AIMC Topic: Radiotherapy Planning, Computer-Assisted
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Quantitative and automatic plan-of-the-day assessment to facilitate adaptive radiotherapy in cervical cancer.
Physics in medicine and biology
Jun 23, 2025
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...
Explainable one-class feature extraction by adaptive resonance for anomaly detection in quality assurance.
PloS one
Jun 10, 2025
In this study, we address the inherent challenges in radiotherapy (RT) plan quality assessment (QA). RT, a prevalent cancer treatment, utilizes high-energy beams to target tumors while sparing adjacent healthy tissues. Typically, an RT plan is refine...
Feasibility study of a general model for synthetic CT generation in MRI-guided extracranial radiotherapy.
Biomedical physics & engineering express
May 19, 2025
This study aims to investigate the feasibility of a single general model to synthesize CT images across body sites, thorax, abdomen, and pelvis, to support treatment planning for MRI-only radiotherapy. A total of 157 patients who received MRI-guided ...
Evaluation of deliverable artificial intelligence-based automated volumetric arc radiation therapy planning for whole pelvic radiation in gynecologic cancer.
Scientific reports
Apr 30, 2025
This study aimed to develop a deep learning (DL)-based deliverable whole pelvic volumetric arc radiation therapy (VMAT) for patients with gynecologic cancer using a prototype DL-based automated planning support system, named RatoGuide, to evaluate it...
Using deep learning generated CBCT contours for online dose assessment of prostate SABR treatments.
Journal of applied clinical medical physics
Apr 23, 2025
Prostate Stereotactic Ablative Body Radiotherapy (SABR) is an ultra-hypofractionated treatment where small setup errors can lead to higher doses to organs at risk (OARs). Although bowel and bladder preparation protocols reduce inter-fraction variabil...
Reinforcement learning-driven automated head and neck simultaneous integrated boost (SIB) radiation therapy: flexible treatment planning aligned with clinical preferences.
Physics in medicine and biology
Apr 22, 2025
Head-and-neck simultaneous integrated boost (SIB) treatment planning using intensity modulated radiation therapy is particularly challenging due to the proximity to organs-at-risk. Depending on the specific clinical conditions, different parotid-spar...
A machine learning toolkit assisted approach for IMRT fluence map optimization: feasibility and advantages.
Biomedical physics & engineering express
Apr 22, 2025
. Traditional machine learning (ML) and deep learning (DL) applications in treatment planning rely on complex model architectures and large, high-quality training datasets. However, they cannot fully replace the conventional optimization process. Thi...
Towards real-time conformal palliative treatment of spine metastases: A deep learning approach for Hounsfield Unit recovery of cone beam CT images.
Medical physics
Apr 21, 2025
BACKGROUND: The extension of onboard cone-beam CT (CBCT) imaging for real-time treatment planning is constrained by limitations in image quality. Synthetic CT (sCT) generation using deep learning provides a potential solution to these limitations.
Two-step beam geometry optimization for volumetric modulated arc therapy gantry angles in breast treatments.
Medical physics
Apr 18, 2025
BACKGROUND: In partial arc volumetric modulated arc therapy (VMAT) for treating breast cancer, setting up the limiting gantry positions of the treatment machine is a nontrivial yet repetitive and time-consuming task during planning. Templatized solut...
Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections.
Radiation oncology (London, England)
Apr 18, 2025
BACKGROUND: Delineating the internal gross tumor volume (IGTV) is crucial for the treatment of non-small cell lung cancer (NSCLC). Deep learning (DL) enables the automation of this process; however, current studies focus mainly on multiple phases of ...
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