View in EDS

Artificial Intelligence-Assisted Quantitative CT Analysis of Airway Changes Following SABR for Central Lung Tumors.

Saved in:
Bibliographic Details
Title: Artificial Intelligence-Assisted Quantitative CT Analysis of Airway Changes Following SABR for Central Lung Tumors.
Authors: Tekatli, H.1 (AUTHOR), Bohoudi, O.1 (AUTHOR), Hardcastle, N.2 (AUTHOR), Palacios, M.A.1 (AUTHOR), Schneiders, F.L.1 (AUTHOR), Bruynzeel, A.1 (AUTHOR), Siva, S.2 (AUTHOR), Senan, S.1 (AUTHOR)
Source: International Journal of Radiation Oncology, Biology, Physics. 2024 Supplement, Vol. 120 Issue 2, pS141-S142. 2p.
Subjects: Stereotactic radiotherapy, Computed tomography, Artificial intelligence, Lung tumors, Standard deviations
Abstract: Late pulmonary toxicity, including bronchial stenosis and fatal hemoptysis, can manifest in up to 35% of patients with central lung tumors treated using stereotactic ablative radiotherapy (SABR). Reliable tolerance doses for central airways are currently lacking. We postulated that objective and earlier identification of airway toxicity may be possible with an automated scoring method using quantitative CT and artificial intelligence (AI)-based airway segmentation to quantify post-SABR bronchial stenosis or occlusion. Patients treated with central lung SABR at two institutions were selected in an Ethics-approved study to define an internal reference dataset and an external validation dataset. Patients were eligible if they had pre- and post-SABR CT scans with ≤1mm slice thickness. Automated scoring was initiated by AI-based airway auto-segmentation using MEDPSeg, an end-to-end deep learning-based model (http://arxiv.org/abs/2312.02365). Next, the Vascular Modeling Toolkit in 3D Slicer (https://www.slicer.org) was used to extract a centerline curve through the auto-segmented airway lumen, and cross-sectional measurements were computed along each bronchial segment for all CT scans. Stenosis was significant if (100∗ (median radius-minimum radius) ÷median radius) = ≥50%. For the internal dataset, airways were evaluated by both visual assessment and automated scoring. Stenosis detected only by automated scoring were jointly reviewed by two investigators to reach consensus. Only the automated method was applied to the external dataset. A total of 26 and 33 patients were studied in the internal and external dataset, respectively. Visual scoring identified stenosis or occlusion in 8 patients from the internal set (31%), most frequently seen in the segmental bronchi. After airway auto-segmentation, minor manual edits were needed in 9% of patients. Time for segmenting a single scan averaged 83sec (range 73-136). Repeat measurements of airway diameters in the contralateral unirradiated lung revealed average relative differences (standard deviations) of 4.7% (± 6.3%), 5.2% (± 9.9%), and 4.9% (± 15.5%) respectively, for the main, lobar, and segmental bronchi. Automated scoring nearly doubled the detected cases of airway changes (n = 15, 58%), and allowed for earlier detection in 5/8 patients who had also visually scored changes, by a mean of 4 months. Estimated rates of airway damage were 48% and 66% at 1- and 2-years, respectively, for the internal set. The automated detection rate for airway damage was 52% in the external set, with 1- and 2-year risks of 56% and 61%, respectively. An AI-based automated scoring method detected more bronchial stenosis and/or occlusion after central lung SABR and did so at earlier time-points. Detection of early airway changes using this technique can allow for more reliable airway tolerance doses. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Radiation Oncology, Biology, Physics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 179875277
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Artificial Intelligence-Assisted Quantitative CT Analysis of Airway Changes Following SABR for Central Lung Tumors.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Tekatli%2C+H%2E%22">Tekatli, H.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bohoudi%2C+O%2E%22">Bohoudi, O.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hardcastle%2C+N%2E%22">Hardcastle, N.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Palacios%2C+M%2EA%2E%22">Palacios, M.A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Schneiders%2C+F%2EL%2E%22">Schneiders, F.L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bruynzeel%2C+A%2E%22">Bruynzeel, A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Siva%2C+S%2E%22">Siva, S.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Senan%2C+S%2E%22">Senan, S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Radiation+Oncology%2C+Biology%2C+Physics%22">International Journal of Radiation Oncology, Biology, Physics</searchLink>. 2024 Supplement, Vol. 120 Issue 2, pS141-S142. 2p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Stereotactic+radiotherapy%22">Stereotactic radiotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Lung+tumors%22">Lung tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Late pulmonary toxicity, including bronchial stenosis and fatal hemoptysis, can manifest in up to 35% of patients with central lung tumors treated using stereotactic ablative radiotherapy (SABR). Reliable tolerance doses for central airways are currently lacking. We postulated that objective and earlier identification of airway toxicity may be possible with an automated scoring method using quantitative CT and artificial intelligence (AI)-based airway segmentation to quantify post-SABR bronchial stenosis or occlusion. Patients treated with central lung SABR at two institutions were selected in an Ethics-approved study to define an internal reference dataset and an external validation dataset. Patients were eligible if they had pre- and post-SABR CT scans with ≤1mm slice thickness. Automated scoring was initiated by AI-based airway auto-segmentation using MEDPSeg, an end-to-end deep learning-based model (http://arxiv.org/abs/2312.02365). Next, the Vascular Modeling Toolkit in 3D Slicer (https://www.slicer.org) was used to extract a centerline curve through the auto-segmented airway lumen, and cross-sectional measurements were computed along each bronchial segment for all CT scans. Stenosis was significant if (100∗ (median radius-minimum radius) ÷median radius) = ≥50%. For the internal dataset, airways were evaluated by both visual assessment and automated scoring. Stenosis detected only by automated scoring were jointly reviewed by two investigators to reach consensus. Only the automated method was applied to the external dataset. A total of 26 and 33 patients were studied in the internal and external dataset, respectively. Visual scoring identified stenosis or occlusion in 8 patients from the internal set (31%), most frequently seen in the segmental bronchi. After airway auto-segmentation, minor manual edits were needed in 9% of patients. Time for segmenting a single scan averaged 83sec (range 73-136). Repeat measurements of airway diameters in the contralateral unirradiated lung revealed average relative differences (standard deviations) of 4.7% (± 6.3%), 5.2% (± 9.9%), and 4.9% (± 15.5%) respectively, for the main, lobar, and segmental bronchi. Automated scoring nearly doubled the detected cases of airway changes (n = 15, 58%), and allowed for earlier detection in 5/8 patients who had also visually scored changes, by a mean of 4 months. Estimated rates of airway damage were 48% and 66% at 1- and 2-years, respectively, for the internal set. The automated detection rate for airway damage was 52% in the external set, with 1- and 2-year risks of 56% and 61%, respectively. An AI-based automated scoring method detected more bronchial stenosis and/or occlusion after central lung SABR and did so at earlier time-points. Detection of early airway changes using this technique can allow for more reliable airway tolerance doses. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Radiation Oncology, Biology, Physics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=179875277
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.ijrobp.2024.07.257
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 2
        StartPage: S141
    Subjects:
      – SubjectFull: Stereotactic radiotherapy
        Type: general
      – SubjectFull: Computed tomography
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Lung tumors
        Type: general
      – SubjectFull: Standard deviations
        Type: general
    Titles:
      – TitleFull: Artificial Intelligence-Assisted Quantitative CT Analysis of Airway Changes Following SABR for Central Lung Tumors.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Tekatli, H.
      – PersonEntity:
          Name:
            NameFull: Bohoudi, O.
      – PersonEntity:
          Name:
            NameFull: Hardcastle, N.
      – PersonEntity:
          Name:
            NameFull: Palacios, M.A.
      – PersonEntity:
          Name:
            NameFull: Schneiders, F.L.
      – PersonEntity:
          Name:
            NameFull: Bruynzeel, A.
      – PersonEntity:
          Name:
            NameFull: Siva, S.
      – PersonEntity:
          Name:
            NameFull: Senan, S.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 02
              M: 10
              Text: 2024 Supplement
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 03603016
          Numbering:
            – Type: volume
              Value: 120
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: International Journal of Radiation Oncology, Biology, Physics
              Type: main
ResultId 1