Therapeutic Radiology

Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.

Machine learning is a rapidly evolving field that offers physicians an innovative and comprehensive mechanism to examine various aspects of patient data. Cervical and lumbar degenerative spine disorders are commonly age-related disease processes that can utilize machine learning to improve patient outcomes with careful patient selection and intervention. The aim of this study is to examine the cur...

The role of deep learning-based survival model in improving survival prediction of patients with glioblastoma.

This retrospective study has been conducted to validate the performance of deep learning-based survival models in glioblastoma (GBM) patients alongside the Cox proportional hazards model (CoxPH) and the random survival forest (RSF). Furthermore, the effect of hyperparameters optimization methods on improving the prediction accuracy of deep learning-based survival models was investigated. Of the 30...

Personalized brachytherapy dose reconstruction using deep learning.

BACKGROUND AND PURPOSE: Accurate calculation of the absorbed dose delivered to the tumor and normal tissues improves treatment gain factor, which is t...

Applications of machine and deep learning to patient-specific IMRT/VMAT quality assurance.

In order to deliver accurate and safe treatment to cancer patients in radiation therapy using advanced techniques such as intensity modulated radiatio...

A hierarchical deep reinforcement learning framework for intelligent automatic treatment planning of prostate cancer intensity modulated radiation therapy.

We have previously proposed an intelligent automatic treatment planning (IATP) framework that builds a virtual treatment planner network (VTPN) to ope...

Independent verification of brachytherapy treatment plan by using deep learning inference modeling.

This study aims to develop a deep learning-based strategy for treatment plan check and verification of high-dose rate (HDR) brachytherapy. A deep neur...

Male pelvic multi-organ segmentation on transrectal ultrasound using anchor-free mask CNN.

PURPOSE: Current prostate brachytherapy uses transrectal ultrasound images for implant guidance, where contours of the prostate and organs-at-risk are...

A novel multi-DoF surgical robotic system for brachytherapy on liver tumor: Design and control.

PURPOSE: Radioactive seed implantation is an effective invasive treatment method for malignant liver tumors in hepatocellular carcinomas. However, cha...

Artificial intelligence in brachytherapy: a summary of recent developments.

Artificial intelligence (AI) applications, in the form of machine learning and deep learning, are being incorporated into practice in various aspects ...

Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer.

BACKGROUND AND PURPOSE: Delineation of organs at risk (OARs), such as the bladder, rectum and sigmoid, plays an important role in the delivery of opti...

Image-guided robots for low dose rate prostate brachytherapy: Perspectives on safety in design and use.

BACKGROUND: Image-guided brachytherapy (BT) robots can be used to assist urologists during seed implantation, thereby improving therapeutic effects. H...

Deep learning dose prediction for IMRT of esophageal cancer: The effect of data quality and quantity on model performance.

PURPOSE: To investigate the effect of data quality and quantity on the performance of deep learning (DL) models, for dose prediction of intensity-modu...

DeepDose: a robust deep learning-based dose engine for abdominal tumours in a 1.5 T MRI radiotherapy system.

We present a robust deep learning-based framework for dose calculations of abdominal tumours in a 1.5 T MRI radiotherapy system. For a set of patient ...

Robotic assisted CyberKnife radiosurgery for the treatment of choroidal metastasis.

PURPOSE: Choroidal metastases occur in many patients with systemic cancer and limit quality of life due to visual deterioration or pain. The limited p...

Deep learning-augmented radiotherapy visualization with a cylindrical radioluminescence system.

This study aims to demonstrate a low-cost camera-based radioluminescence imaging system (CRIS) for high-quality beam visualization that encourages acc...

Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery.

Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that A...

Simplifying Tumor Volume Estimation from Linear Dimensions for Intra-Cranial Lesions Treated with Stereotactic Radiosurgery.

AIMS: This study aims to derive simple yet robust formula(s) for the calculation of cranial tumor volume using linear tumor dimensions in anterioposte...

Machine learning and registration for automatic seed localization in 3D US images for prostate brachytherapy.

PURPOSE: New radiation therapy protocols, in particular adaptive, focal or boost brachytherapy treatments, require determining precisely the position ...

Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy.

PURPOSE: We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluenc...

Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario.

PURPOSE: Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has wide-ranging applications such as radiosurgery planning. Advances i...