AIMC Topic: Radiotherapy Planning, Computer-Assisted

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Single- versus multi-model in the deep learning prediction of monitor units per control point for automated treatment planning in prostate cancer.

Journal of applied clinical medical physics
BACKGROUND: In contemporary radiation therapy, the radiation is modulated to conform the prescription dose to the tumor and spare organs at risk. The modulation results from a complex mathematical calculation that requires several iterations to reach...

An innovative process for efficient automated optimizing IMRT knowledge-based planning (KBP).

Medical physics
BACKGROUND: Radiotherapy treatment planning is a time-consuming task that requires expert and skilled manpower, particularly for weight adjustment. Valuable attempts have been made to automate the treatment planning process as well as decrease comput...

Complexity-based unsupervised machine learning for patient-specific VMAT quality assurance.

Medical physics
BACKGROUND: Patient-specific quality assurance (PSQA) is essential to guarantee the requested accuracy and safety of high-precision radiotherapy treatments. With the widespread adoption of modulated-intensity techniques, there is a growing need for i...

Knowledge-based trade-off prediction for NSCLC treatment planning using multi-output regression.

Medical physics
BACKGROUND: Knowledge-based planning (KBP) is a data-driven approach that utilizes the knowledge from previous high-quality treatment plans to predict dose-volume histogram (DVH) parameters for organs-at-risk (OARs) in new cases. Research has demonst...

Impact of deep learning model uncertainty on manual corrections to MRI-based auto-segmentation in prostate cancer radiotherapy.

Journal of applied clinical medical physics
BACKGROUND: Deep learning (DL)-based organ segmentation is increasingly used in radiotherapy. While methods exist to generate voxel-wise uncertainty maps from DL-based auto-segmentation models, these maps are rarely presented to clinicians.

A predictive quality assurance model for patient-specific gamma passing rate of hyperarc-based stereotactic radiotherapy and radiosurgery of brain metastases.

Journal of applied clinical medical physics
OBJECTIVE: Measurement-based patient specific quality assurance (PSQA) is an increasingly debated topic among medical physicists. Developments like online adaptive radiotherapy and same-day stereotactic treatments limit the time to do measurement-bas...

Daily proton dose re-calculation on deep-learning corrected cone-beam computed tomography scans.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Synthetic CT (sCT) generation from cone-beam CT (CBCT) must maintain stable performance and allow for accurate dose calculation across all treatment fractions to effectively support adaptive proton therapy. This study evaluate...

Automatic contour quality assurance using deep-learning based contours.

Physics in medicine and biology
Safe deployment of auto-contouring models requires the inclusion of automated quality assurance (QA). One such approach is to use two independent auto-contouring models and compare them geometrically for acceptability. This is not effective because g...

Real-Time Dose-Guided Radiation Therapy.

International journal of radiation oncology, biology, physics
Dramatic strides have been made in real-time adaptive radiation therapy, where treating single tumors as dynamic but rigid bodies has demonstrated a halving of toxicities for prostate cancer. However, the human body is much more complex than a rigid ...