Kwa et al., 1998 - Google Patents
Evaluation of two dose–volume histogram reduction models for the prediction of radiation pneumonitisKwa et al., 1998
- Document ID
- 6790808331295231857
- Author
- Kwa S
- Theuws J
- Wagenaar A
- Damen E
- Boersma L
- Baas P
- Muller S
- Lebesque J
- Publication year
- Publication venue
- Radiotherapy and Oncology
External Links
Snippet
Purpose: To evaluate the similarities between the mean lung dose and two dose–volume histogram (DVH) reduction techniques of 3D dose distributions of the lung. Patients and methods: DVHs of the lungs were calculated from 3D dose distributions of patients treated …
- 206010037765 Radiation pneumonitis 0 title abstract description 21
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/103—Treatment planning systems
- A61N5/1031—Treatment planning systems using a specific method of dose optimization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/3437—Medical simulation or modelling, e.g. simulating the evolution of medical disorders
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/103—Treatment planning systems
- A61N5/1038—Treatment planning systems taking into account previously administered plans applied to the same patient, i.e. adaptive radiotherapy
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/1042—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy with spatial modulation of the radiation beam within the treatment head
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/103—Treatment planning systems
- A61N5/1037—Treatment planning systems taking into account the movement of the target, e.g. 4D-image based planning
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/1048—Monitoring, verifying, controlling systems and methods
- A61N5/1064—Monitoring, verifying, controlling systems and methods for adjusting radiation treatment in response to monitoring
- A61N5/1065—Beam adjustment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Kwa et al. | Evaluation of two dose–volume histogram reduction models for the prediction of radiation pneumonitis | |
Knöös et al. | Comparison of dose calculation algorithms for treatment planning in external photon beam therapy for clinical situations | |
Seppenwoolde et al. | Comparing different NTCP models that predict the incidence of radiation pneumonitis | |
Bodensteiner | RayStation: External beam treatment planning system | |
Rosu et al. | Dose reconstruction in deforming lung anatomy: dose grid size effects and clinical implications | |
Du Plessis et al. | The indirect use of CT numbers to establish material properties needed for Monte Carlo calculation of dose distributions in patients | |
Fracchiolla et al. | Clinical validation of a GPU-based Monte Carlo dose engine of a commercial treatment planning system for pencil beam scanning proton therapy | |
Xing et al. | Boosting radiotherapy dose calculation accuracy with deep learning | |
Vedam et al. | Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator‐based four‐dimensional radiation delivery | |
Pawlicki et al. | Monte Carlo simulation for MLC-based intensity-modulated radiotherapy | |
Papanikolaou et al. | Dose‐calculation algorithms in the context of inhomogeneity corrections for high energy photon beams | |
Yan et al. | Expected treatment dose construction and adaptive inverse planning optimization: implementation for offline head and neck cancer adaptive radiotherapy | |
Castriconi et al. | Knowledge-based automatic optimization of adaptive early-regression-guided VMAT for rectal cancer | |
Vanneste et al. | Ano-rectal wall dose-surface maps localize the dosimetric benefit of hydrogel rectum spacers in prostate cancer radiotherapy | |
Pokhrel et al. | Assessment of Monte Carlo algorithm for compliance with RTOG 0915 dosimetric criteria in peripheral lung cancer patients treated with stereotactic body radiotherapy | |
McCulloch et al. | A simulation study to assess the potential impact of developing normal tissue complication probability models with accumulated dose | |
Chvetsov | Tumor response parameters for head and neck cancer derived from tumor‐volume variation during radiation therapy | |
Zhang et al. | Knowledge-based tradeoff hyperplanes for head and neck treatment planning | |
Wang et al. | Feasibility study of fast intensity‐modulated proton therapy dose prediction method using deep neural networks for prostate cancer | |
Reijtenbagh et al. | Multi-center dosimetric predictions to improve plan quality for brachytherapy for cervical cancer treatment | |
Rice et al. | The implementation of RapidPlan in predicting deep inspiration breath-hold candidates with left-sided breast cancer | |
Rong et al. | A planning study for palliative spine treatment using StatRT and megavoltage CT simulation | |
Fleming et al. | A method for the prediction of late organ-at-risk toxicity after radiotherapy of the prostate using equivalent uniform dose | |
Anaya et al. | Assessing the feasibility of adaptive planning for prostate radiotherapy using Smartadapt deformable image registration | |
Li et al. | Managing tumor changes during radiotherapy using a deep learning model |