Oncology/Hematology

Machine learning approach for prediction of hearing preservation in vestibular schwannoma surgery.

In vestibular schwannoma patients with functional hearing status, surgical resection while preserving the hearing is feasible. Hearing levels, tumor size, and location of the tumor have been known to be candidates of predictors. We used a machine learning approach to predict hearing outcomes in vestibular schwannoma patients who underwent hearing preservation surgery: middle cranial fossa, or retr...

High quality proton portal imaging using deep learning for proton radiation therapy: a phantom study.

Purpose; For shoot-through proton treatments, like FLASH radiotherapy, there will be protons exiting the patient which can be used for proton portal imaging (PPI), revealing valuable information for the validation of tumor location in the beam's-eye-view at native gantry angles. However, PPI has poor inherent contrast and spatial resolution. To deal with this issue, we propose a deep-learning-base...

Early experience with Watson for Oncology: a clinical decision-support system for prostate cancer treatment recommendations.

PURPOSE: Urological oncologists have difficulty providing optimal personalized care due to rapid alterations in scientific research results, medical a...

A cross-modal 3D deep learning for accurate lymph node metastasis prediction in clinical stage T1 lung adenocarcinoma.

OBJECTIVES: The evaluation of lymph node (LN) status by radiologists based on preoperative computed tomography (CT) lacks high precision for early lun...

Comparison of statistical and machine learning models for healthcare cost data: a simulation study motivated by Oncology Care Model (OCM) data.

BACKGROUND: The Oncology Care Model (OCM) was developed as a payment model to encourage participating practices to provide better-quality care for can...

Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study.

BACKGROUND: Preoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic cri...

A machine learning-based real-time tumor tracking system for fluoroscopic gating of lung radiotherapy.

To improve respiratory-gated radiotherapy accuracy, we developed a machine learning approach for markerless tumor tracking and evaluated it using lung...

Single patient convolutional neural networks for real-time MR reconstruction: coherent low-resolution versus incoherent undersampling.

Accelerated MRI involves undersampling k-space, creating unwanted artifacts when reconstructing the data. While the strategy of incoherent k-space acq...

Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) may be caused by Alzheimer's disease, Parkinson's disease (PD), cerebrovascular accident, nutritional or metabolic dis...

iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation.

In spite of the repertoire of existing cancer therapies, the ongoing recurrence and new cases of cancer poses a challenging health concern that prompt...

Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment.

The objective of this study was to assess the impact of artificial intelligence (AI)-based decision support (DS) on breast ultrasound (US) lesion ass...

Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images.

Gliomas are the most common malignant brain tumors with different grades that highly determine the rate of survival in patients. Tumor segmentation an...

Perioperative margin detection in basal cell carcinoma using a deep learning framework: a feasibility study.

PURPOSE: Basal cell carcinoma (BCC) is the most commonly diagnosed cancer and the number of diagnosis is growing worldwide due to increased exposure t...

Artificial Intelligence in radiotherapy: state of the art and future directions.

Recent advances in computing capability allowed the development of sophisticated predictive models to assess complex relationships within observationa...

Error detection using a convolutional neural network with dose difference maps in patient-specific quality assurance for volumetric modulated arc therapy.

The aim of this study was to evaluate the use of dose difference maps with a convolutional neural network (CNN) to detect multi-leaf collimator (MLC) ...

Innovations in research and clinical care using patient-generated health data.

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncolog...

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data.

INTRODUCTION: Deep learning-based algorithms have demonstrated enormous performance in segmentation of medical images. We collected a dataset of multi...

Artificial Intelligence Analysis of Gene Expression Data Predicted the Prognosis of Patients with Diffuse Large B-Cell Lymphoma.

OBJECTIVE: We aimed to identify new biomarkers in Diffuse Large B-cell Lymphoma (DLBCL) using the deep learning technique.

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence.

The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has re...

Early-Stage Lung Cancer Diagnosis by Deep Learning-Based Spectroscopic Analysis of Circulating Exosomes.

Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor...