Oncology/Hematology

Prediction of myeloid malignant cells in Fanconi anemia using machine learning.

Fanconi anemia (FA) is an inherited bone marrow failure syndrome with cancer predisposition. Most FA patients develop aplastic anemia during childhood and have an extremely high cumulative risk to develop cancer during their lifespan. Myeloid malignancy is one of the main neoplastic risks for patients with FA, including high-risk myelodysplastic syndrome (MDS), recently renamed as myelodysplastic ...

Leveraging machine learning to predict de novo skin malignancy following lung transplantation.

AIMS: This study aimed to predict post-transplant malignancy risks at multiple levels among lung transplant recipients using machine learning (ML) and to identify key clinical and immunogenetic predictors. MATERIALS AND METHODS: A dataset of 30,917 lung transplant recipients with no prior cancer history was analyzed using pre-, peri-, and post-transplant variables. Multiple ML algorithms-gradient ...

Case-based reasoning for clinical trial recruitment tools in oncology: When you need patients to find patients.

BACKGROUND: Patient recruitment for clinical trials remains a major challenge, with 86% of trials failing to meet enrollment targets on time. In over ...

Evaluation of an Artificial Intelligence Defined Lung Nodule Malignancy Score in Incidental Pulmonary Nodules: The CREATE Study.

OBJECTIVE: To evaluate the effectiveness of the artificial intelligence-based qXR lung nodule malignancy score (qXR-LNMS) in detecting high-risk incid...

Integrating large language models (LLMs) into neuro-oncologic radiology: a retrospective feasibility assessment of GPT-4o for brain tumor diagnosis.

Interpretable machine learning using CT radiomics predicts pathological upgrading after secondary resection in non-muscle-invasive bladder cancer.

BACKGROUND AND PURPOSE: Repeat transurethral resection (ReTUR) is essential for reducing residual and recurrent non-muscle-invasive bladder cancer (NM...

The Potential of Ensemble-Based Automated Sleep Staging on Single-Channel EEG Signal From a Wearable Device.

Machine-learning-based sleep staging models have achieved expert-level performance on standard polysomnographic (PSG) data. However, their application...

Multimetallic nanostructures: innovations and applications in environmental remediation, biosensing, and medical technologies.

Multimetallic nanostructures (MNs) have emerged as versatile materials with significant potential in various applications, including environmental rem...

Artificial Intelligence-assisted Risk Stratification of Thyroid Nodules with Atypia of Undetermined Significance.

OBJECTIVE: Given the limitations of conventional approaches in managing indeterminate thyroid nodules, there remains an unmet need for non-invasive as...

Deep-learning pipeline for automated skeletal muscle segmentation and sarcopenia detection.

BACKGROUND: Sarcopenia, characterized by progressive skeletal muscle loss, is associated with poor outcomes in various diseases. Traditional methods f...

Attenuation-based ultra-low-dose lung computed tomography at 0.1 mSv to 0.3 mSv effective dose in children.

BACKGROUND: Radiation dose reduction is essential in paediatric lung computed tomography (CT). Advances in energy-integrating detector CT and deep-lea...

Machine learning-based risk modeling for safety-focused learning curve assessment in robotic left-sided colorectal cancer surgery.

The transition from laparoscopic to robotic surgery for left-sided colorectal cancer raises safety concerns during the learning curve, particularly wh...

Hybrid imaging-clinical model for predicting microvascular invasion in hepatocellular carcinoma using deep learning-derived features from CT.

The role of adipose tissue in predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains unclear. This study prop...

Development and Validation of a Urinary Exosomal miRNA Diagnostic Panel for Early Detection of Esophageal Cancer.

Esophageal squamous cell carcinoma (ESCC) remains a leading cause of cancer-related mortality, with early detection being challenging. Although endosc...

Machine Learning for Predicting Malignant Transformation in Actinic Cheilitis: A Prognostic Support System Based on Demographic and Clinical Descriptors.

OBJECTIVE: This study aimed to develop and evaluate Machine Learning models to predict the malignant transformation (MT) in patients with actinic chei...

Deep visual detection system for oral squamous cell carcinoma.

BACKGROUND: Oral Squamous Cell Carcinoma (OSCC) is a widespread and aggressive malignancy where early and accurate detection is essential for improvin...

Multiparametric MRI radiomics nomogram predicts synchronous distant metastasis in rectal cancer.

This study aimed to evaluate the utility of a multiparametric MRI-based radiomics nomogram for identifying patients with rectal cancer (RC) at high ri...

Radiogenomics predicts immune microenvironment heterogeneity and response to combination immunotherapy in hepatocellular carcinoma.

CT deep learning radiomics and genomics for predicting staging of epithelial ovarian cancer.

BACKGROUND: Accurate preoperative staging is critical for improving the prognosis, treatment, and survival outcomes of epithelial ovarian cancer (EOC)...

SERPINA1 as a Shared Biomarker in Periodontitis and Oral Squamous Cell Carcinoma.

OBJECTIVE: Periodontitis (PD) and oral squamous cell carcinoma (OSCC) frequently co‑occur in clinical populations. However, shared molecular determina...