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Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy

PURPOSE: To evaluate the clinical feasibility of the Siemens Healthineers AI‐Rad Companion Organs RT VA30A (Organs‐RT) auto‐contouring algorithm for organs at risk (OARs) of the pelvis, thorax, and head and neck (H&N). METHODS: Computed tomography (CT) datasets from 30 patients (10 pelvis, 10 th...

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Autores principales: Maduro Bustos, Luis A., Sarkar, Abhirup, Doyle, Laura A., Andreou, Kelly, Noonan, Jodie, Nurbagandova, Diana, Shah, SunJay A., Irabor, Omoruyi Credit, Mourtada, Firas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647981/
https://www.ncbi.nlm.nih.gov/pubmed/37464581
http://dx.doi.org/10.1002/acm2.14090
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author Maduro Bustos, Luis A.
Sarkar, Abhirup
Doyle, Laura A.
Andreou, Kelly
Noonan, Jodie
Nurbagandova, Diana
Shah, SunJay A.
Irabor, Omoruyi Credit
Mourtada, Firas
author_facet Maduro Bustos, Luis A.
Sarkar, Abhirup
Doyle, Laura A.
Andreou, Kelly
Noonan, Jodie
Nurbagandova, Diana
Shah, SunJay A.
Irabor, Omoruyi Credit
Mourtada, Firas
author_sort Maduro Bustos, Luis A.
collection PubMed
description PURPOSE: To evaluate the clinical feasibility of the Siemens Healthineers AI‐Rad Companion Organs RT VA30A (Organs‐RT) auto‐contouring algorithm for organs at risk (OARs) of the pelvis, thorax, and head and neck (H&N). METHODS: Computed tomography (CT) datasets from 30 patients (10 pelvis, 10 thorax, and 10 H&N) were collected. Four sets of OARs were generated on each scan, one set by Organs‐RT and the others by three experienced users independently. A physician (expert) then evaluated each contour by assigning a score from the following scale: 1‐Must Redo, 2‐Major Edits, 3‐Minor Edits, 4‐Clinically usable. Using the highest‐scored OAR from the human users as a reference, the contours generated by Organs‐RT were evaluated via Dice Similarity Coefficient (DSC), Hausdorff Distance (HDD), Mean Distance to Agreement (mDTA), Volume comparison, and visual inspection. Additionally, each human user recorded the time to delineate each structure set and time‐saving efficiency was measured. RESULTS: The average DSC obtained for the pelvic OARs ranged between (0.81 ± 0.06)(Rectum) and (0.94 ± 0.03)(Bladder). (0.75 ± 0.09)(Esophagus) to [Formula: see text] for the thoracic OARs and (0.66 ± 0.07)(Lips) to (0.83 ± 0.04)(Brainstem) for the H&N. The average HDD in cm for the pelvis cohort ranged between (0.95 ± 0.35)(Bladder) to (3.62 ± 2.50)(Rectum), (0.42 ± 0.06)(SpinalCord) to (2.09 ± 2.00)(Esophagus) for the thoracic set and [Formula: see text] to (1.50 ± 0.50)(Mandible) for the H&N region. The time‐saving efficiency was 67% for H&N, 83% for pelvis, and 84% for thorax. 72.5%, 82%, and 50% of the pelvis, thorax, and H&N OARs were scored as clinically usable by the expert, respectively. CONCLUSIONS: The highest agreement registered between OARs generated by Organs‐RT and their respective references was for the bladder, heart, lungs, and femoral heads, with an overall DSC≥0.92. The poorest agreement was for the rectum, esophagus, and lips, with an overall DSC⩽0.81. Nonetheless, Organs‐RT serves as a reliable auto‐contouring tool by minimizing overall contouring time and increasing time‐saving efficiency in radiotherapy treatment planning.
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spelling pubmed-106479812023-07-18 Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy Maduro Bustos, Luis A. Sarkar, Abhirup Doyle, Laura A. Andreou, Kelly Noonan, Jodie Nurbagandova, Diana Shah, SunJay A. Irabor, Omoruyi Credit Mourtada, Firas J Appl Clin Med Phys Radiation Oncology Physics PURPOSE: To evaluate the clinical feasibility of the Siemens Healthineers AI‐Rad Companion Organs RT VA30A (Organs‐RT) auto‐contouring algorithm for organs at risk (OARs) of the pelvis, thorax, and head and neck (H&N). METHODS: Computed tomography (CT) datasets from 30 patients (10 pelvis, 10 thorax, and 10 H&N) were collected. Four sets of OARs were generated on each scan, one set by Organs‐RT and the others by three experienced users independently. A physician (expert) then evaluated each contour by assigning a score from the following scale: 1‐Must Redo, 2‐Major Edits, 3‐Minor Edits, 4‐Clinically usable. Using the highest‐scored OAR from the human users as a reference, the contours generated by Organs‐RT were evaluated via Dice Similarity Coefficient (DSC), Hausdorff Distance (HDD), Mean Distance to Agreement (mDTA), Volume comparison, and visual inspection. Additionally, each human user recorded the time to delineate each structure set and time‐saving efficiency was measured. RESULTS: The average DSC obtained for the pelvic OARs ranged between (0.81 ± 0.06)(Rectum) and (0.94 ± 0.03)(Bladder). (0.75 ± 0.09)(Esophagus) to [Formula: see text] for the thoracic OARs and (0.66 ± 0.07)(Lips) to (0.83 ± 0.04)(Brainstem) for the H&N. The average HDD in cm for the pelvis cohort ranged between (0.95 ± 0.35)(Bladder) to (3.62 ± 2.50)(Rectum), (0.42 ± 0.06)(SpinalCord) to (2.09 ± 2.00)(Esophagus) for the thoracic set and [Formula: see text] to (1.50 ± 0.50)(Mandible) for the H&N region. The time‐saving efficiency was 67% for H&N, 83% for pelvis, and 84% for thorax. 72.5%, 82%, and 50% of the pelvis, thorax, and H&N OARs were scored as clinically usable by the expert, respectively. CONCLUSIONS: The highest agreement registered between OARs generated by Organs‐RT and their respective references was for the bladder, heart, lungs, and femoral heads, with an overall DSC≥0.92. The poorest agreement was for the rectum, esophagus, and lips, with an overall DSC⩽0.81. Nonetheless, Organs‐RT serves as a reliable auto‐contouring tool by minimizing overall contouring time and increasing time‐saving efficiency in radiotherapy treatment planning. John Wiley and Sons Inc. 2023-07-18 /pmc/articles/PMC10647981/ /pubmed/37464581 http://dx.doi.org/10.1002/acm2.14090 Text en © 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Radiation Oncology Physics
Maduro Bustos, Luis A.
Sarkar, Abhirup
Doyle, Laura A.
Andreou, Kelly
Noonan, Jodie
Nurbagandova, Diana
Shah, SunJay A.
Irabor, Omoruyi Credit
Mourtada, Firas
Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy
title Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy
title_full Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy
title_fullStr Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy
title_full_unstemmed Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy
title_short Feasibility evaluation of novel AI‐based deep‐learning contouring algorithm for radiotherapy
title_sort feasibility evaluation of novel ai‐based deep‐learning contouring algorithm for radiotherapy
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647981/
https://www.ncbi.nlm.nih.gov/pubmed/37464581
http://dx.doi.org/10.1002/acm2.14090
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