AIMC Topic: Radiotherapy, Image-Guided

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Automatic lung dose painting for functional lung avoidance radiotherapy through multi-modality-guided dose prediction.

Physics in medicine and biology
This study aims to develop a multi-modality-guided dose prediction (MMDP)-based auto-planning algorithm for functional lung avoidance radiotherapy (FLART) guided by voxel-wise lung function images.The proposed auto-planning algorithm consists of a no...

GTV segmentation in MRI guided radiotherapy with promptable foundation models.

Physics in medicine and biology
. Magnetic resonance imaging (MRI) guided radiotherapy requires the delineation of gross tumor volumes (GTV) in daily MRI from MRI-linacs. Specialized models have been developed for this task for certain tumors. This study investigated an alternative...

Adaptive radiotherapy for gastrointestinal malignancies.

Radiation oncology (London, England)
BACKGROUND: Adaptive radiotherapy (ART) is an advanced form of image-guided radiotherapy that involves the re-contouring and re-planning of a patient's treatment plan, either while the patient is on the table (online) or in between fractions (offline...

Impact of patient-specific deep learning lung organs-at-risk segmentation on accumulated dose in online adaptive 0.35 T MR-guided radiotherapy.

Physics in medicine and biology
Online adaptation in magnetic resonance imaging-guided radiotherapy (MRgRT) for lung cancer is hindered by time-consuming organs-at-risk (OARs) recontouring on daily MR images (dMRIs) and inter-/intra-observer variability. Deep learning auto-segmenta...

Improving segmentation precision in prostate cancer adaptive radiation therapy with a patient-specific network.

PloS one
Adaptive radiotherapy (ART) enhances prostate cancer treatment by accounting for daily anatomical variations, but clinical implementation remains limited due to the need for accurate and efficient auto segmentation; manual corrections after automated...

Nasopharyngeal cancer adaptive radiotherapy with CBCT-derived synthetic CT: deep learning-based auto-segmentation precision and dose calculation consistency on a C-Arm linac.

Radiation oncology (London, England)
BACKGROUND: To evaluate the precision of automated segmentation facilitated by deep learning (DL) and dose calculation in adaptive radiotherapy (ART) for nasopharyngeal cancer (NPC), leveraging synthetic CT (sCT) images derived from cone-beam CT (CBC...

Deep learning-based real-time detection of head and neck tumors during radiation therapy.

Physics in medicine and biology
Clinical drivers for real-time head and neck (H&N) tumor tracking during radiation therapy (RT) are accounting for motion caused by changes to the immobilization mask fit, and to reduce mask-related patient distress by replacing the masks with patien...

Rule-based AI automated adaptive treatment planning for image guided cervical cancer brachytherapy.

Brachytherapy
BACKGROUND AND PURPOSE: A rule-based AI system for automated adaptive treatment planning for image guided adaptive brachytherapy (IGABT) of locally advanced cervical cancer (LACC) was developed at Erasmus MC, and internally and externally validated b...

Quantitative and automatic plan-of-the-day assessment to facilitate adaptive radiotherapy in cervical cancer.

Physics in medicine and biology
To facilitate implementation of plan-of-the-day (POTD) selection for treating locally advanced cervical cancer (LACC), we developed a POTD assessment tool for CBCT-guided radiotherapy (RT). A female pelvis segmentation model (U-Seg3) is combined with...

Cone-beam computed tomography-based online adaptive radiotherapy of esophageal cancer in the neoadjuvant setting: Dosimetric analysis, toxicity and treatment response.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Chemoradiotherapy (CRT) followed by surgery is a treatment option for esophageal cancer (EC). However, concerns persist regarding cardiopulmonary toxicity and inconsistent daily target coverage due to anatomical changes. To ad...