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

被引:5
|
作者
Bustos, Luis Maduro A. [1 ,2 ]
Sarkar, Abhirup [1 ]
Doyle, Laura A. [1 ,2 ]
Andreou, Kelly [1 ]
Noonan, Jodie [1 ]
Nurbagandova, Diana [1 ]
Shah, SunJay A. [1 ]
Irabor, Omoruyi Credit [2 ]
Mourtada, Firas [2 ,3 ]
机构
[1] Christiana Care Helen F Graham Canc Ctr, Dept Radiat Oncol, Newark, DE USA
[2] Thomas Jefferson Univ Hosp, Dept Radiat Oncol, Philadelphia, PA USA
[3] Thomas Jefferson Univ Hosp, Dept Radiat Oncol, Sidney Kimmel Canc Ctr, 111 S 11th St Bodine G-321D, Philadelphia, PA 19107 USA
来源
关键词
artificial intelligence; contouring; convolutional neural networks; deep-learning; segmentation; AUTO-SEGMENTATION; RADIATION-THERAPY; TARGET VOLUMES; HEAD; DELINEATION; ORGANS; RISK; ONCOLOGY;
D O I
10.1002/acm2.14090
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
PurposeTo 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). MethodsComputed 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. ResultsThe average DSC obtained for the pelvic OARs ranged between (0.81 & PLUSMN; 0.06)(Rectum) and (0.94 & PLUSMN; 0.03)(Bladder). (0.75 & PLUSMN; 0.09)(Esophagus) to (0.96 & PLUSMN;0.02)Rt.Lung${( {0.96 \pm 0.02} )}_{{\mathrm{Rt}}.{\mathrm{\ Lung}}}$ for the thoracic OARs and (0.66 & PLUSMN; 0.07)(Lips) to (0.83 & PLUSMN; 0.04)(Brainstem) for the H & N. The average HDD in cm for the pelvis cohort ranged between (0.95 & PLUSMN; 0.35)(Bladder) to (3.62 & PLUSMN; 2.50)(Rectum), (0.42 & PLUSMN; 0.06)(SpinalCord) to (2.09 & PLUSMN; 2.00)(Esophagus) for the thoracic set and (0.53 & PLUSMN;0.22)Cerv_SpinalCord${( {0.53 \pm 0.22} )}_{{\mathrm{Cerv}}\_{\mathrm{SpinalCord}}}$ to (1.50 & PLUSMN; 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. ConclusionsThe 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 & GE;0.92. The poorest agreement was for the rectum, esophagus, and lips, with an overall DSC & LE;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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页数:10
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