Customizable landmark‐based field aperture design for automated whole‐brain radiotherapy treatment planning

Xiao, Yao; Cárdenas, Carlos; Rhee, Dong Joo; Netherton, Tucker; Zhang, Lifei; Nguyen, Callistus; Douglas, Raphael; Mumme, Raymond; Skett, Stephen; Patel, Tina; Trauernicht, Chris; Chung, Caroline; Simonds, Hannah; Aggarwal, Ajay; Court, Laurence E.

doi:10.1002/acm2.13839

Cited by 4 publications

(12 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The FIF algorithm uses a previously validated tool to generate field apertures based on anatomical landmarks. 12 This landmark‐based field aperture generation approach can be customized to address various clinical and patient‐specific needs. Compared to the deep learning‐based approaches 11 , 13 , the landmark‐based approach does not require additional training or model finetuning to customize or generate new field apertures.…”

Section: Discussionmentioning

confidence: 99%

“…The FIF algorithm uses a previously validated tool to generate field apertures based on anatomical landmarks 12 . This landmark‐based field aperture generation approach can be customized to address various clinical and patient‐specific needs.…”

Section: Discussionmentioning

confidence: 99%

“…Each clinical plan was generated using a customizable, landmark‐based, and automated aperture generation algorithm. 12 In this algorithm, the regions of interest are the brain, left eye, right eye, left lens, right lens, C1 vertebra, and C2 vertebra, which are automatically delineated using deep learning‐based methods. 16 , 17 Next, the regions of interest are projected into the beam's‐eye‐view.…”

Section: Methodsmentioning

confidence: 99%

“…Researchers have automatically generated customizable field apertures to satisfy different clinical requirements. 11 , 12 In the present study, we used a previously validated field generation model to generate field shapes for planning. 12 …”

Section: Introductionmentioning

confidence: 99%

“… 11 , 12 In the present study, we used a previously validated field generation model to generate field shapes for planning. 12 …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Automated field‐in‐field whole brain radiotherapy planning

Huang

Hernandez

Wang

et al. 2022

J Applied Clin Med Phys

Self Cite

View full text Add to dashboard Cite

Purpose We developed and tested an automatic field‐in‐field (FIF) solution for whole‐brain radiotherapy (WBRT) planning that creates a homogeneous dose distribution by minimizing hotspots, resulting in clinically acceptable plans. Methods A configurable auto‐planning algorithm was developed to automatically generate FIF WBRT plans independent of the treatment planning system. Configurable parameters include the definition of hotspots, target volume, maximum number of subfields, and minimum number of monitor units per field. This algorithm iteratively identifies a hotspot, creates two opposing subfields, calculates the dose, and optimizes the beam weight based on user‐configured constraints of dose‐volume histogram coverage and least‐squared cost functions. The algorithm was retrospectively tested on 17 whole‐brain patients. First, an in‐house landmark‐based automated beam aperture technique was used to generate the treatment fields and initial plans. Second, the FIF algorithm was employed to optimize the plans using physician‐defined goals of 99.9% of the brain volume receiving 100% of the prescription dose (30 Gy in 10 fractions) and a target hotspot definition of 107% of the prescription dose. The final auto‐optimized plans were assessed for clinical acceptability by an experienced radiation oncologist using a five‐point scale. Results The FIF algorithm reduced the mean (± SD) plan hotspot percentage dose from 35.0 Gy (116.6%) ± 0.6 Gy (2.0%) to 32.6 Gy (108.8%) ± 0.4 Gy (1.2%). Also, it decreased the mean (± SD) hotspot V107% [cm 3 ] from 959 ± 498 cm 3 to 145 ± 224 cm 3 . On average, plans were produced in 16 min without any user intervention. Furthermore, 76.5% of the auto‐plans were clinically acceptable (needing no or minor stylistic edits), and all of them were clinically acceptable after minor clinically necessary edits. Conclusions This algorithm successfully produced high‐quality WBRT plans and can improve treatment planning efficiency when incorporated into an automatic planning workflow.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“… 11 , 12 In the present study, we used a previously validated field generation model to generate field shapes for planning. 12 …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automated field‐in‐field whole brain radiotherapy planning

Huang

Hernandez

Wang

et al. 2022

J Applied Clin Med Phys

Self Cite

View full text Add to dashboard Cite

show abstract

Customizable landmark‐based field aperture design for automated whole‐brain radiotherapy treatment planning

Xiao

Cárdenas

Rhee

et al. 2022

J Applied Clin Med Phys

View full text Add to dashboard Cite

To develop and evaluate an automated whole-brain radiotherapy (WBRT) treatment planning pipeline with a deep learning-based autocontouring and customizable landmark-based field aperture design. Methods: The pipeline consisted of the following steps:(1) Auto-contour normal structures on computed tomography scans and digitally reconstructed radiographs using deep learning techniques, (2) locate the landmark structures using the beam's-eye-view, (3) generate field apertures based on eight different landmark rules addressing different clinical purposes and physician preferences. Two parallel approaches for generating field apertures were developed for quality control. The performance of the generated field shapes and dose distributions were compared with the original clinical plans. The clinical acceptability of the plans was assessed by five radiation oncologists from four hospitals. Results: The performance of the generated field apertures was evaluated by the Hausdorff distance (HD) and mean surface distance (MSD) from 182 patients' field apertures used in the clinic. The average HD and MSD for the generated field apertures were 16 ± 7 and 7 ± 3 mm for the first approach, respectively, and 17 ± 7 and 7 ± 3 mm, respectively, for the second approach. The differences regarding HD and MSD between the first and the second approaches were 1 ± 2 and 1 ± 3 mm, respectively. A clinical review of the field aperture design, conducted using 30 patients, achieved a 100% acceptance rate for both the first and second approaches, and the plan review achieved a 100% acceptance rate for the first approach and a 93% acceptance rate for the second approach. The average acceptance rate for meeting lens dosimetric recommendations was 80% (left lens) and 77% (right lens) for the first approach, and 70% (both left and right lenses) for the second approach, compared with 50% (left lens) and 53% (right lens) for the clinical plans. Conclusion:This study provided an automated pipeline with two field aperture generation approaches to automatically generate WBRT treatment plans. Both quantitative and qualitative evaluations demonstrated that our novel pipeline was comparable with the original clinical plans.

show abstract

Addressing the Global Expertise Gap in Radiation Oncology: The Radiation Planning Assistant

Court,

Aggarwal,

Burger

et al. 2023

JCO Global Oncology

View full text Add to dashboard Cite

PURPOSE Automation, including the use of artificial intelligence, has been identified as a possible opportunity to help reduce the gap in access and quality for radiotherapy and other aspects of cancer care. The Radiation Planning Assistant (RPA) project was conceived in 2015 (and funded in 2016) to use automated contouring and treatment planning algorithms to support the efforts of oncologists in low- and middle-income countries, allowing them to scale their efforts and treat more patients safely and efficiently (to increase access). DESIGN In this review, we discuss the development of the RPA, with a particular focus on clinical acceptability and safety/risk across jurisdictions as these are important indicators for the successful future deployment of the RPA to increase radiotherapy availability and ameliorate global disparities in access to radiation oncology. RESULTS RPA tools will be offered through a webpage, where users can upload computed tomography data sets and download automatically generated contours and treatment plans. All interfaces have been designed to maximize ease of use and minimize risk. The current version of the RPA includes automated contouring and planning for head and neck cancer, cervical cancer, breast cancer, and metastases to the brain. CONCLUSION The RPA has been designed to bring high-quality treatment planning to more patients across the world, and it may encourage greater investment in treatment devices and other aspects of cancer treatment.

show abstract

Customizable landmark‐based field aperture design for automated whole‐brain radiotherapy treatment planning

Cited by 4 publications

References 18 publications

Automated field‐in‐field whole brain radiotherapy planning

Automated field‐in‐field whole brain radiotherapy planning

Customizable landmark‐based field aperture design for automated whole‐brain radiotherapy treatment planning

Addressing the Global Expertise Gap in Radiation Oncology: The Radiation Planning Assistant

Contact Info

Product

Resources

About