The role of chemotherapeutic drugs in the evaluation of breast tumour response to chemotherapy using serial FDG-PET

INTRODUCTION: The aims of this study were to investigate whether drug sequence (docetaxel followed by anthracyclines or the drugs in reverse order) affects changes in the maximal standard uptake volume (SUVmax) on [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) during neoadjuvant chemotherapy in women with locally advanced breast cancer. METHODS: Women were randomly assigned to receive either drug sequence, and FDG-PET scans were taken at baseline, after four cycles and after eight cycles of chemotherapy. Tumour response to chemotherapy was evaluated based on histology from a surgical specimen collected upon completion of chemotherapy. RESULTS: Sixty women were enrolled into the study. Thirty-one received docetaxel followed by anthracyclines (Arm A) and 29 received drugs in the reverse order (Arm B). Most women (83%) had ductal carcinoma and 10 women (17%) had lobular or lobular/ductal carcinoma. All but one tumour were downstaged during therapy. Overall, there was no significant difference in response between the two drug regimens. However, women in Arm B who achieved complete pathological response had mean FDG-PET SUVmax reduction of 87.7% after four cycles, in contrast to those who had no or minor pathological response. These women recorded mean SUVmax reductions of only 27% (P < 0.01). Women in Arm A showed no significant difference in SUVmax response according to pathological response. Sensitivity, specificity, accuracy and positive and negative predictive values were highest in women in Arm B. CONCLUSIONS: Our results show that SUVmax uptake by breast tumours during chemotherapy can be dependent on the drugs used. Care must be taken when interpreting FDG-PET in settings where patients receive varied drug protocols

A 'small-world-like' model for comparing interventions aimed at preventing and controlling influenza pandemics

BACKGROUND: With an influenza pandemic seemingly imminent, we constructed a model simulating the spread of influenza within the community, in order to test the impact of various interventions. METHODS: The model includes an individual level, in which the risk of influenza virus infection and the dynamics of viral shedding are simulated according to age, treatment, and vaccination status; and a community level, in which meetings between individuals are simulated on randomly generated graphs. We used data on real pandemics to calibrate some parameters of the model. The reference scenario assumes no vaccination, no use of antiviral drugs, and no preexisting herd immunity. We explored the impact of interventions such as vaccination, treatment/prophylaxis with neuraminidase inhibitors, quarantine, and closure of schools or workplaces. RESULTS: In the reference scenario, 57% of realizations lead to an explosive outbreak, lasting a mean of 82 days (standard deviation (SD) 12 days) and affecting 46.8% of the population on average. Interventions aimed at reducing the number of meetings, combined with measures reducing individual transmissibility, would be partly effective: coverage of 70% of affected households, with treatment of the index patient, prophylaxis of household contacts, and confinement to home of all household members, would reduce the probability of an outbreak by 52%, and the remaining outbreaks would be limited to 17% of the population (range 0.8%–25%). Reactive vaccination of 70% of the susceptible population would significantly reduce the frequency, size, and mean duration of outbreaks, but the benefit would depend markedly on the interval between identification of the first case and the beginning of mass vaccination. The epidemic would affect 4% of the population if vaccination started immediately, 17% if there was a 14-day delay, and 36% if there was a 28-day delay. Closing schools when the number of infections in the community exceeded 50 would be very effective, limiting the size of outbreaks to 10% of the population (range 0.9%–22%). CONCLUSION: This flexible tool can help to determine the interventions most likely to contain an influenza pandemic. These results support the stockpiling of antiviral drugs and accelerated vaccine development