Conference proceeding
Development of a radiobiological evaluation tool to assess the expected clinical impacts of contouring accuracy between manual and semi-automated segmentation algorithms
Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), Vol.2017, pp.3409-3412
07/2017
DOI: 10.1109/EMBC.2017.8037588
PMID: 29060629
Abstract
RADEval is a tool developed to assess the expected clinical impact of contouring accuracy when comparing manual contouring and semi-automated segmentation. The RADEval tool, designed to process large scale datasets, imported a total of 2,760 segmentation datasets, along with a Simultaneous Truth and Performance Level Estimation (STAPLE) to act as ground truth tumor segmentations. Virtual dose-maps were created within RADEval and two different tumor control probability (TCP) values using a Logistic and a Poisson TCP models were calculated in RADEval using each STAPLE and each dose-map. RADEval also virtually generated a ring of normal tissue. To evaluate clinical impact, two different uncomplicated TCP (UTCP) values were calculated in RADEval by using two TCP-NTCP correlation parameters (δ = 0 and 1). NTCP values showed that semi-automatic segmentation resulted in lower NTCP with an average 1.5 - 1.6 % regardless of STAPLE design. This was true even though each normal tissue was created from each STAPLE (p <; 0.00001). TCP and UTCP presented no statistically significant differences (p ≥ 0.1884). The intra-operator standard deviations (SDs) for TCP, NTCP and UTCP were significantly lower for the semi-automatic segmentation method regardless of STAPLE design (p <; 0.0331). Both intra-and inter-operator SDs of TCP, NTCP and UTCP were significantly lower for semi-automatic segmentation for the STAPLE 1 design (p <;0.0331). RADEval was able to efficiently process 4,920 datasets of two STAPLE designs and successfully assess the expected clinical impact of contouring accuracy.
Details
- Title: Subtitle
- Development of a radiobiological evaluation tool to assess the expected clinical impacts of contouring accuracy between manual and semi-automated segmentation algorithms
- Creators
- Yusung KimKaustubh Anil PatwardhanReinhard R BeichelBrian J SmithChristopher MartKristin A PlichtaTangel ChangMilan SonkaMichael M GrahamVince MagnottaThomas CasavantJunyi Xia - University of Iowa, Radiation OncologyJohn M Buatti
- Resource Type
- Conference proceeding
- Publication Details
- Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), Vol.2017, pp.3409-3412
- DOI
- 10.1109/EMBC.2017.8037588
- PMID
- 29060629
- NLM abbreviation
- Conf Proc IEEE Eng Med Biol Soc
- eISBN
- 9781509028092; 1509028099
- ISSN
- 1557-170X
- eISSN
- 2694-0604
- Publisher
- United States
- Grant note
- P30 CA086862 / NCI NIH HHS U01 CA140206 / NCI NIH HHS
- Language
- English
- Date published
- 07/2017
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Psychiatry; Iowa Neuroscience Institute; Biostatistics; Radiation Oncology; Injury Prevention Research Center; Neurosurgery; Otolaryngology; Holden Comprehensive Cancer Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9983997304502771
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