Postoperative radiotherapy in women with early operable breast cancer (Scottish Breast Conservation Trial): 30-year update of a randomised, controlled, phase 3 trial.

BACKGROUND: Breast-conserving surgery, adjuvant systemic therapy, and radiotherapy are the standard of care for most women with early breast cancer. There are few reports of clinical outcomes beyond the first decade of follow-up of randomised trials comparing breast-conserving surgery with or without radiotherapy. We present a 30-year update of the Scottish Breast Conservation Trial. METHODS: In this randomised, controlled, phase 3 trial across 14 hospitals in Scotland, women aged younger than 70 years with early breast cancer (tumours ≤4 cm [T1 or T2 and N0 or N1]) were included. They underwent breast-conserving surgery (1 cm margin) with axillary node sampling or clearance. Oestrogen receptor (ER)-rich patients (≥20 fmol/mg protein) received 20 mg oral tamoxifen daily for 5 years. ER-poor patients (<20 fmol/mg protein) received chemotherapy (cyclophosphamide 600 mg/m2, methotrexate 50 mg/m2, and fluorouracil 600 mg/m2 every 21 days intravenously in eight courses). Stratification was by menstrual status (within or more than 12 months from last menstrual period) and ER status (oestrogen concentration ≥20 fmol/mg protein, <20 fmol/mg protein, or unknown) and patients were randomly assigned (1:1) to high-dose (50 Gy in 20-25 fractions) local or locoregional radiotherapy versus no radiotherapy. No blinding was possible due to the nature of the treatment. We report the primary endpoint of the original trial, ipsilateral breast tumour recurrence, and the co-primary endpoint, overall survival. Clinical outcomes were compared by the log-rank test. Hazard ratios (HRs) are reported, with no radiotherapy as the reference group. Failures of the proportional hazards assumption are reported if significant. All analyses are by intention to treat. FINDINGS: Between April 1, 1985, and Oct 2, 1991, 589 patients were enrolled and randomly assigned to the two treatment groups (293 to radiotherapy and 296 to no radiotherapy). After exclusion of four ineligible patients (two in each group), there were 291 patients in the radiotherapy group and 294 patients in the no radiotherapy group. Median follow-up was 17·5 years (IQR 8·4-27·9). Ipsilateral breast tumour recurrence was significantly lower in the radiotherapy group than in the no radiotherapy group (46 [16%] of 291 vs 107 [36%] of 294; HR 0·39 [95% CI 0·28-0·55], p<0·0001). Although there were differences in the hazard rate for ipsilateral breast tumour recurrence in the first decade after treatment (HR 0·24 [95% CI 0·15-0·38], p<0·0001), subsequent risks of ipsilateral breast tumour recurrence were similar in both groups (0·98 [0·54-1·79], p=0·95). There was no difference in overall survival between the two groups (median 18·7 years [95% CI 16·5-21·5] in the no radiotherapy group vs 19·2 years [16·9-21·3] in the radiotherapy group; HR 1·08 [95% CI 0·89-1 ·30], log-rank p=0·43). INTERPRETATION: Our findings suggest that patients whose biology predicts a late relapse a decade or more after breast-conserving surgery for early breast cancer might gain little from adjuvant radiotherapy. FUNDING: Breast Cancer Institute (part of Edinburgh and Lothian Health Foundation) and PFS Genomics (now part of Exact Sciences).

The Scottish Medical Imaging Archive: 57.3 Million Radiology Studies Linked to Their Medical Records.

Keywords: MRI, Imaging Sequences, Ultrasound, Mammography, CT, Angiography, Conventional Radiography Published under a CC BY 4.0 license. See also the commentary by Whitman and Vining in this issue.

A National Network of Safe Havens: Scottish Perspective.

For over a decade, Scotland has implemented and operationalized a system of Safe Havens, which provides secure analytics platforms for researchers to access linked, deidentified electronic health records (EHRs) while managing the risk of unauthorized reidentification. In this paper, a perspective is provided on the state-of-the-art Scottish Safe Haven network, including its evolution, to define the key activities required to scale the Scottish Safe Haven network's capability to facilitate research and health care improvement initiatives. A set of processes related to EHR data and their delivery in Scotland have been discussed. An interview with each Safe Haven was conducted to understand their services in detail, as well as their commonalities. The results show how Safe Havens in Scotland have protected privacy while facilitating the reuse of the EHR data. This study provides a common definition of a Safe Haven and promotes a consistent understanding among the Scottish Safe Haven network and the clinical and academic research community. We conclude by identifying areas where efficiencies across the network can be made to meet the needs of population-level studies at scale.

Are Communities Willing to Transition Into Learning Health Care Communities? A Community-Based Participatory Evaluation of Stakeholders' Receptivity.

This article aims to determine receptivity for advancing the Learning Healthcare System (LHS) model to a novel evidence-based health care delivery framework-Learning Health Care Community (LHCC)-in Baltimore, as a model for a national initiative. Using community-based participatory, qualitative approach, we conducted 16 in-depth interviews and 15 focus groups with 94 participants. Two independent coders thematically analyzed the transcripts. Participants included community members (38%), health care professionals (29%), patients (26%), and other stakeholders (7%). The majority considered LHCC to be a viable model for improving the health care experience, outlining certain parameters for success such as the inclusion of home visits, presentation of research evidence, and incorporation of social determinants and patients' input. Lessons learned and challenges discussed by participants can help health systems and communities explore the LHCC aspiration to align health care delivery with an engaged, empowered, and informed community.

An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population.

AIM: To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish national population. This includes more than 30 million different radiological examinations from a population of 5.4 million and >2 PB of data collected since 2010. METHODS: Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of those data, link it to other clinical or social datasets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. RESULTS: An extensive software platform has been developed to host, extract, and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects. CONCLUSIONS: The data available are from a range of radiological modalities and scanner types and were collected under different environmental conditions. These real-world, heterogenous data are valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using artificial intelligence and machine learning technologies, as well as enabling discovery science.

Use of text-mining methods to improve efficiency in the calculation of drug exposure to support pharmacoepidemiology studies.

Background: Efficient generation of structured dose instructions that enable researchers to calculate drug exposure is central to pharmacoepidemiology studies. Our aim was to design and test an algorithm to codify dose instructions, applied to the NHS Scotland Prescribing Information System (PIS) that records about 100 million prescriptions per annum. Methods: A natural language processing (NLP) algorithm was developed that enabled free-text dose instructions to be represented by three attributes - quantity, frequency and qualifier - specified by three, three and two variables, respectively. A sample of 15 593 distinct dose instructions was used to test, validate and refine the algorithm. The final algorithm used a zero-assumption approach and was then applied to the full dataset. Results: The initial algorithm generated structured output for 13 152 (84.34%) of the 15 593 sample dose instructions, and reviewers identified 767 (5.83%) incorrect translations, giving an accuracy of 94.17%. Following subsequent refinement of the algorithm rules, application to the full dataset of 458 227 687 prescriptions (99.67% had dose instructions represented by 4 964 083 distinct instructions) generated a structured output for 92.3% of dose instruction texts. This varied by therapeutic area (from 86.7% for the central nervous system to 96.8% for the cardiovascular system). Conclusions: We created an NLP algorithm, operational at scale, to produce structured output that gives data users maximum flexibility to formulate, test and apply their own assumptions according to the medicines under investigation. Text mining approaches can provide a solution to the safe and efficient management and provisioning of large volumes of data generated through our health systems.