AIMC Topic: Radiosurgery
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An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body radiation therapy.
Journal of cancer research and clinical oncology
Feb 14, 2025
PURPOSE: Hepatocellular carcinoma (HCC) remains a global health concern, marked by increasing incidence rates and poor outcomes. This study seeks to develop a robust predictive model by integrating radiomics and deep learning features with clinical d...
Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.
Medical physics
Jan 29, 2025
BACKGROUND: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major ...
Brachytherapy Seed Placement by Robotic Bronchoscopy with Cone Beam Computed Tomography Guidance for Peripheral Lung Cancer: A Human Cadaveric Feasibility Pilot.
International journal of radiation oncology, biology, physics
Jan 25, 2025
PURPOSE: This study evaluates the feasibility of using robotic-assisted bronchoscopy with cone beam computed tomography (RB-CBCT) platform to perform low-dose-rate brachytherapy (LDR-BT) implants in a mechanically ventilated human cadaveric model. Po...
Efficient and accurate commissioning and quality assurance of radiosurgery beam via prior-embedded implicit neural representation learning.
Medical physics
Jan 15, 2025
BACKGROUND: Dosimetric commissioning and quality assurance (QA) for linear accelerators (LINACs) present a significant challenge for clinical physicists due to the high measurement workload and stringent precision standards. This challenge is exacerb...
Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features.
Radiation oncology (London, England)
Dec 30, 2024
BACKGROUND AND PURPOSE: Timely identification of local failure after stereotactic radiotherapy for brain metastases allows for treatment modifications, potentially improving outcomes. While previous studies showed that adding radiomics or Deep Learni...
Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations.
Radiation oncology (London, England)
Nov 25, 2024
BACKGROUND: Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm...
Early experience with an artificial intelligence-based module for brain metastasis detection and segmentation.
Journal of neuro-oncology
Oct 18, 2024
INTRODUCTION: - Accurate detection, segmentation, and volumetric analysis of brain lesions are essential in neuro-oncology. Artificial intelligence (AI)-based models have improved the efficiency of these processes. This study evaluated an AI-based mo...
A deep learning-informed interpretation of why and when dose metrics outside the PTV can affect the risk of distant metastasis in SBRT NSCLC patients.
Radiation oncology (London, England)
Sep 27, 2024
PURPOSE: Recent papers suggested a correlation between the risk of distant metastasis (DM) and dose outside the PTV, though conclusions in different publications conflicted. This study resolves these conflicts and provides a compelling explanation of...
Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jul 31, 2024
PURPOSE: This study investigated imaging biomarkers derived from PSMA-PET acquired pre- and post-metastasis-directed therapy (MDT) to predict 2-year metastasis-free survival (MFS), which provides valuable early response assessment to improve patient ...
AutoCorNN: An Unsupervised Physics-Aware Deep Learning Model for Geometric Distortion Correction of Brain MRI Images Towards MR-Only Stereotactic Radiosurgery.
Journal of imaging informatics in medicine
Jul 30, 2024
Geometric distortions in brain MRI images arising from susceptibility artifacts at air-tissue interfaces pose a significant challenge for high-precision radiation therapy modalities like stereotactic radiosurgery, necessitating sub-millimeter accurac...
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