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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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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. |
format | Online Article Text |
id | pubmed-10647981 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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