Objective: We sought to determine the usefulness of the 99mTc MIBI (MIBI) WASHOUT rate for the evaluation of steroid therapy in cardiac sarcoidosis (CS).Methods: Eleven CS patients underwent MIBI myocardial SPECT both before and 6 months after initiating steroid therapy. The WASHOUT rate (WOR) of MIBI was calculated using early and delayed polar map images. The WASHOUT score (WOS) of MIBI was derived from the difference between the early and delayed total defect scores (TDS).Results: Serum ACE and BNP exhibited significant improvement after the therapy (p=0.004, p=0.045). In the LV function, EDV and E/A ratio exhibited significant improvement after the therapy (p=0.041, p=0.007), while there were no significant differences between before and after therapy in EF or ESV. Early and delayed TDS showed no significant differences between before and after the therapy. In contrast, WOR differed significantly (p<.0001), while WOS did not differ significantly between before and after the therapy.Conclusion: The WASHOUT rate of MIBI is suitable for assessment of cardiac function in CS with steroid therapy, being especially better than the WASHOUT score of MIBI for assessment of disease activity of mild myocardial damage in CS with steroid therapy.

Introduction: 99mTc[Tc]-MIBI breast scintimammography offers a noninvasive method for assessing breast cancer biology. Studies suggest a potential prognostic value in WASHOUT analysis, as accelerated WASHOUT correlates with high histologic grade. The study examines whether the WASHOUT rate of 99mTc[Tc]-MIBI, a marker of mitochondrial dysfunction, correlates with the ki-67 index, a reliable predictive tool for breast cancer that estimates cellular proliferation.Methods: In this study, 30 cases of breast cancer who were intact, without previous breast operation, prior chemotherapy, and previous radiotherapy, had prone scintimammagraphy. The diagnosis was approved by biopsy. The standard 20 mCi 99mTc[Tc]-MIBI was applied to all patients, and after 15 min and 60 min of injection, two imaging series were taken at each time. After different methods of calculation of WASHOUT, the WASHOUT percentage has been correlated with the Ki-67 index in the patients.Results: All breast cancers showed significant tracer uptake at 15 minutes, mostly correlated with tumor angiogenesis. Imaging at 60 minutes revealed decreased activity, indicating a positive correlation between higher 99mTc[Tc]-MIBI WASHOUT and higher breast cancer grade as indicated by the Ki-67 index.Conclusion: The significant association between increased 99mTc[Tc]-MIBI WASHOUT and increased cancer grade indicates that WASHOUT analysis may have potential prognostic utility before or during surgical treatment. Additionally, there is a suggestion of increased tumor grade correlating with increased mitochondrial dysfunction. While further prospective validation is necessary, this pilot study demonstrates that WASHOUT kinetics could serve as a noninvasive imaging biomarker for breast cancer grade to supplement clinical management algorithms.

Background: 99mTc-sestamibimyocardial perfusion imaging (MIBI) WASHOUT is associated withmyocardial mitochondrial damage in patients with a successful percutaneous coronary intervention (PCI) following acutemyocardial infarction (AMI) andmaypredict the functional improvement of the left ventricle in follow-ups. Objectives: This study aimed to investigate the clinical efficacy of 99mTc-MIBI WASHOUT in patients with AMI by measuring the mean defect area based on 99mTc-MIBI myocardial perfusion-single photon emission computed tomography (MP-SPECT) rest imaging in early and delayed phases and comparing it with the defect area based on 99mTc-MIBI MP-SPECT adenosine stress imaging based on a two-day rest/stress protocol. Patients and Methods: This study was conducted on 29 consecutive patients with AMI (23 males and 6 females,mean age, 71, 8. 4 years), who underwent MP-SPECT using a standard two-day rest/stress protocol. The rest 99mTc-MIBI MP-SPECT images were acquired in the early phase at one hour after the injection of 99mTc-MIBI and in the delayed phase at three hours after the early phase. The total perfusion deficit (TPD) score for SPECT was measured to compare the defect area between the rest-early phase, rest-delayed phase, and post-stress imaging conditions. Results: Based on the results, the post-stress TPD score was significantly lower than the rest-delayed phase score (TPD: 22. 2% , 14. 3% vs. 27. 8% , 14. 0%,P < 0. 001). Also, the rest-early phase score was significantly lower than the rest-delayed phase score (TPD: 21. 5% ,14. 9% vs. 27. 8% , 14. 0%,P < 0. 001). However, no significant difference was observed between the post-stress score and the rest-early phase score. Conclusion: The combination of rest-early phase, delayed phase, and post-stress 99mTc-MIBI imaging using a two-day protocol after AMI reperfusion was a clinically useful method, which could identify residual ischemia and predict the left ventricular function improvement in the chronic phase of disease while reducing the exposure dose.

Introduction: A cross-over design is a repeated measurements design such that each experimental unit receives different treatments during different time periods. Lower order cross-over designs such as the two treatments, two periods and two sequences C (2, 2, 2) design have been discovered to be inefficient and erroneous in their analysis of treatments efficacy. In this regard, higher order cross-over designs have been recommended and developed like: the two treatments, three periods and four sequence C (2, 3, 4) design; and the two treatments, four periods and four sequence C (2, 4, 4) designs. However, there still exists more efficient higher order cross-over designs for two treatments which can be used in bioequivalence experiments. This study gives a new design and analysis for two treatments, five periods and four sequence C (2, 5, 4) cross-over design that gives more precise estimates and provides estimates for intra subject variability. Method: A hypothetical case study was considered on 160 experimental units which are assumed to be randomly selected from a given population. A cross over design of two treatments (A, B) in five periods whose sequences are given by BABAA, ABABB, BAABA and ABBAB were used. Each of the experimental units was used as its own control. The estimates for both direct treatments and treatments carry-over effects were obtained using best linear unbiased estimation method (BLUE). We simulated data for two treatments in five periods and four sequences and used it to test the null hypotheses of no significant differences in both the direct treatments and treatments carry-over effects using the 𝑡 − test. The subject profiles plots were used to determine the general trend so as to enable an experimenter make a decision on which of the two treatments under consideration was more efficacious. Results: In testing the null hypothesis of no significant difference in carry-over effects for the two treatments (A&B), the calculated value was found to be 0. 55 which was less than the tabular value at 156 degrees of freedom at 95 % confidence level, hence the null hypothesis was not rejected. Similarly, In testing the null hypothesis of no significant difference in treatment effects for A&B, the calculated value was found to be 11. 73 which was higher than the tabular value at 156 degrees of freedom at 95% confidence level hence the null hypothesis was rejected, and it was concluded that there was indeed a significant difference in the treatment effects. The mean subject profiles plots for a majority of periods and their respective sequences indicated that the general trend implied that treatment B was more effective as compared to treatment A. Conclusion: In cross-over designs, the presence carry-over effects affect the precision of treatments effects estimates in an experiment. Apart from increasing the WASHOUT periods, increasing the number of periods in cross-over designs can help in eliminating the carry-over effects. The C (2, 5, 4) design in this study gives more precise estimates and can provide estimates for intra subject variability. The simulated data indicated that there was significant difference in the treatment effects, and in comparison of the two treatments, treatment B was more effective as compared to treatment A.