Adaptive Radiation Therapy.

Li, X. Allen

doi:10.1118/1.3598076

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…The adaptive radiotherapy (ART) uses up-to-date images to create a new treatment plan to incorporate patient anatomy changes. [7][8][9] The up-to-date images usually need to be acquired using high quality imaging modalities (such as helical computed tomography (CT) and magnetic resonance (MR) imaging). [10][11][12] However, the up-to-date high-quality images for re-planning are not always available, 13 and the easily available daily image-guidance cone-beam CT (CBCT) images are inferior to the planning CT (pCT) images, and they are usually considered to be not suitable for treatment planning.…”

Section: Introductionmentioning

confidence: 99%

Quantitative assessment of adaptive radiotherapy for prostate cancer using deep learning: Bladder dose as a decision criterion

Wan,

Jiang,

Zhu

et al. 2023

Medical Physics

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BackgroundAdaptive radiotherapy (ART) can incorporate anatomical variations in a reoptimized treatment plan for fractionated radiotherapy. An automatic solution to objectively determine whether ART should be performed immediately after the daily image acquisition is highly desirable.PurposeWe investigate a quantitative criterion for whether ART should be performed in prostate cancer radiotherapy by synthesizing pseudo‐CT (sCT) images and evaluating dosimetric impact on treatment planning using deep learning approaches.Method and materialsPlanning CT (pCT) and daily cone‐beam CT (CBCT) data sets of 74 patients are used to train (60 patients) and evaluate (14 patients) a cycle adversarial generative network (CycleGAN) that performs the task of synthesizing high‐quality sCT from daily CBCT. Automatic delineation (AD) of the bladder is performed on the sCT using the U‐net. The combination of sCT and AD allows us to perform dose calculations based on the up‐to‐date bladder anatomy to determine whether the original treatment plan (ori‐plan) is still applicable. For positive cases that the patients’ anatomical changes and the associated dose calculations warrant re‐planning, we made rapid plan revisions (re‐plan) based on the ori‐plan.ResultsThe mean absolute error within the region‐of‐interests (i.e., body, bladder, fat, muscle) between the sCT and pCT are 41.2, 25.1, 26.5, and 29.0HU, respectively. Taking the calculated results of pCT doses as the standard, for PTV, the gamma passing rates of sCT doses at 1 mm/1%, 2 mm/2% are 87.92%, 98.78%, respectively. The Dice coefficients of the AD‐contours are 0.93 on pCT and 0.91 on sCT. According to the result of dose calculation, we found when the bladder volume underwent a substantial change (79.7%), the bladder dose is still within the safe limit, suggesting it is insufficient to solely use the bladder volume change as a criterion to determine whether adaptive treatment needs to be done. After AD‐contours of the bladder using sCT, there are two cases whose bladder dose . For the two cases, we perform re‐planning to reduce the bladder dose to , under the condition that the PTV meets the prescribed dose.ConclusionWe provide a dose accurate adaptive workflow for prostate cancer patients by using deep learning approaches, and implement ART that adapts to bladder dose. Of note, the specific replanning criterion for whether ART needs to be performed can adapt to different centers’ choices based on their experience and daily observations.

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Section: Introductionmentioning

confidence: 99%

Quantitative assessment of adaptive radiotherapy for prostate cancer using deep learning: Bladder dose as a decision criterion

Wan,

Jiang,

Zhu

et al. 2023

Medical Physics

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Auto‐detection of necessity for MRI‐guided online adaptive replanning using a machine learning classifier

et al. 2022

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Purpose MRI‐guided adaptive radiation therapy (MRgART), particularly daily online adaptive replanning (OLAR) can substantially improve radiation therapy delivery, however, it can be labor‐intensive and time‐consuming. Currently, the decision to perform OLAR for a treatment fraction is determined subjectively. In this work, we develop a machine learning algorithm based on structural similarity index measure (SSIM) and change in entropy to quickly and objectively determine whether OLAR is necessary for a daily MRI set. Methods A total of 109 daily MRI sets acquired on a 1.5T MR‐Linac during MRgART for 22 pancreatic cancer patients each treated with five fractions were retrospectively analyzed. For each daily MRI set, OLAR and reposition (No‐OLAR) plans were created and the superior plan with the daily fraction determined per clinical dose‐volume criteria. SSIM and entropy maps were extracted from each daily MRI set, with respect to its reference (e.g., dry‐run) MRI in the region enclosed by 50‐100% isodose surfaces. A total of six common features were extracted from SSIM maps. Pearson's rank correlation coefficient was utilized to rule out redundant SSIM features. A t‐test was used to determine significant SSIM features which were combined with the change in entropy to develop anensemble machine classifier with fivefold cross validation. The performance of the classifier was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve. Results A machine learning classifier model using two SSIM features (mean and full width at half maximum) and change in entropy was determined to be able to significantly discriminate between No‐OLAR and OLAR groups. The obtained machine learning ensemble classifier can predict OLAR necessity with a cross validated AUC of 0.93. Misclassification was found primarily for No‐OLAR cases with dosimetric plan quality closely comparable to the corresponding OLAR plans, thus, are not a major practical concern. Conclusion A machine learning classifier based on simple first‐order image features, that is, SSIM features and change in entropy, was developed to determine when OLAR is necessary for a daily MRI set with practical acceptable prediction accuracy. This classifier may be implemented in the MRgART process to automatically and objectively determine if OLAR is required following daily MRI.

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MR-Guided Radiotherapy in the Pediatric and Adolescent Patient

Kozak,

Mahase,

Traughber

et al. 2024

A Practical Guide to MR-Linac

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Adaptive Radiation Therapy.

Abstract: Adaptive Radiation Therapy., Allen Li X., Imaging in Medical Diagnosis and Therapy, William R. Hendee, Series Editor. CRC Press, Taylor & Francis Group, Boca Raton, FL, 2011, 427 pp. (hardcover). Price: $149.95. ISBN: 978-1-43981-634-9.

Cited by 5 publications

References 30 publications

Quantitative assessment of adaptive radiotherapy for prostate cancer using deep learning: Bladder dose as a decision criterion

Quantitative assessment of adaptive radiotherapy for prostate cancer using deep learning: Bladder dose as a decision criterion

Auto‐detection of necessity for MRI‐guided online adaptive replanning using a machine learning classifier

MR-Guided Radiotherapy in the Pediatric and Adolescent Patient

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