Therapeutic Radiology

Interval between planning and frameless stereotactic radiosurgery for brain metastases: are our margins still accurate?

BACKGROUND: Advances in intracranial stereotactic radiosurgery (SRS) have led to dramatically reduced planning target volume (PTV) margins. However, tumor growth between planning and treatment may lead to treatment failure. Our purpose was to assess the kinetics of tumor growth before SRS for brain metastases.

Radiation-Induced Hypothyroidism After Radical Intensity Modulated Radiation Therapy for Oropharyngeal Carcinoma.

PURPOSE: To evaluate 2 published normal tissue complication probability models for radiation-induced hypothyroidism (RHT) on a large cohort of oropharyngeal carcinoma (OPC) patients who were treated with intensity-modulated radiation therapy (IMRT).

Predicting gamma passing rates for portal dosimetry-based IMRT QA using machine learning.

PURPOSE: Intensity-modulated radiation therapy (IMRT) quality assurance (QA) measurements are routinely performed prior to treatment delivery to verif...

Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy.

External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-...

Automated 4π radiotherapy treatment planning with evolving knowledge-base.

PURPOSE: Non-coplanar 4π radiotherapy generalizes intensity modulated radiation therapy (IMRT) to automate beam geometry selection but requires compli...

Use of artificial neural network for pretreatment verification of intensity modulation radiation therapy fields.

OBJECTIVE: The accuracy of dose delivery for intensity modulated radiotherapy (IMRT) treatments should be determined by an accurate quality assurance ...

Deep-learning-assisted automatic digitization of applicators in 3D CT image-based high-dose-rate brachytherapy of gynecological cancer.

PURPOSE: Applicator digitization is one of the most critical steps in 3D high-dose-rate brachytherapy (HDRBT) treatment planning. Motivated by recent ...

Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images.

The goal of this work was to develop a method for accurate and robust automatic segmentation of the prostate clinical target volume in transrectal ult...

Image-Guided Robotic Radiosurgery for Treatment of Recurrent Grade II and III Meningiomas. A Single-Center Study.

OBJECTIVE: Stereotactic radiosurgery (SRS) has been increasingly applied for malignant meningiomas as an alternative to conventionally fractioned radi...

Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network.

An accurate prediction of achievable dose distribution on a patient specific basis would greatly improve IMRT/VMAT planning in both efficiency and qua...

Three-dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations.

PURPOSE: The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the ...

Spatio-temporal deep learning models for tip force estimation during needle insertion.

PURPOSE: Precise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle...

Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net.

PURPOSE: Transrectal ultrasound (TRUS) is a versatile and real-time imaging modality that is commonly used in image-guided prostate cancer interventio...

Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer.

Inverse treatment planning in radiation therapy is formulated as solving optimization problems. The objective function and constraints consist of mult...

Learning-based automatic segmentation of arteriovenous malformations on contrast CT images in brain stereotactic radiosurgery.

PURPOSE: Stereotactic radiosurgery (SRS) is widely used to obliterate arteriovenous malformations (AVMs). Its performance relies on the accuracy of de...

A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies.

PURPOSE: External beam radiotherapy combined with interstitial brachytherapy is commonly used to treat patients with bulky, advanced gynecologic cance...

Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy.

INTRODUCTION: Cone-beam CT (CBCT) image quality is important for its quantitative analysis in adaptive radiation therapy. However, due to severe artif...

Automated noncoplanar treatment planning strategy in stereotactic radiosurgery of multiple cranial metastases: HyperArc and CyberKnife dose distributions.

The purpose of this study was to evaluate and compare the dosimetric effects of HyperArc-based stereotactic radiosurgery (SRS) and a robotic radiosurg...

A trial for EBT3 film without batch-specific calibration using a neural network.

This note reports a trial to establish an ANN (artificial neural network) method applying to EBT3 films of different batches without batch-specific ca...

Application of an artificial neural network model for early outcome prediction of gamma knife radiosurgery in patients with trigeminal neuralgia and determining the relative importance of risk factors.

OBJECTIVES: Stereotactic radiosurgery (SRS) is a minimally invasive modality for the treatment of trigeminal neuralgia (TN). Outcome prediction of thi...