AIMC Topic: Radiotherapy Dosage

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Essentially unedited deep-learning-based OARs are suitable for rigorous oropharyngeal and laryngeal cancer treatment planning.

Journal of applied clinical medical physics
Quality of organ at risk (OAR) autosegmentation is often judged by concordance metrics against the human-generated gold standard. However, the ultimate goal is the ability to use unedited autosegmented OARs in treatment planning, while maintaining th...

Predictive modeling of dose-volume parameters of carcinoma tongue cases using machine learning models.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
The aim of this study is to create a single institution-based machine learning model for a dose prediction generation tool for post-operative carcinoma of the tongue cases prospectively. Intensity-modulated radiotherapy (IMRT) plans for 20 patients w...

Robust stochastic optimization of needle configurations for robotic HDR prostate brachytherapy.

Medical physics
BACKGROUND: Ideally, inverse planning for HDR brachytherapy (BT) should include the pose of the needles which define the trajectory of the source. This would be particularly interesting when considering the additional freedom and accuracy in needle p...

Technical note: Evaluation of deep learning based synthetic CTs clinical readiness for dose and NTCP driven head and neck adaptive proton therapy.

Medical physics
BACKGROUND: Adaptive proton therapy workflows rely on accurate imaging throughout the treatment course. Our centre currently utilizes weekly repeat CTs (rCTs) for treatment monitoring and plan adaptations. However, deep learning-based methods have re...

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy.

Medical physics
BACKGROUND: Accurate and efficient dose calculation is essential for on-line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL-based dose predicti...

Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity modulated radiation therapy.

Medical physics
BACKGROUND: Automatic solutions for generating radiotherapy treatment plans using deep learning (DL) have been investigated by mimicking the voxel's dose. However, plan optimization using voxel-dose features has not been extensively studied.

Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network.

Physics in medicine and biology
. In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT v...

Deep learning in MRI-guided radiation therapy: A systematic review.

Journal of applied clinical medical physics
Recent advances in MRI-guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real-time MRI monitoring, and the MRI-only treatment planning workflow. Given the rapid growth and emergence of new...

Deep-learning Method for the Prediction of Three-Dimensional Dose Distribution for Left Breast Cancer Conformal Radiation Therapy.

Clinical oncology (Royal College of Radiologists (Great Britain))
AIMS: An increase in the demand of a new generation of radiotherapy planning systems based on learning approaches has been reported. At this stage, the new approach is able to improve the planning speed while saving a reasonable level of plan quality...