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Background Breast cancer is the most common cancer in women worldwide, with a great diversity in outcomes among individual patients. The ability to accurately predict a breast cancer outcome is important to patients, physicians, researchers, and policy makers. Many models have been developed and tested in different settings. We systematically reviewed the prognostic models developed and/or validated for patients with breast cancer. Methods We conducted a systematic search in four electronic databases and some oncology websites, and a manual search in the bibliographies of the included studies. We identified original studies that were published prior to 1st January 2017, and presented the development and/or validation of models based mainly on clinico-pathological factors to predict mortality and/or recurrence in female breast cancer patients. Results From the 96 articles selected from 4095 citations found, we identified 58 models, which predicted mortality ( n  = 28), recurrence ( n  = 23), or both ( n  = 7). The most frequently used predictors were nodal status ( n  = 49), tumour size ( n  = 42), tumour grade ( n  = 29), age at diagnosis ( n  = 24), and oestrogen receptor status ( n  = 21). Models were developed in Europe ( n  = 25), Asia ( n  = 13), North America ( n  = 12), and Australia ( n  = 1) between 1982 and 2016. Models were validated in the development cohorts ( n  = 43) and/or independent populations ( n  = 17), by comparing the predicted outcomes with the observed outcomes ( n  = 55) and/or with the outcomes estimated by other models ( n  = 32), or the outcomes estimated by individual prognostic factors ( n  = 8). The most commonly used methods were: Cox proportional hazards regression for model development ( n  = 32); the absolute differences between the predicted and observed outcomes ( n  = 30) for calibration; and C-index/AUC ( n  = 44) for discrimination. Overall, the models performed well in the development cohorts but less accurately in some independent populations, particularly in patients with high risk and young and elderly patients. An exception is the Nottingham Prognostic Index, which retains its predicting ability in most independent populations. Conclusions Many prognostic models have been developed for breast cancer, but only a few have been validated widely in different settings. Importantly, their performance was suboptimal in independent populations, particularly in patients with high risk and in young and elderly patients.

Nottingham is both the birthplace and the global centre of the machine-made lace industry and is home to the Nationally Designated collection of machine-made lace and machinery. In 2017 Lace Unravelled was launched with the aim to conserve the collection and connect with local ‘industry mentors’ to learn more about its objects.  This led to wider collaborations and the exhibition ‘Lace Unarchived’, held at Nottingham Trent University intended to reveal the legacy of the art school, established in 1843, and its impact on the generations of lace designers it educated.  This paper proposes to explore the project’s achievements, what it revealed, and what it has meant for the interpretation and understanding of the collection, and wider understanding of ‘Nottingham Lace’.

\"This book concerns two men, a stockingmaker and a magistrate, who both lived in a small English village at the turn of the nineteenth century. It focuses on Joseph Woolley the stockingmaker, on his way of seeing and writing the world around him, and on the activities of magistrate Sir Gervase Clifton, administering justice from his country house Clifton Hall. Using Woolley's voluminous diaries and Clifton's magistrate records, Carolyn Steedman gives us a unique and fascinating account of working-class living and loving, and getting and spending. Through Woolley and his thoughts on reading and drinking, sex, the law and social relations, she challenges traditional accounts which she argues have overstated the importance of work to the working man's understanding of himself, as a creature of time, place and society. She shows instead that, for men like Woolley, law and fiction were just as critical as work in framing everyday life\"-- Provided by publisher.

This book concerns two men, a stockingmaker and a magistrate, who both lived in a small English village at the turn of the nineteenth century. It focuses on Joseph Woolley the stockingmaker, on his way of seeing and writing the world around him, and on the activities of magistrate Sir Gervase Clifton, administering justice from his country house Clifton Hall. Using Woolley's voluminous diaries and Clifton's magistrate records, Carolyn Steedman gives us a unique and fascinating account of working-class living and loving, and getting and spending. Through Woolley and his thoughts on reading and drinking, sex, the law and social relations, she challenges traditional accounts which she argues have overstated the importance of work to the working man's understanding of himself, as a creature of time, place and society. She shows instead that, for men like Woolley, law and fiction were just as critical as work in framing everyday life.

Objectives Histopathological tissue analysis by a pathologist determines the diagnosis and prognosis of most tumors, such as breast cancer. To estimate the aggressiveness of cancer, a pathologist evaluates the microscopic appearance of a biopsied tissue sample based on morphological features which have been correlated with patient outcome. Data description This paper introduces a dataset of 162 breast cancer histopathology images, namely the breast cancer histopathological annotation and diagnosis dataset (BreCaHAD) which allows researchers to optimize and evaluate the usefulness of their proposed methods. The dataset includes various malignant cases. The task associated with this dataset is to automatically classify histological structures in these hematoxylin and eosin (H&E) stained images into six classes, namely mitosis, apoptosis, tumor nuclei, non-tumor nuclei, tubule, and non-tubule. By providing this dataset to the biomedical imaging community, we hope to encourage researchers in computer vision, machine learning and medical fields to contribute and develop methods/tools for automatic detection and diagnosis of cancerous regions in breast cancer histology images.

Key summary points Aim To investigate functional and cognitive outcomes among patients with delirium in COVID-19. Findings Delirium in COVID-19 was prevalent (42%), but only a minority had been recognised by the clinical team. At 4-week follow-up, delirium was significantly associated with worse functional outcomes, independent of pre-morbid frailty. Cognitive outcomes were not appreciably worse. Message The presence of delirium is a significant factor in predicting worse functional outcomes in patients with COVID-19. Purpose To ascertain delirium prevalence and outcomes in COVID-19. Methods We conducted a point-prevalence study in a cohort of COVID-19 inpatients at University College Hospital. Delirium was defined by DSM-IV criteria. The primary outcome was all-cause mortality at 4 weeks; secondary outcomes were physical and cognitive function. Results In 71 patients (mean age 61, 75% men), 31 (42%) had delirium, of which only 12 (39%) had been recognised by the clinical team. At 4 weeks, 20 (28%) had died, 26 (36%) were interviewed by telephone and 21 (30%) remained as inpatients. Physical function was substantially worse in people after delirium − 50 out of 166 points (95% CI − 83 to − 17, p  = 0.01). Mean cognitive scores at follow-up were similar and delirium was not associated with mortality in this sample. Conclusions Our findings indicate that delirium is common, yet under-recognised. Delirium is associated with functional impairments in the medium term.

Background The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide. Traditional grading systems rely on subjective expert judgment and require extensive pathological expertise, are time-consuming, and often lead to inter-observer variability. Methods To address these limitations, we develop an AI-based model to predict Nottingham grade from whole-slide images of hematoxylin and eosin (H&E)-stained breast cancer tissue using a pathology foundation model. From TCGA database, we trained and evaluated using 521 H&E breast cancer slide images with available Nottingham scores through internal split validation, and further validated its clinical utility using an additional set of 597 cases without Nottingham scores. The model leveraged deep features extracted from a pathology foundation model (UNI) and incorporated 14 distinct multiple instance learning (MIL) algorithms. Results The best-performing model achieved an F1 score of 0.731 and a multiclass average AUC of 0.835. The top 300 genes correlated with model predictions were significantly enriched in pathways related to cell division and chromosome segregation, supporting the model’s biological relevance. The predicted grades demonstrated statistically significant association with 5-year overall survival ( p  < 0.05). Conclusion Our AI-based automated Nottingham grading system provides an efficient and reproducible tool for breast cancer assessment, offering potential for standardization of histologic grade in clinical practice.

Aims Clinicians use multi-gene/biomarker prognostic tests and free online tools to optimize treatment in early ER+/HER2− breast cancer. Here we report the comparison of recurrence risk predictions by CanAssist Breast (CAB), Nottingham Prognostic Index (NPI), and PREDICT along with the differences in the performance of these tests across Indian and European cohorts. Methods Current study used a retrospective cohort of 1474 patients from Europe, India, and USA. NPI risk groups were categorized into three prognostic groups, good (GPG-NPI index ≤ 3.4) moderate (MPG 3.41–5.4), and poor (PPG  > 5.4). Patients with chemotherapy benefit of < 2% were low-risk and ≥ 2% high-risk by PREDICT. We assessed the agreement between the CAB and NPI/PREDICT risk groups by kappa coefficient. Results Risk proportions generated by all tools were: CAB low:high 74:26; NPI good:moderate:poor prognostic group- 38:55:7; PREDICT low:high 63:37. Overall, there was a fair agreement between CAB and NPI[κ = 0.31(0.278–0.346)]/PREDICT [κ = 0.398 (0.35–0.446)], with a concordance of 97%/88% between CAB and NPI/PREDICT low-risk categories. 65% of NPI-MPG patients were called low-risk by CAB. From PREDICT high-risk patients CAB segregated 51% as low-risk, thus preventing over-treatment in these patients. In cohorts (European) with a higher number of T1N0 patients, NPI/PREDICT segregated more as LR compared to CAB, suggesting that T1N0 patients with aggressive biology are missed out by online tools but not by the CAB. Conclusion Data shows the use of CAB in early breast cancer overall and specifically in NPI-MPG and PREDICT high-risk patients for making accurate decisions on chemotherapy use. CAB provided unbiased risk stratification across cohorts of various geographies with minimal impact by clinical parameters.