Treatment Planning Protocols: A Method to Improve Consistency in IMRT Planning

Bahm, Jamie; Montgomery, Lynn

doi:10.1016/j.meddos.2010.02.001

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…During routine inverse treatment planning, planners usually set optimization parameters subjectively based on their experience. Studies have concluded that the quality of IMRT plans varies among institutes and planners . Recently, mathematical algorithms have been developed for knowledge‐based planning and quality assurance (QA) with few manual interventions .…”

Section: Introductionmentioning

confidence: 99%

A feasibility study on an automated method to generate patient‐specific dose distributions for radiotherapy using deep learning

et al. 2018

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Purpose: To develop a method for predicting optimal dose distributions, given the planning image and segmented anatomy, by applying deep learning techniques to a database of previously optimized and approved Intensity-modulated radiation therapy treatment plans. Methods: Eighty cases of early-stage nasopharyngeal cancer (NPC) were included in the study. Seventy cases were chosen randomly as the training set and the remaining as the test set. The inputs were the images with structures, with each target and organs at risk (OARs) assigned a unique label. The outputs were dose maps, including coarse dose maps and converted fine dose maps (FDM) from convolution. Two types of input images with structures were used in the model building. One type of input included the images (with associated structures) without manipulation. The second type of input involved modifying the image gray label with information from radiation beam geometry. ResNet101 was chosen as the deep learning network for both. The accuracy of predicted dose distributions was evaluated against the corresponding dose as used in the clinic. A global three-dimensional gamma analysis was calculated for the evaluation.Results: The proposed model trained with the two different sets of input images and structures could both predict patient-specific dose distributions accurately. For the out-of-field dose distributions, the model obtained from the input with radiation geometry performed better (dose difference in %, 4.7 AE 6.1% vs 5.5 AE 7.9%, P < 0.05). The mean Gamma pass rates of dose distributions predicted with both types of input were comparable for most OARs (P > 0.05), except for the bilateral optic nerves and the optic chiasm. Conclusions: The proposed system with radiation geometry added to the input is a promising method to generate patient-specific dose distributions for radiotherapy. It can be applied to obtain the dose distributions slice-by-slice for planning quality assurance and for guiding automated planning.

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Section: Introductionmentioning

confidence: 99%

A feasibility study on an automated method to generate patient‐specific dose distributions for radiotherapy using deep learning

et al. 2018

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“…Current manual radiotherapy planning procedures are subjective and time-consuming, and since planning relies heavily on the individual planner's knowledge and experience, the quality of radiotherapy plans may vary between different planners at different institutions. [1][2][3][4][5][6] Moreover, it is difficult to achieve an optimized individual treatment plan for each patient with the group dose limitation for the organ at risk (OAR). Knowledge-based radiotherapy planning (KBRP) is an objective tool for incorporating patientspecific information and prior experience into the treatment planning process.…”

Section: Introductionmentioning

confidence: 99%

“…Radiotherapy treatment planning is essential to create a high‐quality optimized plan based on the patient’s specific anatomical structures. Current manual radiotherapy planning procedures are subjective and time‐consuming, and since planning relies heavily on the individual planner’s knowledge and experience, the quality of radiotherapy plans may vary between different planners at different institutions 1–6 . Moreover, it is difficult to achieve an optimized individual treatment plan for each patient with the group dose limitation for the organ at risk (OAR).…”

Section: Introductionmentioning

confidence: 99%

DVHnet: A deep learning‐based prediction of patient‐specific dose volume histograms for radiotherapy planning

et al. 2021

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Purpose To develop a deep learning method to predict patient‐specific dose volume histograms (DVHs) for radiotherapy planning. Methods Patient data included 180 cases with nasopharyngeal cancer, of which 153 cases were used for training and 27 for testing. A network (named “DVHnet”) based on a convolutional neural network (CNN) was designed for directly predicting DVHs of organs at risk (OARs). Two‐channel images with contoured structures were generated as the inputs for training the model. A one‐dimensional array consisting of 256 continuous volume percentages on a DVH curve for each slice was calculated as the corresponding output. The combined DVH was then calculated. Sixteen OARs were modeled in the study. Prediction accuracy was evaluated against the corresponding DVH curve of ground truth (GT) plans. A global DVH analysis and critical dosimetry metrics for each OAR were calculated for quantitative evaluation. The performance of DVHnet also was evaluated against two baselines: DosemapNet (developed by our research group) and commercial RapidPlan software. Results The predicted mean difference in average dose of all OARs using DVHnet was 0.30 ± 0.95 Gy. And the predicted differences in D2% and D50 can be control within 2.32 and 0.69 Gy. For most OARs, there were no obvious differences between the dosimetric metrics of the predicted and GT values for both DVHnet and DosemapNet (P ≥ 0.05). Only the predicted D2% of the optic organs for DVHnet, and of brain stem PRV for DosemapNet displayed statistically significant differences. Except for the optic organs, DVHnet performs better than or comparably with RapidPlan. The mean difference in proportion of points of interest was 3.59% ± 7.78%. Conclusions A deep learning network model was developed to automatically extract useful features for accurate prediction of patient‐specific DVH curves directly. The performance of DVHnet was comparable to DosemapNet and RapidPlan.

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“…Several groups have developed a knowledgebased approach using a database of prior plans developed clinically to generate acceptable IMRT treatment plans of high quality with reduced planning times [11,12]. Others have developed treatment planning protocols to reduce planning times [13]. Both approaches serve to make the planning process independent of planner experience.…”

Section: Introductionmentioning

confidence: 99%

Template-based Intensity-Modulated Radiation Therapy: A Cost-effective Intensity-Modulated Radiation Therapy Planning Procedure for Prostate Cancer

Cao

Ramaseshan²,

Cooper

et al. 2015

Journal of Medical Imaging and Radiation Sciences

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Treatment Planning Protocols: A Method to Improve Consistency in IMRT Planning

Cited by 6 publications

References 1 publication

A feasibility study on an automated method to generate patient‐specific dose distributions for radiotherapy using deep learning

A feasibility study on an automated method to generate patient‐specific dose distributions for radiotherapy using deep learning

DVHnet: A deep learning‐based prediction of patient‐specific dose volume histograms for radiotherapy planning

Template-based Intensity-Modulated Radiation Therapy: A Cost-effective Intensity-Modulated Radiation Therapy Planning Procedure for Prostate Cancer

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