Planning and reporting of quality-of-life outcomes in cancer trials.

BACKGROUND Information about the impact of cancer treatments on patients' quality of life (QoL) is of paramount importance to patients and treating oncologists. Cancer trials that do not specify QoL as an outcome or fail to report collected QoL data, omit crucial information for decision making. To estimate the magnitude of these problems, we investigated how frequently QoL outcomes were specified in protocols of cancer trials and subsequently reported. DESIGN Retrospective cohort study of RCT protocols approved by six research ethics committees in Switzerland, Germany, and Canada between 2000 and 2003. We compared protocols to corresponding publications, which were identified through literature searches and investigator surveys. RESULTS Of the 173 cancer trials, 90 (52%) specified QoL outcomes in their protocol, 2 (1%) as primary and 88 (51%) as secondary outcome. Of the 173 trials, 35 (20%) reported QoL outcomes in a corresponding publication (4 modified from the protocol), 18 (10%) were published but failed to report QoL outcomes in the primary or a secondary publication, and 37 (21%) were not published at all. Of the 83 (48%) trials that did not specify QoL outcomes in their protocol, none subsequently reported QoL outcomes. Failure to report pre-specified QoL outcomes was not associated with industry sponsorship (versus non-industry), sample size, and multicentre (versus single centre) status but possibly with trial discontinuation. CONCLUSIONS About half of cancer trials specified QoL outcomes in their protocols. However, only 20% reported any QoL data in associated publications. Highly relevant information for decision making is often unavailable to patients, oncologists, and health policymakers.

Ultra-low-dose dual-source CT coronary angiography with high pitch: diagnostic yield of a volumetric planning scan and effects on dose reduction and imaging strategy

OBJECTIVE To evaluate the role of an ultra-low-dose dual-source CT coronary angiography (CTCA) scan with high pitch for delimiting the range of the subsequent standard CTCA scan. METHODS 30 patients with an indication for CTCA were prospectively examined using a two-scan dual-source CTCA protocol (2.0 × 64.0 × 0.6 mm; pitch, 3.4; rotation time of 280 ms; 100 kV): Scan 1 was acquired with one-fifth of the tube current suggested by the automatic exposure control software [CareDose 4D™ (Siemens Healthcare, Erlangen, Germany) using 100 kV and 370 mAs as a reference] with the scan length from the tracheal bifurcation to the diaphragmatic border. Scan 2 was acquired with standard tube current extending with reduced scan length based on Scan 1. Nine central coronary artery segments were analysed qualitatively on both scans. RESULTS Scan 2 (105.1 ± 10.1 mm) was significantly shorter than Scan 1 (127.0 ± 8.7 mm). Image quality scores were significantly better for Scan 2. However, in 5 of 6 (83%) patients with stenotic coronary artery disease, a stenosis was already detected in Scan 1 and in 13 of 24 (54%) patients with non-stenotic coronary arteries, a stenosis was already excluded by Scan 1. Using Scan 2 as reference, the positive- and negative-predictive value of Scan 1 was 83% (5 of 6 patients) and 100% (13 of 13 patients), respectively. CONCLUSION An ultra-low-dose CTCA planning scan enables a reliable scan length reduction of the following standard CTCA scan and allows for correct diagnosis in a substantial proportion of patients. ADVANCES IN KNOWLEDGE Further dose reductions are possible owing to a change in the individual patient's imaging strategy as a prior ultra-low-dose CTCA scan may already rule out the presence of a stenosis or may lead to a direct transferal to an invasive catheter procedure.

Automatic quality control in clinical (1) H MRSI of brain cancer.

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1) H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin. Copyright © 2016 John Wiley & Sons, Ltd.