A Novel Polymer-Encapsulated Multi-Imaging Modality Fiducial Marker with Positive Signal Contrast for Image-Guided Radiation Therapy

BACKGROUND

Current fiducial markers (FMs) in external-beam radiotherapy (EBRT) for prostate cancer (PCa) cannot be positively visualized on magnetic resonance imaging (MRI) and create dose perturbation and significant imaging artifacts on computed tomography (CT) and MRI. We report our initial experience with clinical imaging of a novel multimodality FM, NOVA.

METHODS

We tested Gold Anchor [G-FM], BiomarC [carbon, C-FM], and NOVA FMs in phantoms imaged with kilovoltage (kV) X-rays, transrectal ultrasound (TRUS), CT, and MRI. Artifacts of the FMs on CT were quantified by the relative streak artifacts level (rSAL) metric. Proton dose perturbations (PDPs) were measured with Gafchromic EBT3 film, with FMs oriented either perpendicular to or parallel with the beam axis. We also tested the performance of NOVA-FMs in a patient.

RESULTS

NOVA-FMs were positively visualized on all 4 imaging modalities tested. The rSAL on CT was 0.750 ± 0.335 for 2-mm reconstructed slices. In F-tests, PDP was associated with marker type and depth of measurement (p < 10-6); at 5-mm depth, PDP was significantly greater for the G-FM (12.9%, p = 10-6) and C-FM (6.0%, p = 0.011) than NOVA (4.5%). EBRT planning with MRI/CT image co-registration and daily alignments using NOVA-FMs in a patient was feasible and reproducible.

CONCLUSIONS

NOVA-FMs were positively visible and produced less PDP than G-FMs or C-FMs. NOVA-FMs facilitated MRI/CT fusion and identification of regions of interest.

Analgesic effect of oral paracetamol 1000 mg/ibuprofen 400 mg, paracetamol 1000 mg/codeine 60 mg, paracetamol 1000 mg/ibuprofen 400 mg/codeine 60 mg, or placebo on acute postoperative pain: a single-dose, randomized, and double-blind study

PURPOSE

Combining analgesics with different mechanisms of action may increase the analgesic efficacy. The multidimensional pharmacodynamic profiles of ibuprofen 400 mg/paracetamol 1000 mg, ibuprofen 400 mg/paracetamol 1000 mg/codeine 60 mg, and paracetamol 1000 mg/codeine 60 mg and placebo were compared.

METHODS

A randomized, double-blind, placebo-controlled, parallel-group, single-centre, outpatient, and single-dose study used 200 patients of both sexes and homogenous ethnicity after third molar surgery (mean age 24 years, range 19-30 years). Primary outcome was sum pain intensity over 6 h (SPI). Secondary outcomes were time to analgesic onset, duration of analgesia, time to rescue drug intake, number of patients taking rescue drug, sum pain intensity difference (SPID), maximum pain intensity difference, time to maximum pain intensity difference, number needed to treat, prevent remedication and harm values, adverse effects, and patient-reported outcome measure (PROM).

RESULTS

Analgesia following ibuprofen and paracetamol combination with or without codeine was comparable. Both were better than paracetamol combined with codeine. Secondary variables supported this finding. Post hoc analysis of SPI and SPID revealed a sex/drug interaction trend in the codeine-containing groups where females experienced less analgesia. PROM showed a significant sex/drug interaction in the paracetamol and codeine group, but not in the other codeine-containing group. Especially females reported known and mild side effects in the codeine-containing groups.

CONCLUSION

Codeine added to ibuprofen/paracetamol does not seem to add analgesia in a sex-mixed study population. Sex may be a confounding factor when testing weak opioid analgesics such as codeine. PROM seems to be more sensitive than traditional outcome measures.

TRIAL REGISTRATION

ClinicalTrials.gov June 2009 NCT00921700.

Hypothesis Tests for Neyman's Bias in Case-Control Studies

Survival bias is a long-recognized problem in case-control studies, and many varieties of bias can come under this umbrella term. We focus on one of them, termed Neyman's bias or "prevalence-incidence bias." It occurs in case-control studies when exposure affects both disease and disease-induced mortality, and we give a formula for the observed, biased odds ratio under such conditions. We compare our result with previous investigations into this phenomenon and consider models under which this bias may or may not be important. Finally, we propose three hypothesis tests to identify when Neyman's bias may be present in case-control studies. We apply these tests to three data sets, one of stroke mortality, another of brain tumors, and the last of atrial fibrillation, and find some evidence of Neyman's bias in the former two cases, but not the last case.