Breast Cancer Index is a predictive biomarker of treatment benefit and outcome from extended tamoxifen therapy: final analysis of the Trans-aTTom study

PURPOSE: The Breast Cancer Index (BCI) HOXB13/IL17BR (H/I) ratio predicts benefit from extended endocrine therapy in hormone receptor–positive (HR(+)) early-stage breast cancer. Here, we report the final analysis of the Trans-aTTom study examining BCI (H/I)'s predictive performance. EXPERIMENTAL DESIGN: BCI results were available for 2,445 aTTom trial patients. The primary endpoint of recurrence-free interval (RFI) and secondary endpoints of disease-free interval (DFI) and disease-free survival (DFS) were examined using Cox proportional hazards regression and log-rank test. RESULTS: Final analysis of the overall study population (N = 2,445) did not show a significant improvement in RFI with extended tamoxifen [HR, 0.90; 95% confidence interval (CI), 0.69–1.16; P = 0.401]. Both the overall study population and N0 group were underpowered due to the low event rate in the N0 group. In a pre-planned analysis of the N(+) subset (N = 789), BCI (H/I)-High patients derived significant benefit from extended tamoxifen (9.7% absolute benefit: HR, 0.33; 95% CI, 0.14–0.75; P = 0.016), whereas BCI (H/I)-Low patients did not (−1.2% absolute benefit; HR, 1.11; 95% CI, 0.76–1.64; P = 0.581). A significant treatment-to-biomarker interaction was demonstrated on the basis of RFI, DFI, and DFS (P = 0.037, 0.040, and 0.025, respectively). BCI (H/I)-High patients remained predictive of benefit from extended tamoxifen in the N(+)/HER2(−) subgroup (9.4% absolute benefit: HR, 0.35; 95% CI, 0.15–0.81; P = 0.047). A three-way interaction evaluating BCI (H/I), treatment, and HER2 status was not statistically significant (P = 0.849). CONCLUSIONS: Novel findings demonstrate that BCI (H/I) significantly predicts benefit from extended tamoxifen in HR(+) N(+) patients with HER2(−) disease. Moreover, BCI (H/I) demonstrates significant treatment to biomarker interaction across survival outcomes

Addressing overtreatment of screen detected DCIS; the LORIS trial

Abstract Overdiagnosis, and thus overtreatment, are inevitable consequences of most screening programmes; identification of ways of minimising the impact of overdiagnosis demands new prospective research, in particular the need to separate clinically relevant lesions that require active treatment from those that can be safely left alone or monitored and only need treated if they change characteristics. Breast cancer screening has led to a large increase in ductal carcinoma in situ (DCIS) diagnoses. This is a widely heterogeneous disease and most DCIS detected through screening is of high cytonuclear grade and therefore likely to be important clinically. However, the historic practice of surgical treatment for all DCIS is unlikely to be optimal for lower risk patients. A clearer understanding of how to manage DCIS is required. This article describes the background and development of ‘The low risk’ DCIS trial (LORIS), a phase III trial of surgery versus active monitoring. LORIS will determine if it is appropriate to manage women with screen detected or asymptomatic, low grade and intermediate grade DCIS with low grade features, by active monitoring rather than by surgical treatment

Quantifying the impact of avian influenza on the northern gannet colony of Bass Rock using ultra-high-resolution drone imagery and deep learning

Drones are an increasingly popular choice for wildlife surveys due to their versatility, quick response capabilities, and ability to access remote areas while covering large regions. A novel application presented here is to combine drone imagery with neural networks to assess mortality within a bird colony. Since 2021, Highly Pathogenic Avian Influenza (HPAI) has caused significant bird mortality in the UK, mainly affecting aquatic bird species. The world’s largest northern gannet colony on Scotland’s Bass Rock experienced substantial losses in 2022 due to the outbreak. To assess the impact, RGB imagery of Bass Rock was acquired in both 2022 and 2023 by deploying a drone over the island for the first time. A deep learning neural network was subsequently applied to the data to automatically detect and count live and dead gannets, providing population estimates for both years. The model was trained on the 2022 dataset and achieved a mean average precision (mAP) of 37%. Application of the model predicted 18,220 live and 3761 dead gannets for 2022, consistent with NatureScot’s manual count of 21,277 live and 5035 dead gannets. For 2023, the model predicted 48,455 live and 43 dead gannets, and the manual count carried out by the Scottish Seabird Centre and UK Centre for Ecology and Hydrology (UKCEH) of the same area gave 51,428 live and 23 dead gannets. This marks a promising start to the colony’s recovery with a population increase of 166% determined by the model. The results presented here are the first known application of deep learning to detect dead birds from drone imagery, showcasing the methodology’s swift and adaptable nature to not only provide ongoing monitoring of seabird colonies and other wildlife species but also to conduct mortality assessments. As such, it could prove to be a valuable tool for conservation purposes

Visualising harms in publications of randomised controlled trials: consensus and recommendations

OBJECTIVE: To improve communication of harm in publications of randomised controlled trials via the development of recommendations for visually presenting harm outcomes. DESIGN: Consensus study. SETTING: 15 clinical trials units registered with the UK Clinical Research Collaboration, an academic population health department, Roche Products, and The BMJ. PARTICIPANTS: Experts in clinical trials: 20 academic statisticians, one industry statistician, one academic health economist, one data graphics designer, and two clinicians. MAIN OUTCOME: measures A methodological review of statistical methods identified visualisations along with those recommended by consensus group members. Consensus on visual recommendations was achieved (at least 60% of the available votes) over a series of three meetings with participants. The participants reviewed and critically appraised candidate visualisations against an agreed framework and voted on whether to endorse each visualisation. Scores marginally below this threshold (50-60%) were revisited for further discussions and votes retaken until consensus was reached. RESULTS: 28 visualisations were considered, of which 10 are recommended for researchers to consider in publications of main research findings. The choice of visualisations to present will depend on outcome type (eg, binary, count, time-to-event, or continuous), and the scenario (eg, summarising multiple emerging events or one event of interest). A decision tree is presented to assist trialists in deciding which visualisations to use. Examples are provided of each endorsed visualisation, along with an example interpretation, potential limitations, and signposting to code for implementation across a range of standard statistical software. Clinician feedback was incorporated into the explanatory information provided in the recommendations to aid understanding and interpretation. CONCLUSIONS: Visualisations provide a powerful tool to communicate harms in clinical trials, offering an alternative perspective to the traditional frequency tables. Increasing the use of visualisations for harm outcomes in clinical trial manuscripts and reports will provide clearer presentation of information and enable more informative interpretations. The limitations of each visualisation are discussed and examples of where their use would be inappropriate are given. Although the decision tree aids the choice of visualisation, the statistician and clinical trial team must ultimately decide the most appropriate visualisations for their data and objectives. Trialists should continue to examine crude numbers alongside visualisations to fully understand harm profiles

Total fluid intake and the risk of recurrence in patients with non-muscle invasive bladder cancer::a prospective cohort study

Objectives: To investigate the role of fluid intake from beverages before and after a diagnosis of bladder cancer in relation to the risk of developing bladder cancer recurrence. Study Design: Prospective cohort study. Methods: 716 patients with non-muscle invasive bladder cancer (NMIBC), who received transurethral resection of a primary bladder tumour (TURBT) and completed self-administrated questionnaires on usual fluid intake from beverages at time of diagnosis (over the year before diagnosis) and during follow-up (over the year after diagnosis), were included. Multivariable Cox regression was used to calculate hazard ratios and 95% confidence intervals of developing recurrent bladder cancer in relation to the intake of total fluid, total alcohol, and individual beverages. Results: During 2,025 person-years of follow-up, 238 (33%) of the included 716 NMIBC patients developed one or more recurrences of bladder cancer. Total fluid intake before diagnosis was not associated with a first recurrence of bladder cancer when comparing the highest and lowest intake group (HR = 0.98, 95% C.I. 0.70-1.38, p = 0.91). Comparable results were obtained for total fluid intake pre-diagnosis and the risk of developing multiple recurrences of bladder cancer (HR = 1.01, 95% C. I. 0.87-1.19, p = 0.85). A total of 379 of the 716 patients reported on usual fluid intake within 1 year of diagnosis. No significant associations between total fluid intake 1 year after diagnosis and a first recurrence of bladder cancer were found when comparing the highest and lowest intake group (HR = 0.91; 95% C. I. 0.60-1.37, p = 0.65) or with multiple recurrences of bladder cancer (HR = 1.06; 95% C. I. 0.89-1.26, p = 0.54). In addition, total alcohol intake and individual beverages were not associated with bladder cancer recurrence. Conclusions: The results indicate that an individual's fluid intake from beverages is unlikely to have an important role in bladder cancer recurrence