ObjectiveTo assess the reliability of ADC measurements in vitro and in cervical lymph nodes of healthy volunteers.MethodsWe used a GE 1.5 T MRI scanner and a first ice-water phantom according to recommendations released by the Quantitative Imaging Biomarker Alliance (QIBA) for assessing ADC against reference values. We analysed the target size effect by using a second phantom made of six inserted spheres with diameters ranging from 10 to 37 mm. Thirteen healthy volunteers were also scanned to assess the inter- and intra-observer reproducibility of volumetric ADC measurements of cervical lymph nodes.ResultsOn the ice-water phantom, the error in ADC measurements was less than 4.3 %. The spatial bias due to the non-linearity of gradient fields was found to be 24 % at 8 cm from the isocentre. ADC measure reliability decreased when addressing small targets due to partial volume effects (up to 12.8 %). The mean ADC value of cervical lymph nodes was 0.87.10-3 ± 0.12.10-3 mm2/s with a good intra-observer reliability. Inter-observer reproducibility featured a bias of -5.5 % due to segmentation issues.ConclusionADC is a potentially important imaging biomarker in oncology; however, variability issues preclude its broader adoption. Reliable use of ADC requires technical advances and systematic quality control.Key Points• ADC is a promising quantitative imaging biomarker.• ADC has a fair inter-reader variability and good intra-reader variability.• Partial volume effect, post-processing software and non-linearity of scanners are limiting factors.• No threshold values for detecting cervical lymph node malignancy can be drawn.Electronic supplementary materialThe online version of this article (10.1007/s00330-017-5265-2) contains supplementary material, which is available to authorized users.

BackgroundIn imaging-based clinical trials, it is common practice to perform double reads for each image, discrepant interpretations can result from these two different evaluations. In this study we analyzed discrepancies that occurred between local investigators (LI) and blinded independent central review (BICR) by comparing reader-selected imaging scans and lesions. Our goal was to identify the causes of discrepant declarations of progressive disease (PD) between LI and BICR in a clinical trial.MethodsWe retrospectively analyzed imaging data from a RECIST 1.1-based, multi-sites, phase II clinical trial of 179 patients with adult small cell lung cancer, treated with Cabazitaxel compared to Topotecan. Any discrepancies in the determination of PD between LI and BICR readers were reviewed by a third-party adjudicator. For each imaging time point and reader, we recorded the selected target lesions, non-target lesions, and new lesions. Odds ratios were calculated to measure the association between discrepant declarations of PD and the differences in reviewed imaging scans (e.g. same imaging modality but with different reconstruction parameters) and selected lesions. Reasons for discrepancies were analyzed.ResultsThe average number of target lesions found by LI and BICR was respectively 2.9 and 3.4 per patient (p < 0.05), 18.4% of these target lesions were actually non-measurable. LI and BICR performed their evaluations based on different baseline imaging scans for 59% of the patients, they selected at least one different target lesion in 85% of patients. A total of 36.7% of patients required adjudication. Reasons of adjudication included differences in 1) reporting new lesions (53.7%), 2) the measured change of the tumor burden (18.5%), and 3) the progression of non-target lesions (11.2%). The rate of discrepancy was not associated with the selection of non-measurable target lesions or with the readers’ assessment of different images. Paradoxically, more discrepancies occurred when LI and BICR selected exactly the same target lesions at baseline compared to when readers selected not exactly the same lesions.ConclusionsFor a large proportion of evaluations, LI and BICR did not select the same imaging scans and target lesions but with a limited impact on the rate of discrepancy. The majority of discrepancies were explained by the difference in detecting new lesions.Trial RegistrationARD12166 (https://clinicaltrials.gov/ct2/show/NCT01500720).

BackgroundTo evaluate the effectiveness and feasibility of high-intensity focused ultrasound (HIFU) for the treatment of bone metastases. MethodsA single-center prospective study was made involving 17 consecutive patients with symptomatic bone metastases. Patients were treated by Focused Ultrasound (FUs) performed with magnetic resonance (MR) guidance. Surgical treatment or radiotherapy treatment was not indicated for patients who underwent FUs. Lesions were located in the appendicular and axial skeleton and consisted of secondary symptomatic lesions. The clinical course of pain was evaluated using the Visual Analog Scale (VAS) before treatment, at 1 week, and at 1 month after treatment and the Oral Morphine Equivalent Daily Dose (OMEDD) was also recorded. We used Wilcoxon signed rank test to assess change in patient pain (R CRAN software V 3.1.1).ResultsWe observed a significant decrease in the pain felt by patients between pre- procedure and 1 week post-procedure (p = 2.9.10–4), and pre-procedure and 1 month post-procedure (p = 3.10–4). The proportion of responders according to the International Bone Metastases Consensus Working Party was: Partial Response 50% (8/16) and Complete Response 37.5% (6/16).ConclusionsHIFU under MR-guidance seems to be an effective and safe procedure in the treatment of symptomatic bone lesions for patients suffering from metastatic disease. A significant decrease of patient pain was observed.Trial registrationNCT01091883. Registered 24 March 2010. Level of evidence: Level 3.

Rationale and objectives Tumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semi-automated lung tumor volume measurement algorithms from clinical thoracic CT datasets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) CT Volumetry Profile. Materials and Methods Industry 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. Results Intra-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 determined in three partitions and 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 above 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 just in overall volume but in detail. Conclusions Nine of the twelve participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the current 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 above 10 mm. No partition of the algorithms were able to meet the QIBA requirements for interchangeability down to 10 mm, though the partition comprised of the best performing algorithms did meet this requirement above a tumor size of approximately 40 mm.

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.