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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.

To retrospectively compare semi-automated and manual volume measurements of malignant liver tumours and inter- and intra-observer variability using commercially available software.This study was performed on 60 consecutive patients with untreated liver metastases (30) and HCCs (30), i.e. 92 lesions (49 metastases, 43 HCCs) using hepatic MDCT. Lesion volumes were manually measured independently by two radiologists and semi-automatically by the same two radiologists and a technician. Those measurements were repeated on 20 patients (10 metastases and 10 HCCs) a week later. An independent operator timed all the measurements. Using the Spearman correlation coefficient and Bland-Altman plots, statistical analyses were performed.Liver lesion volumes obtained with semi-automated and manual methods were well correlated (Spearman, r = 0.98 and 0.91). Their agreement was high for intra-observer measurements with the semi-automated method (Spearman, r = 0.91 and 0.94). The agreement was lower for inter-observer measurements with both methods (Spearman, r = 0.87 for semi-automated and 0.91 for manual). The semi-automated method significantly reduced the post-processing duration (23s ± 19s vs. 33s ± 11s, p value0.0001).In our study, semi-automated volume analysis of malignant liver tumours correlated well with the manual method. Furthermore, the semi-automated volume analysis was significantly quicker.