AIMC Topic: Radiosurgery

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Development of a tracking error prediction system for the CyberKnife Synchrony Respiratory Tracking System with use of support vector regression.

Medical & biological engineering & computing
PURPOSE: The accuracy of the CyberKnife Synchrony Respiratory Tracking System is dependent on the breathing pattern of a patient. Therefore, the tracking error in each patient must be determined. Support vector regression (SVR) can be used to easily ...

A new prognostic score for predicting survival in patients treated with robotic stereotactic radiotherapy for brain metastases.

Scientific reports
The study aimed to analyze potential prognostic factors in patients treated with robotic radiosurgery for brain metastases irrespective of primary tumor location and create a simple prognostic score that can be used without a full diagnostic workup. ...

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy.

Medical physics
PURPOSE: The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment c...

Computation of epistemic uncertainty due to limited data samples in small field dosimetry using Fuzzy Set Theory.

The British journal of radiology
OBJECTIVE: To estimate the epistemic (or fuzzy) uncertainty, arising due to limited data samples in the measurement of the output factors (OFs) of the small fields using Fuzzy Set Theory (FST).

Robotic assisted CyberKnife radiosurgery for the treatment of choroidal metastasis.

Eye (London, England)
PURPOSE: Choroidal metastases occur in many patients with systemic cancer and limit quality of life due to visual deterioration or pain. The limited prognosis of these patients demand treatment approaches that aim at a quick response and easy applica...

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

Scientific reports
Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensiv...

Automatic detection and segmentation of multiple brain metastases on magnetic resonance image using asymmetric UNet architecture.

Physics in medicine and biology
Detection of brain metastases is a paramount task in cancer management due both to the number of high-risk patients and the difficulty of achieving consistent detection. In this study, we aim to improve the accuracy of automated brain metastasis (BM)...

Deep learning-based real-time volumetric imaging for lung stereotactic body radiation therapy: a proof of concept study.

Physics in medicine and biology
Due to the inter- and intra- variation of respiratory motion, it is highly desired to provide real-time volumetric images during the treatment delivery of lung stereotactic body radiation therapy (SBRT) for accurate and active motion management. In t...

Using deep learning to model the biological dose prediction on bulky lung cancer patients of partial stereotactic ablation radiotherapy.

Medical physics
PURPOSE: To develop a biological dose prediction model considering tissue bio-reactions in addition to patient anatomy for achieving a more comprehensive evaluation of tumor control and promoting the automatic planning of bulky lung cancer.

Deep learning-based survival analysis for brain metastasis patients with the national cancer database.

Journal of applied clinical medical physics
PURPOSE: Prognostic indices such as the Brain Metastasis Graded Prognostic Assessment have been used in clinical settings to aid physicians and patients in determining an appropriate treatment regimen. These indices are derivative of traditional surv...