Oncology/Hematology

Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.

The association of the intrinsic optical and biophysical properties of cells to homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly and time-consuming labelling protocols that perturb the living cells. However, wide-ranging applicability of such label-free cell-based assays requires sufficient throughput, statistical pow...

Extracting and classifying diagnosis dates from clinical notes: A case study.

Myeloproliferative neoplasms (MPNs) are chronic hematologic malignancies that may progress over long disease courses. The original date of diagnosis is an important piece of information for patient care and research, but is not consistently documented. We describe an attempt to build a pipeline for extracting dates with natural language processing (NLP) tools and techniques and classifying them as...

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up.

Some patients with acromegaly do not reach the remission standard in the short term after surgery but achieve remission without additional postoperat...

Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma.

BACKGROUND: Deep learning is promising to predict treatment response. We aimed to evaluate and validate the predictive performance of the CT-based mod...

Transoral robotic parapharyngeal approach to the submandibular space.

The aim of the study was to demonstrate a novel transoral parapharyngeal approach to the submandibular space. The method used was the clinical case of...

Machine Learning-Based MRI Texture Analysis to Predict the Histologic Grade of Oral Squamous Cell Carcinoma.

This study aimed to explore the performance of machine learning (ML)-based MRI texture analysis in discriminating between well-differentiated (WD) or...

Evaluation of a multiview architecture for automatic vertebral labeling of palliative radiotherapy simulation CT images.

PURPOSE: The purpose of this work was to evaluate the performance of X-Net, a multiview deep learning architecture, to automatically label vertebral l...

Robotic Neck Dissection.

Management of head and neck squamous cell carcinoma necessitates a multimodal approach. The neck dissection has evolved over many years but is well es...

Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives.

Artificial intelligence and machine learning based approaches are increasingly finding their way into various areas of nuclear medicine imaging. With ...

Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports.

BACKGROUND: The widespread use of electronic patient-generated health data has led to unprecedented opportunities for automated extraction of clinical...

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance.

Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine lea...

Automated detection of cribriform growth patterns in prostate histology images.

Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patte...

Molecular imaging and deep learning analysis of uMUC1 expression in response to chemotherapy in an orthotopic model of ovarian cancer.

Artificial Intelligence (AI) algorithms including deep learning have recently demonstrated remarkable progress in image-recognition tasks. Here, we ut...

Personalized prediction of daily eczema severity scores using a mechanistic machine learning model.

BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalized treatment strat...

A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes.

Our understanding of noncoding mutations in cancer genomes has been derived primarily from mutational recurrence analysis by aggregating clinical samp...

Development of machine learning-based clinical decision support system for hepatocellular carcinoma.

There is a significant discrepancy between the actual choice for initial treatment option for hepatocellular carcinoma (HCC) and recommendations from ...

Synthesis of diagnostic quality cancer pathology images by generative adversarial networks.

Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images....

Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.

Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-consuming task. In this study, we aim to develop a...

Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning.

PURPOSE: To develop a deep learning-based metal artifact reduction (DL-MAR) method using unpaired data and to evaluate its dosimetric impact in head a...

Artificial intelligence technology applications in the pathologic diagnosis of the gastrointestinal tract.

Artificial intelligence (AI) is a complex technology with a steady flow of new applications, including in the pathology laboratory. Applications of AI...