Oncology/Hematology

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) of the parotid glands, submandibular glands, and level II and level III lymph nodes on magnetic resonance imaging (MRI). Outlining radiosensitive organs on images used to assist radiation therapy (radiotherapy) of patients with head and neck cancer (HNC) is a time-consuming task, in which variabil...

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 metastases that simultaneously uses contrast-enhanced and non-enhanced images as inputs, and to compare its performance with that of a model that uses only contrast-enhanced images.

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 to many areas in different domains such as health...

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 tissue is limited or unavailable for direct testing...

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 workload in clinical histopathology. Carcinomas consist...

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-checkpoint inhibitor plus chemotherapy (ICI + CT) in...

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 minimal thermal injury on surrounding healthy tissues. I...

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 localized prostate cancer (PCa) operated on by the same su...

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 older adults diagnosed with incident breast, pros...

Deep learning-based thoracic CBCT correction with histogram matching.

Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especial...

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 time during robot-assisted partial nephrectomy (RAPN)...

Hierarchical graph representations in digital pathology.

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of con...

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 and palliative care, by using mortality as a proxy...

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) have been made, to date, no viable method for radia...

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 I non-small cell lung cancer (NSCLC). Purpose To d...

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

Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence.

Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis and is widely used clinically. Dynamic cont...

Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression.

Deep learning has proven advantageous in solving cancer diagnostic or classification problems. However, it cannot explain the rationale behind human d...

Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network.

Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be corre...

Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images.

Diagnosis of different breast cancer stages using histopathology whole slide images (WSI) is the gold standard in determining the grade of tissue meta...