Machine learning-based prediction of breast cancer growth rate in-vivo

BackgroundDetermining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen.MethodsA serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort.ResultsSM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours.ConclusionOur Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications

Enhancements in nocturnal surface ozone at urban sites in the UK

Analysis of diurnal patterns of surface ozone (O3) at multiple urban sites in the UK shows the occurrence of prominent nocturnal enhancements during the winter months (November–March). Whilst nocturnal surface ozone (NSO) enhancement events have been observed at other locations, this is the first time that such features have been demonstrated to occur in the UK and the second location globally. The observed NSO enhancement events in the UK were found to be so prevalent that they are clearly discernible in monthly diurnal cycles averaged over several years of data. Long-term (2000–2010) analysis of hourly surface ozone data from 18 urban background stations shows a bimodal diurnal variation during the winter months with a secondary nighttime peak around 0300 hours along with the primary daytime peak. For all but one site, the daily maxima NSO concentrations during the winter months exceeded 60 μg/m3 on >20 % of the nights. The highest NSO value recorded was 118 μg/m3. During the months of November, December, and January, the monthly averaged O3 concentrations observed at night (0300 h) even exceeded those observed in the daytime (1300 h). The analysis also shows that these NSO enhancements can last for several hours and were regional in scale, extending across several stations simultaneously. Interestingly, the urban sites in the north of the UK exhibited higher NSO than the sites in the south of the UK, despite their daily maxima being similar. In part, this seems to be related to the sites in the north typically having lower concentrations of nitrogen oxides