TY - JOUR
T1 - A real-time contouring feedback tool for consensus-based contour training
AU - Nelson, Christopher L.
AU - Nguyen, Callistus
AU - Fang, Raymond
AU - Court, Laurence E.
AU - Cardenas, Carlos E.
AU - Rhee, Dong Joo
AU - Netherton, Tucker J.
AU - Mumme, Raymond P.
AU - Gay, Skylar
AU - Gay, Casey
AU - Marquez, Barbara
AU - El Basha, Mohammad D.
AU - Zhao, Yao
AU - Gronberg, Mary
AU - Hernandez, Soleil
AU - Nealon, Kelly A.
AU - Martel, Mary K.
AU - Yang, Jinzhong
N1 - Publisher Copyright:
Copyright © 2023 Nelson, Nguyen, Fang, Court, Cardenas, Rhee, Netherton, Mumme, Gay, Gay, Marquez, El Basha, Zhao, Gronberg, Hernandez, Nealon, Martel and Yang.
PY - 2023
Y1 - 2023
N2 - Purpose: Variability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring. Methods: We developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group. Results: For every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was –0.8 mm ([–37.9, 34.9], 4.2) and 0.3 mm ([–25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to –0.1 mm ([–16.2, 7.3], 0.8) and 0.1 mm ([–6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were –1.5 mm ([–22.9, 19.9], 3.4) and –0.2 mm ([–4.5, 1.5], 0.7) for the heart and 1.8 mm ([–16.7, 34.5], 5.1) and 0.2 mm ([-3.9, 1.6],0.7) for the LV. Conclusions: A tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee’s contouring.
AB - Purpose: Variability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring. Methods: We developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group. Results: For every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was –0.8 mm ([–37.9, 34.9], 4.2) and 0.3 mm ([–25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to –0.1 mm ([–16.2, 7.3], 0.8) and 0.1 mm ([–6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were –1.5 mm ([–22.9, 19.9], 3.4) and –0.2 mm ([–4.5, 1.5], 0.7) for the heart and 1.8 mm ([–16.7, 34.5], 5.1) and 0.2 mm ([-3.9, 1.6],0.7) for the LV. Conclusions: A tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee’s contouring.
KW - consensus contouring
KW - contour training
KW - contour variability
KW - localized signed surface distance
KW - radiotherapy planning
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U2 - 10.3389/fonc.2023.1204323
DO - 10.3389/fonc.2023.1204323
M3 - Article
C2 - 37771435
AN - SCOPUS:85172200592
SN - 2234-943X
VL - 13
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 1204323
ER -