AIMC Topic: Radiosurgery
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Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.
International journal of radiation oncology, biology, physics
Apr 12, 2024
PURPOSE: To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS).
Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT.
Medical physics
Mar 27, 2024
BACKGROUND: Stereotactic body radiotherapy of thoracic and abdominal tumors has to account for respiratory intrafractional tumor motion. Commonly, an external breathing signal is continuously acquired that serves as a surrogate of the tumor motion an...
Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.
Journal of applied clinical medical physics
Mar 4, 2024
PURPOSE: Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lung cancer provides important information for the feasibility of the individualized radiotherapy and allows to select the appropriate treatment strateg...
Humans
Radiotherapy Planning, Computer-Assisted
Radiotherapy Dosage
Radiotherapy, Intensity-Modulated
Radiosurgery
Radiomics
Male
Middle Aged
Image Processing, Computer-Assisted
Lung Neoplasms
Carcinoma, Non-Small-Cell Lung
Positron Emission Tomography Computed Tomography
Neoplasm Recurrence, Local
Retrospective Studies
Machine Learning
Female
Algorithms
Aged
Prognosis
Aged, 80 and over
Deep match: A zero-shot framework for improved fiducial-free respiratory motion tracking.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Feb 24, 2024
BACKGROUND AND PURPOSE: Motion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during rad...
Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline.
Journal of neuro-oncology
Feb 1, 2024
PURPOSE: Close MRI surveillance of patients with brain metastases following Stereotactic Radiosurgery (SRS) treatment is essential for assessing treatment response and the current disease status in the brain. This follow-up necessitates the compariso...
Efficacy of stereotactic body radiotherapy and response prediction using artificial intelligence in oligometastatic gynaecologic cancer.
Gynecologic oncology
Jan 24, 2024
PURPOSE: We present a large real-world multicentric dataset of ovarian, uterine and cervical oligometastatic lesions treated with SBRT exploring efficacy and clinical outcomes. In addition, an exploratory machine learning analysis was performed.
Determination of output factor for CyberKnife using scintillation dosimetry and deep learning.
Physics in medicine and biology
Jan 22, 2024
. Small-field dosimetry is an ongoing challenge in radiotherapy quality assurance (QA) especially for radiosurgery systems such as CyberKnife. The objective of this work is to demonstrate the use of a plastic scintillator imaged with a commercial cam...
Deep Learning-Guided Dosimetry for Mitigating Local Failure of Patients With Non-Small Cell Lung Cancer Receiving Stereotactic Body Radiation Therapy.
International journal of radiation oncology, biology, physics
Dec 5, 2023
PURPOSE: Non-small cell lung cancer (NSCLC) stereotactic body radiation therapy with 50 Gy/5 fractions is sometimes considered controversial, as the nominal biologically effective dose (BED) of 100 Gy is felt by some to be insufficient for long-term ...
Deep learning-based detection and quantification of brain metastases on black-blood imaging can provide treatment suggestions: a clinical cohort study.
European radiology
Sep 2, 2023
OBJECTIVES: We aimed to evaluate whether deep learning-based detection and quantification of brain metastasis (BM) may suggest treatment options for patients with BMs.
Federated Learning: A Cross-Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery.
Journal of magnetic resonance imaging : JMRI
Aug 12, 2023
BACKGROUND: Deep learning-based segmentation algorithms usually required large or multi-institute data sets to improve the performance and ability of generalization. However, protecting patient privacy is a key concern in the multi-institutional stud...
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