AIMC Topic: Radiotherapy, Intensity-Modulated

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3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture.

Physics in medicine and biology
The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of the most complicated due to large target volume, multiple prescription dose levels, and many radiation-sensitive critical structures near the target. Tr...

Impact of nominal photon energies on normal tissue sparing in knowledge-based radiotherapy treatment planning for rectal cancer patients.

PloS one
The interactive adjustment of the optimization objectives during the treatment planning process has made it difficult to evaluate the impact of beam quality exclusively in radiotherapy. Without consensus in the published results, the arbitrary select...

Automated Closed- and Open-Loop Validation of Knowledge-Based Planning Routines Across Multiple Disease Sites.

Practical radiation oncology
PURPOSE: Knowledge-based planning (KBP) clinical implementation necessitates significant upfront effort, even within a single disease site. The purpose of this study was to demonstrate an efficient method for clinicians to assess the noninferiority o...

A trial for EBT3 film without batch-specific calibration using a neural network.

Physics in medicine and biology
This note reports a trial to establish an ANN (artificial neural network) method applying to EBT3 films of different batches without batch-specific calibration. Based on Pytorch (Facebook, https://pytorch.org/), a feed-forward ANN model was built to ...

Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast over computed tomographies (CTs), without the ionizing radiation exposure. However, it requires the generation of a s...

A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning.

Scientific reports
With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate thi...

Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer.

La Radiologia medica
OBJECTIVE: To develop different radiomic models based on the magnetic resonance imaging (MRI) radiomic features and machine learning methods to predict early intensity-modulated radiation therapy (IMRT) response, Gleason scores (GS) and prostate canc...

Dosimetric features-driven machine learning model for DVH prediction in VMAT treatment planning.

Medical physics
PURPOSE: Few features characterizing the dosimetric properties of the patients are included in currently available dose-volume histogram (DVH) prediction models, making it intractable to build a correlative relationship between the input and output p...

Intensity-modulated radiation therapy of anal squamous cell carcinoma: Relationship between delineation quality and regional recurrence.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Intensity-modulated radiation therapy (IMRT) is currently indicated to treat anal squamous cell carcinoma (ASCC). Conformal dose delivery and steep dose gradients may cause marginal misses. We analyzed patterns of locoregional...