Other Cancers

Enhancing electrochemical detection through machine learning-driven prediction for canine mammary tumor biomarker with green silver nanoparticles.

This study developed an innovative biosensor strategy for the sensitive and selective detection of canine mammary tumor biomarkers, cancer antigen 15-3 (CA 15-3) and mucin 1 (MUC-1), integrating green silver nanoparticles (GAgNPs) with machine learning (ML) algorithms to achieve high diagnostic accuracy and potential for noninvasive early detection. The GAgNPs-enhanced electrochemical biosensor de...

Improved sleep stage predictions by deep learning of photoplethysmogram and respiration patterns.

Sleep staging is a crucial tool for diagnosing and monitoring sleep disorders, but the standard clinical approach using polysomnography (PSG) in a sleep lab is time-consuming, expensive, uncomfortable, and limited to a single night. Advancements in sensor technology have enabled home sleep monitoring, but existing devices still lack sufficient accuracy to inform clinical decisions. To address this...

Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images.

OBJECTIVES: This study utilizes [Tc]-methylene diphosphate (MDP) single photon emission computed tomography (SPECT) images as a reference standard to ...

Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection.

OBJECTIVES: This study aimed to evaluate the potential of deep learning (DL)-assisted automated three-dimensional quantitative tumor burden at MRI to ...

Artificial Intelligence-Based Histopathological Subtyping of High-Grade Serous Ovarian Cancer.

Four subtypes of ovarian high-grade serous carcinoma (HGSC) have previously been identified, each with different prognoses and drug sensitivities. How...

Uncertainty quantification via localized gradients for deep learning-based medical image assessments.

Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep...

STC-UNet: renal tumor segmentation based on enhanced feature extraction at different network levels.

Renal tumors are one of the common diseases of urology, and precise segmentation of these tumors plays a crucial role in aiding physicians to improve ...

The Current Application and Future Potential of Artificial Intelligence in Renal Cancer.

Artificial intelligence (AI) is the integration of human tasks into machine processes. The role of AI in kidney cancer evaluation, management, and out...

Prognostic significance of migrasomes in neuroblastoma through machine learning and multi-omics.

This study explores migrasomes' role in neuroblastoma, a common malignant tumor in children, and their potential impact on tumor formation. We analyze...

Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy.

BACKGROUND: Lenvatinib plus PD-1 inhibitors and interventional (LPI) therapy have demonstrated promising treatment effects in unresectable hepatocellu...

Enhancing Prostate Cancer Diagnosis: Artificial Intelligence-Driven Virtual Biopsy for Optimal Magnetic Resonance Imaging-Targeted Biopsy Approach and Gleason Grading Strategy.

An optimal approach to magnetic resonance imaging fusion targeted prostate biopsy (PBx) remains unclear (number of cores, intercore distance, Gleason ...

Lymph node metastasis detection using artificial intelligence in T1 colorectal cancer: A comprehensive systematic review.

We systematically reviewed the application of artificial intelligence (AI) in predicting lymph node metastasis (LNM) in T1 colorectal cancer (CRC). Th...

Neurological insights into brain-targeted cancer therapy and bioinspired microrobots.

Cancer, a multifaceted and pernicious disease, continuously challenges medicine, requiring innovative treatments. Brain cancers pose unique and daunti...

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.

BACKGROUND: Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focus...

Artificial intelligence for assisted HER2 immunohistochemistry evaluation of breast cancer: A systematic review and meta-analysis.

Accurate assessment of HER2 expression in tumor tissue is crucial for determining HER2-targeted treatment options. Nevertheless, pathologists' assessm...

Using Social Media to Understand Primary Discussions in Gastrointestinal Cancers: Machine Learning Approach.

BACKGROUND: The incidence and mortality rates of gastrointestinal (GI) cancers are high in the United States as well as worldwide. The widespread use ...

Deep learning-based hyperspectral technique identifies metastatic lymph nodes in oral squamous cell carcinoma-A pilot study.

AIMS: To establish a system based on hyperspectral imaging and deep learning for the detection of cancer cells in metastatic lymph nodes.

Diagnostic utility of transfer learning by using convolutional neural network for cytological diagnosis of malignant effusions.

INTRODUCTION: Cytological analysis of effusion specimens provides critical information regarding the diagnosis and staging of malignancies, thus guidi...

Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review.

In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversib...

Identifying potential targets for preventing cancer progression through the PLA2G1B recombinant protein using bioinformatics and machine learning methods.

Lung cancer is the deadliest and most aggressive malignancy in the world. Preventing cancer is crucial. Therefore, the new molecular targets have laid...