Artificial neural network based gynaecological image-guided adaptive brachytherapy treatment planning correction of intra-fractional organs at risk dose variation

Jaberi, Ramin; Siavashpour, Zahra; Aghamiri, Mahmoud Reza; Kirisits, Christian; Ghaderi, Reza

doi:10.5114/jcb.2017.72567

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…The models achieved mean squared errors ranging from 0.13 Gy to 0.40 Gy ( 31 ). Furthermore, dose prediction models have also been used to assess intra-fractional dose variations in OARs ( 27 ). To our knowledge, the accumulated dose of EBRT and BT is currently used only for predicting toxicity prediction and not for KBP ( 45 , 46 ).…”

Section: Discussionmentioning

confidence: 99%

Improvement of accumulated dose distribution in combined cervical cancer radiotherapy with deep learning–based dose prediction

Fu,

Chen,

Liu

et al. 2024

Front. Oncol.

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PurposeDifficulties remain in dose optimization and evaluation of cervical cancer radiotherapy that combines external beam radiotherapy (EBRT) and brachytherapy (BT). This study estimates and improves the accumulated dose distribution of EBRT and BT with deep learning–based dose prediction.Materials and methodsA total of 30 patients treated with combined cervical cancer radiotherapy were enrolled in this study. The dose distributions of EBRT and BT plans were accumulated using commercial deformable image registration. A ResNet-101–based deep learning model was trained to predict pixel-wise dose distributions. To test the role of the predicted accumulated dose in clinic, each EBRT plan was designed using conventional method and then redesigned referencing the predicted accumulated dose distribution. Bladder and rectum dosimetric parameters and normal tissue complication probability (NTCP) values were calculated and compared between the conventional and redesigned accumulated doses.ResultsThe redesigned accumulated doses showed a decrease in mean values of V50, V60, and D2cc for the bladder (−3.02%, −1.71%, and −1.19 Gy, respectively) and rectum (−4.82%, −1.97%, and −4.13 Gy, respectively). The mean NTCP values for the bladder and rectum were also decreased by 0.02‰ and 0.98%, respectively. All values had statistically significant differences (p < 0.01), except for the bladder D2cc (p = 0.112).ConclusionThis study realized accumulated dose prediction for combined cervical cancer radiotherapy without knowing the BT dose. The predicted dose served as a reference for EBRT treatment planning, leading to a superior accumulated dose distribution and lower NTCP values.

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Section: Discussionmentioning

confidence: 99%

Improvement of accumulated dose distribution in combined cervical cancer radiotherapy with deep learning–based dose prediction

Fu,

Chen,

Liu

et al. 2024

Front. Oncol.

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“…), TCP/NTCP models to estimate the clinical effect of a dose deviation, and tools to pinpoint the its most likely cause. Such tools could employ artificial intelligence as already evaluated for treatment planning optimization [64] and inter-fraction adaptation [65] .…”

Section: Requirements and Future Directions For Research Developmentmentioning

confidence: 99%

In vivo dosimetry in brachytherapy: Requirements and future directions for research, development, and clinical practice

Fonseca

Johansen

Smith

et al. 2020

Physics and Imaging in Radiation Oncology

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Brachytherapy can deliver high doses to the target while sparing healthy tissues due to its steep dose gradient leading to excellent clinical outcome. Treatment accuracy depends on several manual steps making brachytherapy susceptible to operational mistakes. Currently, treatment delivery verification is not routinely available and has led, in some cases, to systematic errors going unnoticed for years. The brachytherapy community promoted developments in in vivo dosimetry (IVD) through research groups and small companies. Although very few of the systems have been used clinically, it was demonstrated that the likelihood of detecting deviations from the treatment plan increases significantly with time-resolved methods. Time-resolved methods could interrupt a treatment avoiding gross errors which is not possible with time-integrated dosimetry. In addition, lower experimental uncertainties can be achieved by using source-tracking instead of direct dose measurements. However, the detector position in relation to the patient anatomy remains a main source of uncertainty. The next steps towards clinical implementation will require clinical trials and systematic reporting of errors and nearmisses. It is of utmost importance for each IVD system that its sensitivity to different types of errors is well understood, so that end-users can select the most suitable method for their needs. This report aims to formulate requirements for the stakeholders (clinics, vendors, and researchers) to facilitate increased clinical use of IVD in brachytherapy. The report focuses on high dose-rate IVD in brachytherapy providing an overview and outlining the need for further development and research. ☆ During the 1st ESTRO Physics Workshop celebrated in November 2017 in Glasgow, Scotland, a task group was created to stimulate the wider adoption of in vivo dosimetry for radiotherapy. The members of this task group, authors of this report, were selected on the basis of their expertise to contribute relevant input to the area of study and their long-term experience in the clinical implementation of in vivo dosimetry systems.

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“…Thanks to machine learning analysis of pre- and post-plan seeds disposition, effective algorithms were developed in order to obtain adequate target coverage and optimal OARs avoidance [ 28 , 29 ]. Intra-fractional dose variations could result in higher toxicities and delivery uncertainties; AI models may be able to optimize planning and motion management, achieving a more safe treatment delivery [ 30 ]. Recently, initial results from a phase I trial study on prostate cancer and LDR IRT have been released [ 31 ].…”

Section: Role Of Artificial Intelligence In Interventional Radiation mentioning

confidence: 99%

Artificial intelligence (AI) and interventional radiotherapy (brachytherapy): state of art and future perspectives

Fionda¹,

Boldrini²,

D’Aviero³

et al. 2020

jcb

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Purpose Artificial intelligence (AI) plays a central role in building decision supporting systems (DSS), and its application in healthcare is rapidly increasing. The aim of this study was to define the role of AI in healthcare, with main focus on radiation oncology (RO) and interventional radiotherapy (IRT, brachytherapy). Artificial intelligence in interventional radiation therapy AI in RO has a large impact in providing clinical decision support, data mining and advanced imaging analysis, automating repetitive tasks, optimizing time, and modelling patients and physicians’ behaviors in heterogeneous contexts. Implementing AI and automation in RO and IRT can successfully facilitate all the steps of treatment workflow, such as patient consultation, target volume delineation, treatment planning, and treatment delivery. Conclusions AI may contribute to improve clinical outcomes through the application of predictive models and DSS optimization. This approach could lead to reducing time-consuming repetitive tasks, healthcare costs, and improving treatment quality assurance and patient’s assistance in IRT.

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Artificial neural network based gynaecological image-guided adaptive brachytherapy treatment planning correction of intra-fractional organs at risk dose variation

Cited by 9 publications

References 27 publications

Improvement of accumulated dose distribution in combined cervical cancer radiotherapy with deep learning–based dose prediction

Improvement of accumulated dose distribution in combined cervical cancer radiotherapy with deep learning–based dose prediction

In vivo dosimetry in brachytherapy: Requirements and future directions for research, development, and clinical practice

Artificial intelligence (AI) and interventional radiotherapy (brachytherapy): state of art and future perspectives

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