PMID- 36110933
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231115
IS  - 2234-943X (Print)
IS  - 2234-943X (Electronic)
IS  - 2234-943X (Linking)
VI  - 12
DP  - 2022
TI  - Auto-segmentation for total marrow irradiation.
PG  - 970425
LID - 10.3389/fonc.2022.970425 [doi]
LID - 970425
AB  - PURPOSE: To evaluate the accuracy and efficiency of Artificial-Intelligence (AI) 
      segmentation in Total Marrow Irradiation (TMI) including contours throughout the 
      head and neck (H&N), thorax, abdomen, and pelvis. METHODS: An AI segmentation 
      software was clinically introduced for total body contouring in TMI including 27 
      organs at risk (OARs) and 4 planning target volumes (PTVs). This work compares 
      the clinically utilized contours to the AI-TMI contours for 21 patients. 
      Structure and image dicom data was used to generate comparisons including 
      volumetric, spatial, and dosimetric variations between the AI- and human-edited 
      contour sets. Conventional volume and surface measures including the 
      Sorensen-Dice coefficient (Dice) and the 95(th)% Hausdorff Distance (HD95) were 
      used, and novel efficiency metrics were introduced. The clinical efficiency gains 
      were estimated by the percentage of the AI-contour-surface within 1mm of the 
      clinical contour surface. An unedited AI-contour has an efficiency gain=100%, an 
      AI-contour with 70% of its surface<1mm from a clinical contour has an efficiency 
      gain of 70%. The dosimetric deviations were estimated from the clinical dose 
      distribution to compute the dose volume histogram (DVH) for all structures. 
      RESULTS: A total of 467 contours were compared in the 21 patients. In PTVs, 
      contour surfaces deviated by >1mm in 38.6% +/- 23.1% of structures, an average 
      efficiency gain of 61.4%. Deviations >5mm were detected in 12.0% +/- 21.3% of the 
      PTV contours. In OARs, deviations >1mm were detected in 24.4% +/- 27.1% of the 
      structure surfaces and >5mm in 7.2% +/- 18.0%; an average clinical efficiency gain 
      of 75.6%. In H&N OARs, efficiency gains ranged from 42% in optic chiasm to 100% 
      in eyes (unedited in all cases). In thorax, average efficiency gains were >80% in 
      spinal cord, heart, and both lungs. Efficiency gains ranged from 60-70% in 
      spleen, stomach, rectum, and bowel and 75-84% in liver, kidney, and bladder. DVH 
      differences exceeded 0.05 in 109/467 curves at any dose level. The most common 
      5%-DVH variations were in esophagus (86%), rectum (48%), and PTVs (22%). 
      CONCLUSIONS: AI auto-segmentation software offers a powerful solution for 
      enhanced efficiency in TMI treatment planning. Whole body segmentation including 
      PTVs and normal organs was successful based on spatial and dosimetric comparison.
CI  - Copyright (c) 2022 Watkins, Qing, Han, Hui and Liu.
FAU - Watkins, William Tyler
AU  - Watkins WT
AD  - Department of Radiation Oncology, City of Hope National Medical Center, Duarte, 
      CA, United States.
FAU - Qing, Kun
AU  - Qing K
AD  - Department of Radiation Oncology, City of Hope National Medical Center, Duarte, 
      CA, United States.
FAU - Han, Chunhui
AU  - Han C
AD  - Department of Radiation Oncology, City of Hope National Medical Center, Duarte, 
      CA, United States.
FAU - Hui, Susanta
AU  - Hui S
AD  - Department of Radiation Oncology, City of Hope National Medical Center, Duarte, 
      CA, United States.
FAU - Liu, An
AU  - Liu A
AD  - Department of Radiation Oncology, City of Hope National Medical Center, Duarte, 
      CA, United States.
LA  - eng
GR  - R01 CA154491/CA/NCI NIH HHS/United States
PT  - Journal Article
DEP - 20220830
PL  - Switzerland
TA  - Front Oncol
JT  - Frontiers in oncology
JID - 101568867
PMC - PMC9468379
OTO - NOTNLM
OT  - artificial intelligence
OT  - auto-contouring
OT  - auto-segmentation
OT  - total marrow irradiation
OT  - total marrow lymphoid irradiation
COIS- AL, KQ, CH, and TW have a research collaboration with Medical Mind, Inc. The 
      remaining author declares that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2022/09/17 06:00
MHDA- 2022/09/17 06:01
PMCR- 2022/01/01
CRDT- 2022/09/16 02:40
PHST- 2022/06/15 00:00 [received]
PHST- 2022/07/21 00:00 [accepted]
PHST- 2022/09/16 02:40 [entrez]
PHST- 2022/09/17 06:00 [pubmed]
PHST- 2022/09/17 06:01 [medline]
PHST- 2022/01/01 00:00 [pmc-release]
AID - 10.3389/fonc.2022.970425 [doi]
PST - epublish
SO  - Front Oncol. 2022 Aug 30;12:970425. doi: 10.3389/fonc.2022.970425. eCollection 
      2022.