conventional imaging. This review aimed to evaluate the role of prostate-specific...

Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.

BACKGROUND: Patients with early-stage non-small cell lung cancer (NSCLC) typically receive surgery as their primary form of treatment. However, studie...

Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma.

Post-hepatectomy liver failure (PHLF) is a severe complication following liver surgery. We aimed to develop a novel, interpretable machine learning (M...

Effects of artificial intelligence assistance on endoscopist performance: Comparison of diagnostic performance in superficial esophageal squamous cell carcinoma detection using video-based models.

OBJECTIVES: Superficial esophageal squamous cell carcinoma (ESCC) detection is crucial. Although narrow-band imaging improves detection, its effective...

Application of multivalent aptamers in tumor diagnosis, analysis and therapy (Review).

Cancer remains one of the leading causes of mortality worldwide, making early diagnosis and precise treatment crucial for enhancing patient survival r...

Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.

This project aimed to develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patie...

Metabolomics as a tool for understanding and treating triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous variant of breast cancer distinguished by a lack of targeted therapies, posing...

Deep learning model for predicting the RAS oncogene status in colorectal cancer liver metastases.

BACKGROUND: To develop a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CECT) to assess the rat sarcoma (RAS) onc...

A deep learning-based clinical-radiomics model predicting the treatment response of immune checkpoint inhibitors (ICIs)-based conversion therapy in potentially convertible hepatocelluar carcinoma patients: a tumor marker prognostic study.

BACKGROUND: The majority of patients with hepatocellular carcinoma (HCC) miss the opportunity of radical resection, making immune check-point inhibito...