AIMC Topic: Radiotherapy Dosage
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Development of learning-based predictive models for radiation-induced atrial fibrillation in non-small cell lung cancer patients by integrating patient-specific clinical, dosimetry, and diagnostic information.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Oct 1, 2024
BACKGROUND AND PURPOSE: Radiotherapy (RT) in non-small cell lung cancer (NSCLC) can induce cardiac adverse events, including atrial fibrillation (AF), despite advanced RT. This study integrates patient-specific information to develop learning-based m...
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...
Deep learning-based segmentation for high-dose-rate brachytherapy in cervical cancer using 3D Prompt-ResUNet.
Physics in medicine and biology
Sep 27, 2024
To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined with 3D nnUNet for rapid and consistent autosegmentation of high-risk clinical target volume (HRCTV) and organ at risk (OAR) in high-dose-rate brachyther...
Deep evidential learning for radiotherapy dose prediction.
Computers in biology and medicine
Sep 23, 2024
BACKGROUND: As we navigate towards integrating deep learning methods in the real clinic, a safety concern lies in whether and how the model can express its own uncertainty when making predictions. In this work, we present a novel application of an un...
Online Adaptive Proton Therapy Facilitated by Artificial Intelligence-Based Autosegmentation in Pencil Beam Scanning Proton Therapy.
International journal of radiation oncology, biology, physics
Sep 20, 2024
PURPOSE: Online adaptive proton therapy (oAPT) is essential to address interfractional anatomical changes in patients receiving pencil beam scanning proton therapy. Artificial intelligence (AI)-based autosegmentation can increase the efficiency and a...
Deep learning-based statistical robustness evaluation of intensity-modulated proton therapy for head and neck cancer.
Physics in medicine and biology
Sep 20, 2024
. Previous methods for robustness evaluation rely on dose calculation for a number of uncertainty scenarios, which either fails to provide statistical meaning when the number is too small (e.g., ∼8) or becomes unfeasible in daily clinical practice wh...
Deep learning-based prediction of the dose-volume histograms for volumetric modulated arc therapy of left-sided breast cancer.
Medical physics
Sep 18, 2024
BACKGROUND: The advancements in artificial intelligence and computational power have made deep learning an attractive tool for radiotherapy treatment planning. Deep learning has the potential to significantly simplify the trial-and-error process invo...
Nested CNN architecture for three-dimensional dose distribution prediction in tomotherapy for prostate cancer.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Sep 16, 2024
BACKGROUND: The hypothesis of changing network layers to increase the accuracy of dose distribution prediction, instead of expanding their dimensions, which requires complex calculations, has been considered in our study.
Evaluation of deep learning based dose prediction in head and neck cancer patients using two different types of input contours.
Journal of applied clinical medical physics
Sep 16, 2024
PURPOSE: This study evaluates deep learning (DL) based dose prediction methods in head and neck cancer (HNC) patients using two types of input contours.
Automated confidence estimation in deep learning auto-segmentation for brain organs at risk on MRI for radiotherapy.
Journal of applied clinical medical physics
Sep 16, 2024
PURPOSE: We have built a novel AI-driven QA method called AutoConfidence (ACo), to estimate segmentation confidence on a per-voxel basis without gold standard segmentations, enabling robust, efficient review of automated segmentation (AS). We have de...
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