ArticleAcademic radiology2015
Inter-Method Performance Study of Tumor Volumetry Assessment on Computed Tomography Test-Retest Data.
Article in Academic radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Reproducibility and repeatability of 18F-(2S, 4R)-4-fluoroglutamine PET imaging in preclinical oncology models.PloS one · 2025Article
- Annotated test-retest dataset of lung cancer CT scan images reconstructed at multiple imaging parameters.Scientific data · 2024Article
- Evaluation of Simulated Lesions as Surrogates to Clinical Lesions for Thoracic CT Volumetry: The Results of an International Challenge.Academic radiology · 2019Article
- Interchangeability between real and three-dimensional simulated lung tumors in computed tomography: an interalgorithm volumetry study.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Volumetric MRI Analysis of Plexiform Neurofibromas in Neurofibromatosis Type 1: Comparison of Two Methods.Academic radiology · 2018Article
- Quantitative assessment of nonsolid pulmonary nodule volume with computed tomography in a phantom study.Quantitative imaging in medicine and surgery · 2017Article
Corrections and comments
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Authors and funding
22 authors at 12 institutions in 5 countries.
Funding
Abstract
RATIONALE AND
objectivesTumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile. MATERIALS AND
methodsIndustry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers.
resultsIntra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail.
conclusionsNine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm.
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