Usefulness of EZFluence software for radiotherapy planning of breast cancer treatment

Yoder, Todd; Hsia, A; Xu, Zhigang; Stessin, A.; Ryu, Samuel

doi:10.1016/j.meddos.2018.12.001

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…This includes automation for simpler treatments, such as four-field box treatments and complex VMAT planning. There are, however, an increasing number of commercial (eg, DLC Expert, Mvision, Limbus.ai, and ART-Plan) and open-source (eg, Unet, Vnet, and nnUnet) autocontouring 35 , 36 and, to a lesser degree, autoplanning 37 solutions available. In addition, the availability of scripting in some commercial treatment planning systems has further increased the ability of clinical teams to develop tools to improve their workflows.…”

Section: Discussionmentioning

confidence: 99%

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

Court,

Aggarwal,

Burger

et al. 2023

JCO Global Oncology

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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.

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Section: Discussionmentioning

confidence: 99%

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

Court,

Aggarwal,

Burger

et al. 2023

JCO Global Oncology

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“…[16][17][18] A variety of commercially available automation tools exist, including but not limited to atlas-based 19 and deep-learningbased 13 auto-contouring (AC), knowledge-based planning (KBP), 20 rule-based automated planning, 21 and automated field-in-field planning. 22 However, little is known about the scale of clinical implementation of ATP techniques in the United States. We hypothesized that clinical adoption of treatment planning automation may be less than fully realized and furthermore that barriers to the implementation may exist.…”

Section: Introductionmentioning

confidence: 99%

“…Prospective studies have shown that in carefully controlled situations, automated treatment planning (ATP) generates plans of comparable or better clinical quality, at significant time savings 16–18 . A variety of commercially available automation tools exist, including but not limited to atlas‐based 19 and deep‐learning‐based 13 auto‐contouring (AC), knowledge‐based planning (KBP), 20 rule‐based automated planning, 21 and automated field‐in‐field planning 22 …”

Section: Introductionmentioning

confidence: 99%

Barriers and facilitators to clinical implementation of radiotherapy treatment planning automation: A survey study of medical dosimetrists

Petragallo

Bardach

Ramirez

et al. 2022

J Applied Clin Med Phys

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Little is known about the scale of clinical implementation of automated treatment planning techniques in the United States. In this work, we examine the barriers and facilitators to adoption of commercially available automated planning tools into the clinical workflow using a survey of medical dosimetrists. Methods/materials: Survey questions were developed based on a literature review of automation research and cognitive interviews of medical dosimetrists at our institution. Treatment planning automation was defined to include autocontouring and automated treatment planning. Survey questions probed frequency of use, positive and negative perceptions, potential implementation changes, and demographic and institutional descriptive statistics. The survey sample was identified using both a LinkedIn search and referral requests sent to physics directors and senior physicists at 34 radiotherapy clinics in our state. The survey was active from August 2020 to April 2021. Results: Thirty-four responses were collected out of 59 surveys sent.Three categories of barriers to use of automation were identified. The first related to perceptions of limited accuracy and usability of the algorithms. Eighty-eight percent of respondents reported that auto-contouring inaccuracy limited its use, and 62% thought it was difficult to modify an automated plan, thus limiting its usefulness. The second barrier relates to the perception that automation increases the probability of an error reaching the patient. Third, respondents were concerned that automation will make their jobs less satisfying and less secure. Large majorities reported that they enjoyed plan optimization, would not want to lose that part of their job, and expressed explicit job security fears. Conclusion:To our knowledge this is the first systematic investigation into the views of automation by medical dosimetrists. Potential barriers and facilitators to use were explicitly identified. This investigation highlights several concrete approaches that could potentially increase the translation of automation into the clinic, along with areas of needed research.

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Novel in-house knowledge-based automated planning system for lung cancer treated with intensity-modulated radiotherapy

Shao,

Guo,

Wang

et al. 2023

Strahlenther Onkol

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Purpose The goal of this study was to propose a knowledge-based planning system which could automatically design plans for lung cancer patients treated with intensity-modulated radiotherapy (IMRT). Methods and materials From May 2018 to June 2020, 612 IMRT treatment plans of lung cancer patients were retrospectively selected to construct a planning database. Knowledge-based planning (KBP) architecture named αDiar was proposed in this study. It consisted of two parts separated by a firewall. One was the in-hospital workstation, and the other was the search engine in the cloud. Based on our previous study, A‑Net in the in-hospital workstation was used to generate predicted virtual dose images. A search engine including a three-dimensional convolutional neural network (3D CNN) was constructed to derive the feature vectors of dose images. By comparing the similarity of the features between virtual dose images and the clinical dose images in the database, the most similar feature was found. The optimization parameters (OPs) of the treatment plan corresponding to the most similar feature were assigned to the new plan, and the design of a new treatment plan was automatically completed. After αDiar was developed, we performed two studies. The first retrospective study was conducted to validate whether this architecture was qualified for clinical practice and involved 96 patients. The second comparative study was performed to investigate whether αDiar could assist dosimetrists in improving the quality of planning for the patients. Two dosimetrists were involved and designed plans for only one trial with and without αDiar; 26 patients were involved in this study. Results The first study showed that about 54% (52/96) of the automatically generated plans would achieve the dosimetric constraints of the Radiation Therapy Oncology Group (RTOG) and about 93% (89/96) of the automatically generated plans would achieve the dosimetric constraints of the National Comprehensive Cancer Network (NCCN). The second study showed that the quality of treatment planning designed by junior dosimetrists was improved with the help of αDiar. Conclusions Our results showed that αDiar was an effective tool to improve planning quality. Over half of the patients’ plans could be designed automatically. For the remaining patients, although the automatically designed plans did not fully meet the clinical requirements, their quality was also better than that of manual plans.

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Usefulness of EZFluence software for radiotherapy planning of breast cancer treatment

Cited by 4 publications

References 10 publications

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

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

Barriers and facilitators to clinical implementation of radiotherapy treatment planning automation: A survey study of medical dosimetrists

Novel in-house knowledge-based automated planning system for lung cancer treated with intensity-modulated radiotherapy

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