Format

Send to

Choose Destination
Acad Radiol. 2016 Aug;23(8):940-52. doi: 10.1016/j.acra.2016.02.018. Epub 2016 May 20.

Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.

Author information

1
Definiens AG, Bernhard-Wicki Str 5, 80636 Munich, Germany. Electronic address: mathelogou@definiens.com.
2
UCLA, Center for Computer Vision and Imaging Biomarkers, Dept. of Radiological Sciences David Geffen School of Medicine at UCLA Dept. of Biostatistics Fielding School of Public at UCLA, Los Angeles, USA.
3
National Institute of Standards and Technology, Gaithersburg, USA.
4
Quantitative Health Sciences/JJN3, Cleveland Clinic Foundation, Cleveland, USA.
5
U.S. Food and Drug Administration, Silver Spring, Maryland.
6
Consultant QM/RA, Jena, Germany.
7
MEDIAN Technologies, Valbonne Sophia Antipolis, France.
8
Columbia University Medical Center, Department of Radiology, New York, USA.
9
Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany.
10
GE Healthcare, Buc, France.
11
Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.
12
ICON Medical Imaging, Warrington, Pennsylvania, USA.
13
INTIO, Inc., Broomfield, Colorado, USA.
14
NYU Langone Medical Center Faculty Practice Radiology, New York, USA.
15
Perceptive Informatics, Andhra Pradesh, India.
16
Siemens AG, Healthcare Sector, Computed Tomography, Forchheim, Germany.
17
Toshiba Corporation, Corporate R&D Center, Kawasaki, Japan.
18
Toshiba Corporation, Toshiba Medical Systems Corporation, Otawara, Japan.
19
Vital Images, Inc. (a Toshiba Medical Systems Group), Minnesota, USA.
20
Buckler Biomedical Associates LLC, Massachusetts, USA.

Abstract

RATIONALE AND OBJECTIVES:

Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA).

MATERIALS AND METHODS:

The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type.

RESULTS:

Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall.

CONCLUSION:

The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.

KEYWORDS:

CT volumetry; QIBA; algorithms; anthropomorphic phantoms; challenge; lung tumor

PMID:
27215408
PMCID:
PMC6237094
DOI:
10.1016/j.acra.2016.02.018
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center