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Robust automated ra...
Abstract
Ämnesord
Stäng
- Purpose: We present a framework for robust automated treatment planning using machine learning, comprising scenario-specific dose prediction and robust dose mimicking. Methods: The scenario dose prediction pipeline is divided into the prediction of nominal dose from input image and the prediction of scenario dose from nominal dose, each using a deep learning model with U-net architecture. By using a specially developed dose–volume histogram–based loss function, the predicted scenario doses are ensured sufficient target coverage despite the possibility of the training data being non-robust. Deliverable plans may then be created by solving a robust dose mimicking problem with the predictions as scenario-specific reference doses. Results: Numerical experiments are performed using a data set of 52 intensity-modulated proton therapy plans for prostate patients. We show that the predicted scenario doses resemble their respective ground truth well, in particular while having target coverage comparable to that of the nominal scenario. The deliverable plans produced by the subsequent robust dose mimicking were showed to be robust against the same scenario set considered for prediction. Conclusions: We demonstrate the feasibility and merits of the proposed methodology for incorporating robustness into automated treatment planning algorithms.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Samhällsfarmaci och klinisk farmaci (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Social and Clinical Pharmacy (hsv//eng)
Nyckelord
- dose mimicking
- knowledge-based planning
- robust optimization
- scenario dose prediction
- Automation
- Deep learning
- Knowledge based systems
- Optimization
- Radiotherapy
- Dose mimicing
- Input image
- Knowledge based planning
- Learning models
- NET architecture
- Radiation therapy treatment planning
- Target coverage
- Treatment planning
- Forecasting
- adult
- algorithm
- article
- controlled study
- dose volume histogram
- feasibility study
- human
- loss of function mutation
- male
- pipeline
- prediction
- prostate
- proton therapy
- intensity modulated radiation therapy
- organs at risk
- procedures
- radiotherapy dosage
- radiotherapy planning system
- Humans
- Radiotherapy Planning
- Computer-Assisted
- Radiotherapy
- Intensity-Modulated
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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