Oncology/Hematology

Application potential of biogenically synthesized silver nanoparticles using L. extracts as pharmaceuticals and catalysts for organic pollutant degradation.

This study was designed to evaluate the optimal conditions for the eco-friendly synthesis of silver ...

Identification of Benign and Malignant Lung Nodules in CT Images Based on Ensemble Learning Method.

BACKGROUND AND OBJECTIVE: Under the background of urgent need for computer-aided technology to provi...

Cascaded deep learning-based auto-segmentation for head and neck cancer patients: Organs at risk on T2-weighted magnetic resonance imaging.

PURPOSE: To investigate multiple deep learning methods for automated segmentation (auto-segmentation...

Deep-learning single-shot detector for automatic detection of brain metastases with the combined use of contrast-enhanced and non-enhanced computed tomography images.

PURPOSE: To develop a deep-learning object detection model for automatic detection of brain metastas...

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions.

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied...

Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas.

Image-based analysis as a method for mutation detection can be advantageous in settings when tumor t...

Automated annotations of epithelial cells and stroma in hematoxylin-eosin-stained whole-slide images using cytokeratin re-staining.

The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workl...

Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers.

OBJECTIVES: To develop and validate a radiomics model for evaluating treatment response to immune-ch...

Enhanced precision of real-time control photothermal therapy using cost-effective infrared sensor array and artificial neural network.

Photothermal therapy (PTT) requires tight thermal dose control to achieve tumor ablation with minima...

Comparing Revo-i and da Vinci in Retzius-Sparing Robot-Assisted Radical Prostatectomy: A Preliminary Propensity Score Analysis of Outcomes.

This study aims to compare perioperative and oncologic outcomes between matched cohorts of localize...

Low-value care and excess out-of-pocket expenditure among older adults with incident cancer - A machine learning approach.

OBJECTIVE: To evaluate the association of low-value care with excess out-of-pocket expenditure among...

Deep learning-based thoracic CBCT correction with histogram matching.

Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used...

Learning-based synthetic dual energy CT imaging from single energy CT for stopping power ratio calculation in proton radiation therapy.

OBJECTIVE: Dual energy CT (DECT) has been shown to estimate stopping power ratio (SPR) map with a hi...

A deep learning- and CT image-based prognostic model for the prediction of survival in non-small cell lung cancer.

OBJECTIVE: To assist clinicians in arranging personalized treatment, planning follow-up programs and...

Hierarchical graph representations in digital pathology.

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on...

Identifying Preoperative Predictors of Operative Time and Their Impact on Outcomes in Robot-Assisted Partial Nephrectomy.

To identify preoperative characteristics in patients with renal masses that influence operative tim...

The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis.

Although significant advancements in computer-aided diagnostics using artificial intelligence (AI) h...

Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths.

BACKGROUND: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion ...

Empowering study of breast cancer data with application of artificial intelligence technology: promises, challenges, and use cases.

In healthcare, artificial intelligence (AI) technologies have the potential to create significant va...

Deep Learning for Lung Cancer Nodal Staging and Real-World Clinical Practice.

Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer.

Background Preoperative mediastinal staging is crucial for the optimal management of clinical stage ...