Oncology/Hematology

Emerging Trends in Artificial Intelligence in Neuro-Oncology.

PURPOSE OF REVIEW: This article explores the evolving role of artificial intelligence (AI) in neuro-...

Integrating transcriptomics and hybrid machine learning enables high-accuracy diagnostic modeling for nasopharyngeal carcinoma.

BACKGROUND: Nasopharyngeal carcinoma (NPC) lacks biomarkers demonstrating both high specificity and ...

Simulation-free workflow for lattice radiation therapy using deep learning predicted synthetic computed tomography: A feasibility study.

PURPOSE: Lattice radiation therapy (LRT) is a form of spatially fractionated radiation therapy that ...

Development of Machine Learning Models to Predict Tumor Endoprosthesis Survival.

BACKGROUND AND OBJECTIVES: Endoprosthetic reconstruction is the preferred approach for limb salvage ...

Letter to the editor: a commentary on "integrating machine learning and molecular docking to decipher the molecular network of aflatoxin b1-induced hepatocellular carcinoma.

Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms.

The evaluation of bone marrow pathology is essential for diagnosing and classifying myeloproliferati...

High visceral-to-subcutaneous fat area ratio is an unfavorable prognostic indicator in patients with uterine sarcoma.

PURPOSE: Uterine sarcoma is a rare disease whose association with body composition parameters is poo...

Radiogenomic correlation of hypoxia-related biomarkers in clear cell renal cell carcinoma.

PURPOSE: This study aimed to evaluate radiomic models' ability to predict hypoxia-related biomarker ...

Integrated machine learning based on cuproptosis and RNA methylation regulators to explore the molecular model of prostate cancer and provide novel insights to immunotherapy.

As a highly prevalent tumor in males, prostate cancer (PCa) needs newly developed biomarkers to gui...

Pretreatment Multi-sequence Contrast-Enhanced MRI to Predict Response to Immunotherapy in Unresectable Hepatocellular Carcinoma Using Transformer: A Multicenter Study.

Targeted combined immunotherapy (TCI) has shown certain antitumor effects in patients with unresect...

Integrative machine learning reveals the biological function and prognostic significance of α-ketoglutarate in gastric cancer.

Gastric cancer (GC) has a poor response to treatment, an unfavorable prognosis and a lack of reliabl...

Machine learning-based radiomics models for the prediction of metachronous liver metastases in patients with colorectal cancer: A multimodal study.

The aim of the present study was to investigate whether a multimodal radiomics model powered by mach...

Implementation of biomedical segmentation for brain tumor utilizing an adapted U-net model.

Using radio signals from a magnetic field, magnetic resonance imaging (MRI) represents a medical pro...

Split DNA Tetrahedron-Mediated Spatiotemporal-Hierarchy CRISPR Cascade Integrated with Au@Pt Nanolabels and Artificial Intelligence for a Cervical Cancer MicroRNA Bioassay.

The screening and monitoring of microRNAs as cancer molecular biomarkers is clinically significant, ...

Multivariate and Machine Learning-Derived Virtual Staining and Biochemical Quantification of Cancer Cells through Raman Hyperspectral Imaging.

Advances in virtual staining and spatial omics have revolutionized our ability to explore cellular a...

Time-Gated Raman Spectroscopy Combined with Deep Learning for Rapid, Label-Free Histopathological Discrimination of Gastric Cancer.

Gastric cancer is one of the most common malignant tumors of the digestive system, with a high morta...

Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis.

OBJECTIVE: This meta-analysis evaluates the diagnostic accuracy of machine learning (ML)-based magne...

DeepHeme, a high-performance, generalizable deep ensemble for bone marrow morphometry and hematologic diagnosis.

Cytomorphological analysis of the bone marrow aspirate (BMA) is pivotal for the diagnostic workup of...

Deep learning neural network prediction of postoperative complications in patients undergoing laparoscopic right hemicolectomy with or without CME and CVL for colon cancer: insights from SICE (Società Italiana di Chirurgia Endoscopica) CoDIG data.

BACKGROUND: Postoperative complications in colorectal surgery can significantly impact patient outco...

Next-Generation Sequencing-Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non-Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods.

BACKGROUND: Next-generation sequencing (NGS) has become a cornerstone of treatment for lung cancer a...

Extremity Soft Tissue Sarcoma Reconstruction Nomograms: A Clinicoradiomic, Machine Learning-Powered Predictor of Postoperative Outcomes.

PURPOSE: The choice of wound closure modality after limb-sparing extremity soft-tissue sarcoma (eSTS...