Radiation Treatment Planning

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Tucker Netherton - One of the best experts on this subject based on the ideXlab platform.

  • generating high quality lymph node clinical target volumes for head and neck cancer Radiation therapy using a fully automated deep learning based approach
    International Journal of Radiation Oncology Biology Physics, 2021
    Co-Authors: Carlos E Cardenas, Tucker Netherton, Dong Joo Rhee, Beth M Beadle, Lifei Zhang, Jinzhong Yang, Adam S Garden, Heath D Skinner, R Mccarroll, L Court
    Abstract:

    Purpose To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated Radiation Treatment Planning workflow. Methods and Materials Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a Radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 Radiation oncologists as being “clinically acceptable without requiring edits,” “requiring minor edits,” or “requiring major edits.” Results When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, Conclusions We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated Radiation Treatment Planning.

  • the emergence of artificial intelligence within Radiation oncology Treatment Planning
    Oncology, 2021
    Co-Authors: Tucker Netherton, Carlos E Cardenas, Dong Joo Rhee, L Court, Beth M Beadle
    Abstract:

    Background The future of artificial intelligence (AI) heralds unprecedented change for the field of Radiation oncology. Commercial vendors and academic institutions have created AI tools for Radiation oncology, but such tools have not yet been widely adopted into clinical practice. In addition, numerous discussions have prompted careful thoughts about AI's impact upon the future landscape of Radiation oncology: How can we preserve innovation, creativity, and patient safety? When will AI-based tools be widely adopted into the clinic? Will the need for clinical staff be reduced? How will these devices and tools be developed and regulated? Summary In this work, we examine how deep learning, a rapidly emerging subset of AI, fits into the broader historical context of advancements made in Radiation oncology and medical physics. In addition, we examine a representative set of deep learning-based tools that are being made available for use in external beam radiotherapy Treatment Planning and how these deep learning-based tools and other AI-based tools will impact members of the Radiation Treatment Planning team. Key Messages: Compared to past transformative innovations explored in this article, such as the Monte Carlo method or intensity-modulated radiotherapy, the development and adoption of deep learning-based tools is occurring at faster rates and promises to transform practices of the Radiation Treatment Planning team. However, accessibility to these tools will be determined by each clinic's access to the internet, web-based solutions, or high-performance computing hardware. As seen by the trends exhibited by many technologies, high dependence on new technology can result in harm should the product fail in an unexpected manner, be misused by the operator, or if the mitigation to an expected failure is not adequate. Thus, the need for developers and researchers to rigorously validate deep learning-based tools, for users to understand how to operate tools appropriately, and for professional bodies to develop guidelines for their use and maintenance is essential. Given that members of the Radiation Treatment Planning team perform many tasks that are automatable, the use of deep learning-based tools, in combination with other automated Treatment Planning tools, may refocus tasks performed by the Treatment Planning team and may potentially reduce resource-related burdens for clinics with limited resources.

  • generating high quality lymph node clinical target volumes for head and neck cancer Radiation therapy using a fully automated deep learning based approach
    International Journal of Radiation Oncology Biology Physics, 2020
    Co-Authors: Carlos E Cardenas, Tucker Netherton, Dong Joo Rhee, Beth M Beadle, Lifei Zhang, Jinzhong Yang, Adam S Garden, Heath D Skinner, R Mccarroll, Laurence Edward Court
    Abstract:

    PURPOSE To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated Radiation Treatment Planning workflow. METHODS AND MATERIALS Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a Radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 Radiation oncologists as being "clinically acceptable without requiring edits," "requiring minor edits," or "requiring major edits." RESULTS When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, <2 minutes). CONCLUSIONS We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated Radiation Treatment Planning.

Beth M Beadle - One of the best experts on this subject based on the ideXlab platform.

  • generating high quality lymph node clinical target volumes for head and neck cancer Radiation therapy using a fully automated deep learning based approach
    International Journal of Radiation Oncology Biology Physics, 2021
    Co-Authors: Carlos E Cardenas, Tucker Netherton, Dong Joo Rhee, Beth M Beadle, Lifei Zhang, Jinzhong Yang, Adam S Garden, Heath D Skinner, R Mccarroll, L Court
    Abstract:

    Purpose To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated Radiation Treatment Planning workflow. Methods and Materials Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a Radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 Radiation oncologists as being “clinically acceptable without requiring edits,” “requiring minor edits,” or “requiring major edits.” Results When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, Conclusions We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated Radiation Treatment Planning.

  • the emergence of artificial intelligence within Radiation oncology Treatment Planning
    Oncology, 2021
    Co-Authors: Tucker Netherton, Carlos E Cardenas, Dong Joo Rhee, L Court, Beth M Beadle
    Abstract:

    Background The future of artificial intelligence (AI) heralds unprecedented change for the field of Radiation oncology. Commercial vendors and academic institutions have created AI tools for Radiation oncology, but such tools have not yet been widely adopted into clinical practice. In addition, numerous discussions have prompted careful thoughts about AI's impact upon the future landscape of Radiation oncology: How can we preserve innovation, creativity, and patient safety? When will AI-based tools be widely adopted into the clinic? Will the need for clinical staff be reduced? How will these devices and tools be developed and regulated? Summary In this work, we examine how deep learning, a rapidly emerging subset of AI, fits into the broader historical context of advancements made in Radiation oncology and medical physics. In addition, we examine a representative set of deep learning-based tools that are being made available for use in external beam radiotherapy Treatment Planning and how these deep learning-based tools and other AI-based tools will impact members of the Radiation Treatment Planning team. Key Messages: Compared to past transformative innovations explored in this article, such as the Monte Carlo method or intensity-modulated radiotherapy, the development and adoption of deep learning-based tools is occurring at faster rates and promises to transform practices of the Radiation Treatment Planning team. However, accessibility to these tools will be determined by each clinic's access to the internet, web-based solutions, or high-performance computing hardware. As seen by the trends exhibited by many technologies, high dependence on new technology can result in harm should the product fail in an unexpected manner, be misused by the operator, or if the mitigation to an expected failure is not adequate. Thus, the need for developers and researchers to rigorously validate deep learning-based tools, for users to understand how to operate tools appropriately, and for professional bodies to develop guidelines for their use and maintenance is essential. Given that members of the Radiation Treatment Planning team perform many tasks that are automatable, the use of deep learning-based tools, in combination with other automated Treatment Planning tools, may refocus tasks performed by the Treatment Planning team and may potentially reduce resource-related burdens for clinics with limited resources.

  • generating high quality lymph node clinical target volumes for head and neck cancer Radiation therapy using a fully automated deep learning based approach
    International Journal of Radiation Oncology Biology Physics, 2020
    Co-Authors: Carlos E Cardenas, Tucker Netherton, Dong Joo Rhee, Beth M Beadle, Lifei Zhang, Jinzhong Yang, Adam S Garden, Heath D Skinner, R Mccarroll, Laurence Edward Court
    Abstract:

    PURPOSE To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated Radiation Treatment Planning workflow. METHODS AND MATERIALS Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a Radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 Radiation oncologists as being "clinically acceptable without requiring edits," "requiring minor edits," or "requiring major edits." RESULTS When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, <2 minutes). CONCLUSIONS We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated Radiation Treatment Planning.

  • retrospective validation and clinical implementation of automated contouring of organs at risk in the head and neck a step toward automated Radiation Treatment Planning for low and middle income countries
    Journal of Global Oncology, 2018
    Co-Authors: Rachel E Mccarroll, Carlos E Cardenas, Beth M Beadle, Hester Burger, Sameera Dalvie, Peter A Balter, David S Followill, K Kisling, Michael Benedict A Mejia, Komeela Naidoo
    Abstract:

    Purpose We assessed automated contouring of normal structures for patients with head-and-neck cancer (HNC) using a multiatlas deformable-image-registration algorithm to better provide a fully automated Radiation Treatment Planning solution for low- and middle-income countries, provide quantitative analysis, and determine acceptability worldwide. Methods Autocontours of eight normal structures (brain, brainstem, cochleae, eyes, lungs, mandible, parotid glands, and spinal cord) from 128 patients with HNC were retrospectively scored by a dedicated HNC Radiation oncologist. Contours from a 10-patient subset were evaluated by five additional Radiation oncologists from international partner institutions, and interphysician variability was assessed. Quantitative agreement of autocontours with independently physician-drawn structures was assessed using the Dice similarity coefficient and mean surface and Hausdorff distances. Automated contouring was then implemented clinically and has been used for 166 patients, and contours were quantitatively compared with the physician-edited autocontours using the same metrics. Results Retrospectively, 87% of normal structure contours were rated as acceptable for use in dose-volume-histogram-based Planning without edit. Upon clinical implementation, 50% of contours were not edited for use in Treatment Planning. The mean (± standard deviation) Dice similarity coefficient of autocontours compared with physician-edited autocontours for parotid glands (0.92 ± 0.10), brainstem (0.95 ± 0.09), and spinal cord (0.92 ± 0.12) indicate that only minor edits were performed. The average mean surface and Hausdorff distances for all structures were less than 0.15 mm and 1.8 mm, respectively. Conclusion Automated contouring of normal structures generates reliable contours that require only minimal editing, as judged by retrospective ratings from multiple international centers and clinical integration. Autocontours are acceptable for Treatment Planning with no or, at most, minor edits, suggesting that automated contouring is feasible for clinical use and in the ongoing development of automated Radiation Treatment Planning algorithms.

Carlos E Cardenas - One of the best experts on this subject based on the ideXlab platform.

  • generating high quality lymph node clinical target volumes for head and neck cancer Radiation therapy using a fully automated deep learning based approach
    International Journal of Radiation Oncology Biology Physics, 2021
    Co-Authors: Carlos E Cardenas, Tucker Netherton, Dong Joo Rhee, Beth M Beadle, Lifei Zhang, Jinzhong Yang, Adam S Garden, Heath D Skinner, R Mccarroll, L Court
    Abstract:

    Purpose To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated Radiation Treatment Planning workflow. Methods and Materials Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a Radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 Radiation oncologists as being “clinically acceptable without requiring edits,” “requiring minor edits,” or “requiring major edits.” Results When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, Conclusions We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated Radiation Treatment Planning.

  • the emergence of artificial intelligence within Radiation oncology Treatment Planning
    Oncology, 2021
    Co-Authors: Tucker Netherton, Carlos E Cardenas, Dong Joo Rhee, L Court, Beth M Beadle
    Abstract:

    Background The future of artificial intelligence (AI) heralds unprecedented change for the field of Radiation oncology. Commercial vendors and academic institutions have created AI tools for Radiation oncology, but such tools have not yet been widely adopted into clinical practice. In addition, numerous discussions have prompted careful thoughts about AI's impact upon the future landscape of Radiation oncology: How can we preserve innovation, creativity, and patient safety? When will AI-based tools be widely adopted into the clinic? Will the need for clinical staff be reduced? How will these devices and tools be developed and regulated? Summary In this work, we examine how deep learning, a rapidly emerging subset of AI, fits into the broader historical context of advancements made in Radiation oncology and medical physics. In addition, we examine a representative set of deep learning-based tools that are being made available for use in external beam radiotherapy Treatment Planning and how these deep learning-based tools and other AI-based tools will impact members of the Radiation Treatment Planning team. Key Messages: Compared to past transformative innovations explored in this article, such as the Monte Carlo method or intensity-modulated radiotherapy, the development and adoption of deep learning-based tools is occurring at faster rates and promises to transform practices of the Radiation Treatment Planning team. However, accessibility to these tools will be determined by each clinic's access to the internet, web-based solutions, or high-performance computing hardware. As seen by the trends exhibited by many technologies, high dependence on new technology can result in harm should the product fail in an unexpected manner, be misused by the operator, or if the mitigation to an expected failure is not adequate. Thus, the need for developers and researchers to rigorously validate deep learning-based tools, for users to understand how to operate tools appropriately, and for professional bodies to develop guidelines for their use and maintenance is essential. Given that members of the Radiation Treatment Planning team perform many tasks that are automatable, the use of deep learning-based tools, in combination with other automated Treatment Planning tools, may refocus tasks performed by the Treatment Planning team and may potentially reduce resource-related burdens for clinics with limited resources.

  • generating high quality lymph node clinical target volumes for head and neck cancer Radiation therapy using a fully automated deep learning based approach
    International Journal of Radiation Oncology Biology Physics, 2020
    Co-Authors: Carlos E Cardenas, Tucker Netherton, Dong Joo Rhee, Beth M Beadle, Lifei Zhang, Jinzhong Yang, Adam S Garden, Heath D Skinner, R Mccarroll, Laurence Edward Court
    Abstract:

    PURPOSE To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated Radiation Treatment Planning workflow. METHODS AND MATERIALS Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a Radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 Radiation oncologists as being "clinically acceptable without requiring edits," "requiring minor edits," or "requiring major edits." RESULTS When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, <2 minutes). CONCLUSIONS We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated Radiation Treatment Planning.

  • retrospective validation and clinical implementation of automated contouring of organs at risk in the head and neck a step toward automated Radiation Treatment Planning for low and middle income countries
    Journal of Global Oncology, 2018
    Co-Authors: Rachel E Mccarroll, Carlos E Cardenas, Beth M Beadle, Hester Burger, Sameera Dalvie, Peter A Balter, David S Followill, K Kisling, Michael Benedict A Mejia, Komeela Naidoo
    Abstract:

    Purpose We assessed automated contouring of normal structures for patients with head-and-neck cancer (HNC) using a multiatlas deformable-image-registration algorithm to better provide a fully automated Radiation Treatment Planning solution for low- and middle-income countries, provide quantitative analysis, and determine acceptability worldwide. Methods Autocontours of eight normal structures (brain, brainstem, cochleae, eyes, lungs, mandible, parotid glands, and spinal cord) from 128 patients with HNC were retrospectively scored by a dedicated HNC Radiation oncologist. Contours from a 10-patient subset were evaluated by five additional Radiation oncologists from international partner institutions, and interphysician variability was assessed. Quantitative agreement of autocontours with independently physician-drawn structures was assessed using the Dice similarity coefficient and mean surface and Hausdorff distances. Automated contouring was then implemented clinically and has been used for 166 patients, and contours were quantitatively compared with the physician-edited autocontours using the same metrics. Results Retrospectively, 87% of normal structure contours were rated as acceptable for use in dose-volume-histogram-based Planning without edit. Upon clinical implementation, 50% of contours were not edited for use in Treatment Planning. The mean (± standard deviation) Dice similarity coefficient of autocontours compared with physician-edited autocontours for parotid glands (0.92 ± 0.10), brainstem (0.95 ± 0.09), and spinal cord (0.92 ± 0.12) indicate that only minor edits were performed. The average mean surface and Hausdorff distances for all structures were less than 0.15 mm and 1.8 mm, respectively. Conclusion Automated contouring of normal structures generates reliable contours that require only minimal editing, as judged by retrospective ratings from multiple international centers and clinical integration. Autocontours are acceptable for Treatment Planning with no or, at most, minor edits, suggesting that automated contouring is feasible for clinical use and in the ongoing development of automated Radiation Treatment Planning algorithms.

Jinzhong Yang - One of the best experts on this subject based on the ideXlab platform.

  • generating high quality lymph node clinical target volumes for head and neck cancer Radiation therapy using a fully automated deep learning based approach
    International Journal of Radiation Oncology Biology Physics, 2021
    Co-Authors: Carlos E Cardenas, Tucker Netherton, Dong Joo Rhee, Beth M Beadle, Lifei Zhang, Jinzhong Yang, Adam S Garden, Heath D Skinner, R Mccarroll, L Court
    Abstract:

    Purpose To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated Radiation Treatment Planning workflow. Methods and Materials Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a Radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 Radiation oncologists as being “clinically acceptable without requiring edits,” “requiring minor edits,” or “requiring major edits.” Results When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, Conclusions We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated Radiation Treatment Planning.

  • generating high quality lymph node clinical target volumes for head and neck cancer Radiation therapy using a fully automated deep learning based approach
    International Journal of Radiation Oncology Biology Physics, 2020
    Co-Authors: Carlos E Cardenas, Tucker Netherton, Dong Joo Rhee, Beth M Beadle, Lifei Zhang, Jinzhong Yang, Adam S Garden, Heath D Skinner, R Mccarroll, Laurence Edward Court
    Abstract:

    PURPOSE To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated Radiation Treatment Planning workflow. METHODS AND MATERIALS Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a Radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 Radiation oncologists as being "clinically acceptable without requiring edits," "requiring minor edits," or "requiring major edits." RESULTS When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, <2 minutes). CONCLUSIONS We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated Radiation Treatment Planning.

  • development and application of an elastic net logistic regression model to investigate the impact of cardiac substructure dose on Radiation induced pericardial effusion in patients with nsclc
    Acta Oncologica, 2020
    Co-Authors: Joshua S Niedzielski, X Wei, Daniel R Gomez, Zhongxing Liao, James A Bankson, Stephen Y Lai, Laurence Edward Court, Jinzhong Yang
    Abstract:

    Typically, cardiac substructures are neither delineated nor analyzed during Radiation Treatment Planning. Therefore, we developed a novel machine learning model to evaluate the impact of cardiac su...

  • autosegmentation for thoracic Radiation Treatment Planning a grand challenge at aapm 2017
    Medical Physics, 2018
    Co-Authors: Jinzhong Yang, Harini Veeraraghavan, Samuel G Armato, Keyvan Farahani, Justin Kirby, Jayashree Kalpathykramer, Wouter Van Elmpt, Andre Dekker, Xiao Han, Xue Feng
    Abstract:

    Purpose This report presents the methods and results of the Thoracic Auto-Segmentation Challenge organized at the 2017 Annual Meeting of American Association of Physicists in Medicine. The purpose of the challenge was to provide a benchmark dataset and platform for evaluating performance of autosegmentation methods of organs at risk (OARs) in thoracic CT images. Methods Sixty thoracic CT scans provided by three different institutions were separated into 36 training, 12 offline testing, and 12 online testing scans. Eleven participants completed the offline challenge, and seven completed the online challenge. The OARs were left and right lungs, heart, esophagus, and spinal cord. Clinical contours used for Treatment Planning were quality checked and edited to adhere to the RTOG 1106 contouring guidelines. Algorithms were evaluated using the Dice coefficient, Hausdorff distance, and mean surface distance. A consolidated score was computed by normalizing the metrics against interrater variability and averaging over all patients and structures. Results The interrater study revealed highest variability in Dice for the esophagus and spinal cord, and in surface distances for lungs and heart. Five out of seven algorithms that participated in the online challenge employed deep-learning methods. Although the top three participants using deep learning produced the best segmentation for all structures, there was no significant difference in the performance among them. The fourth place participant used a multi-atlas-based approach. The highest Dice scores were produced for lungs, with averages ranging from 0.95 to 0.98, while the lowest Dice scores were produced for esophagus, with a range of 0.55-0.72. Conclusion The results of the challenge showed that the lungs and heart can be segmented fairly accurately by various algorithms, while deep-learning methods performed better on the esophagus. Our dataset together with the manual contours for all training cases continues to be available publicly as an ongoing benchmarking resource.

Dong Joo Rhee - One of the best experts on this subject based on the ideXlab platform.

  • generating high quality lymph node clinical target volumes for head and neck cancer Radiation therapy using a fully automated deep learning based approach
    International Journal of Radiation Oncology Biology Physics, 2021
    Co-Authors: Carlos E Cardenas, Tucker Netherton, Dong Joo Rhee, Beth M Beadle, Lifei Zhang, Jinzhong Yang, Adam S Garden, Heath D Skinner, R Mccarroll, L Court
    Abstract:

    Purpose To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated Radiation Treatment Planning workflow. Methods and Materials Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a Radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 Radiation oncologists as being “clinically acceptable without requiring edits,” “requiring minor edits,” or “requiring major edits.” Results When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, Conclusions We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated Radiation Treatment Planning.

  • the emergence of artificial intelligence within Radiation oncology Treatment Planning
    Oncology, 2021
    Co-Authors: Tucker Netherton, Carlos E Cardenas, Dong Joo Rhee, L Court, Beth M Beadle
    Abstract:

    Background The future of artificial intelligence (AI) heralds unprecedented change for the field of Radiation oncology. Commercial vendors and academic institutions have created AI tools for Radiation oncology, but such tools have not yet been widely adopted into clinical practice. In addition, numerous discussions have prompted careful thoughts about AI's impact upon the future landscape of Radiation oncology: How can we preserve innovation, creativity, and patient safety? When will AI-based tools be widely adopted into the clinic? Will the need for clinical staff be reduced? How will these devices and tools be developed and regulated? Summary In this work, we examine how deep learning, a rapidly emerging subset of AI, fits into the broader historical context of advancements made in Radiation oncology and medical physics. In addition, we examine a representative set of deep learning-based tools that are being made available for use in external beam radiotherapy Treatment Planning and how these deep learning-based tools and other AI-based tools will impact members of the Radiation Treatment Planning team. Key Messages: Compared to past transformative innovations explored in this article, such as the Monte Carlo method or intensity-modulated radiotherapy, the development and adoption of deep learning-based tools is occurring at faster rates and promises to transform practices of the Radiation Treatment Planning team. However, accessibility to these tools will be determined by each clinic's access to the internet, web-based solutions, or high-performance computing hardware. As seen by the trends exhibited by many technologies, high dependence on new technology can result in harm should the product fail in an unexpected manner, be misused by the operator, or if the mitigation to an expected failure is not adequate. Thus, the need for developers and researchers to rigorously validate deep learning-based tools, for users to understand how to operate tools appropriately, and for professional bodies to develop guidelines for their use and maintenance is essential. Given that members of the Radiation Treatment Planning team perform many tasks that are automatable, the use of deep learning-based tools, in combination with other automated Treatment Planning tools, may refocus tasks performed by the Treatment Planning team and may potentially reduce resource-related burdens for clinics with limited resources.

  • generating high quality lymph node clinical target volumes for head and neck cancer Radiation therapy using a fully automated deep learning based approach
    International Journal of Radiation Oncology Biology Physics, 2020
    Co-Authors: Carlos E Cardenas, Tucker Netherton, Dong Joo Rhee, Beth M Beadle, Lifei Zhang, Jinzhong Yang, Adam S Garden, Heath D Skinner, R Mccarroll, Laurence Edward Court
    Abstract:

    PURPOSE To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated Radiation Treatment Planning workflow. METHODS AND MATERIALS Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a Radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 Radiation oncologists as being "clinically acceptable without requiring edits," "requiring minor edits," or "requiring major edits." RESULTS When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, <2 minutes). CONCLUSIONS We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated Radiation Treatment Planning.