Other Cancers

Preirradiation of Spheroids with Ac-Trastuzumab Improves Penetration of Ac-Liposomes and MIRDcell Predictions of Responses to Drug Cocktails.

This investigation examined the factors involved in predicting the responses of micrometastases to t...

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 ...

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...

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...

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...

Monitoring of Early Skin Reactions by Optical Coherence Tomography and Dermatoscopy in Patients Receiving Radiation Therapy for Head and Neck Cancer.

PURPOSE: Patients with head and neck cancer undergoing radiation therapy (RT) may experience pronoun...

Deep learning and inflammatory markers predict early response to immunotherapy in unresectable NSCLC: A multicenter study.

Immune checkpoint inhibitors (ICIs) demonstrate substantial interpatient variability in clinical eff...

Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging.

Accurate brain tumor classification is essential in neuro-oncology, as it directly informs treatment...

Thalamic neural activity and epileptic network analysis using stereoelectroencephalography: a prospective study protocol.

INTRODUCTION: Epilepsy is a prevalent chronic neurological disorder, with approximately one-third of...

Preoperative prediction model for benign and malignant gallbladder polyps on the basis of machine-learning algorithms.

BACKGROUND: This study aimed to differentiate between benign and malignant gallbladder polyps preope...

"Artificial intelligence and multi-modal data integration in renal cell carcinoma a review of its potential and limitations".