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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.
Afiliação
  • Maduro Bustos LA; Department of Radiation Oncology, Christiana Care Helen F. Graham Cancer Center, Newark, Delaware, USA.
  • Sarkar A; Department of Radiation Oncology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA.
  • Doyle LA; Department of Radiation Oncology, Christiana Care Helen F. Graham Cancer Center, Newark, Delaware, USA.
  • Andreou K; Department of Radiation Oncology, Christiana Care Helen F. Graham Cancer Center, Newark, Delaware, USA.
  • Noonan J; Department of Radiation Oncology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA.
  • Nurbagandova D; Department of Radiation Oncology, Christiana Care Helen F. Graham Cancer Center, Newark, Delaware, USA.
  • Shah SA; Department of Radiation Oncology, Christiana Care Helen F. Graham Cancer Center, Newark, Delaware, USA.
  • Irabor OC; Department of Radiation Oncology, Christiana Care Helen F. Graham Cancer Center, Newark, Delaware, USA.
  • Mourtada F; Department of Radiation Oncology, Christiana Care Helen F. Graham Cancer Center, Newark, Delaware, USA.
J Appl Clin Med Phys ; 24(11): e14090, 2023 Nov.
Article em En | MEDLINE | ID: mdl-37464581
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 ( 0.96 ± 0.02 ) Rt . Lung ${( {0.96 \pm 0.02} )}_{{\mathrm{Rt}}.{\mathrm{\ Lung}}}$ 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 ( 0.53 ± 0.22 ) Cerv _ SpinalCord ${( {0.53 \pm 0.22} )}_{{\mathrm{Cerv}}\_{\mathrm{SpinalCord}}}$ 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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Appl Clin Med Phys Assunto da revista: BIOFISICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Appl Clin Med Phys Assunto da revista: BIOFISICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos