AI Medical Compendium Topic:
Radiotherapy Planning, Computer-Assisted

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Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods.

Medical physics
Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor c...

A deep learning framework for prostate localization in cone beam CT-guided radiotherapy.

Medical physics
PURPOSE: To develop a deep learning-based model for prostate planning target volume (PTV) localization on cone beam computed tomography (CBCT) to improve the workflow of CBCT-guided patient setup.

Cone-beam CT-derived relative stopping power map generation via deep learning for proton radiotherapy.

Medical physics
PURPOSE: In intensity-modulated proton therapy (IMPT), protons are used to deliver highly conformal dose distributions, targeting tumors, and sparing organs-at-risk. However, due to uncertainties in both patient setup and relative stopping power (RSP...

Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives.

Methods (San Diego, Calif.)
Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Imaging is now playing a critical role on deployment of the clinical workflow, both for treatment plan...

Radiomics in radiation oncology-basics, methods, and limitations.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machin...

Feasibility and analysis of CNN-based candidate beam generation for robotic radiosurgery.

Medical physics
PURPOSE: Robotic radiosurgery offers the flexibility of a robotic arm to enable high conformity to the target and a steep dose gradient. However, treatment planning becomes a computationally challenging task as the search space for potential beam dir...

DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation.

Scientific reports
Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the...

Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring of Bladder and Rectum for Prostate Radiation Therapy.

Practical radiation oncology
PURPOSE: Auto-contouring may reduce workload, interobserver variation, and time associated with manual contouring of organs at risk. Manual contouring remains the standard due in part to uncertainty around the time and workload savings after accounti...

Boosting radiotherapy dose calculation accuracy with deep learning.

Journal of applied clinical medical physics
In radiotherapy, a trade-off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil-beam convolution can be much faster than Monte-Carlo methods, but less accurate. The dose difference, mostly cause...

Developing knowledge-based planning for gynaecological and rectal cancers: a clinical validation of RapidPlan.

Journal of medical radiation sciences
INTRODUCTION: To create and clinically validate knowledge-based planning (KBP) models for gynaecologic (GYN) and rectal cancer patients. Assessment of ecologic generalisability and predictive validity of conventional planning versus single calculatio...