There are estimated to be 3.4 million patients in the UK living after a diagnosis of cancer. We know very little about their quality of life or healthcare usage. Patient-reported outcome measures (PROMs) are tools which help to translate a patient’s quality of life into measurable categories, but how to do this at scale remains underexplored. The study employs a randomised design to assess different engagement strategies for optimising participation, data linkage and questionnaire completion in Northwest London and then nationally, with appropriate research approvals.
We have designed and implemented an online, patient-completed, randomised observational trial. We will pilot it in Northwest London before national roll-out, using initially the General Practice (GP) record of a cancer diagnosis and then exploring the use of social media. The primary objective is to explore the feasibility of recruiting participants via self-identification or contact from the primary care research network and obtaining consent to link participants’ PROMs responses to their cancer registry records. Data collection occurs through a secure platform, with participants directly responsible for data entry. There is no formal target sample size because this is a feasibility study, and we want to explore how many patients we can recruit. Analyses will be conducted using descriptive statistics, repeated measures multilevel modelling and machine learning techniques. If a substantial difference in responses between randomisation arms is detected, ineffective strategies will be removed. If no clear difference is observed, recruitment will continue with periodic reviews based on response rates and data completeness.
The Study Coordination Centre has obtained approval from the London—Surrey Research Ethics Committee and Health Research Authority. We will publish and disseminate the results in local, national and international meetings, in peer-reviewed journals, on social media and on websites.
It has been registered under ‘Investigating Digital Outcomes for Cancer Survivors in the Community’ (NCT06095024).
NCT06095024: Investigating Digital Outcomes for Cancer Survivors in the Community.
Stroke is the second leading cause of death worldwide, with the greatest burden in low- and middle-income countries (LMICs). Haemorrhagic stroke or spontaneous intracranial haemorrhage (sICH), including intraparenchymal haemorrhage (IPH) and subarachnoid haemorrhage (SAH), has the highest mortality and morbidity. Local management practices for haemorrhagic stroke vary greatly between geographical regions. The Planetary Outcomes after Intracranial Haemorrhage study aims to provide a global snapshot of the patient characteristics, processes of care and short-term outcomes of patients being treated for sICH across high- and low-income settings. It will also describe variation seen in care processes and available resources and time delays to receiving care. A greater understanding of the current state of sICH care is essential to identify possible interventions and targets for improved standards of care in all settings.
We describe a planned prospective, multicentre, international observational cohort study of patients admitted to hospital for management of sICH. We will include patients of all ages presenting to hospital with imaging evidence of sICH (IPH, intraventricular haemorrhage and/or SAH). The study will collect patient, care process and short-term outcome data, following patients for up to 30 days (or until discharge or death, whichever occurs first). Any centre globally where patients with sICH are admitted and managed can participate, targeting a sample size of 712 patients. The study will recruit centres worldwide through pre-existing research networks and by dissemination through neurosurgical and stroke conferences and courses. Each participating centre will complete a site questionnaire alongside patient data collection.
The study has received ethical approval by the University of Cambridge (PRE.2024.070). Participating centres will also confirm that they have undergone all necessary local governance procedures prior to starting local data collection. The findings will be disseminated via open access peer-reviewed journals, relevant conferences and other professional networks and lay channels, including the study website (https://plotich.org/) and social media channels (@plotichstudy).
by Dovile Zilenaite-Petrulaitiene, Allan Rasmusson, Ruta Barbora Valkiuniene, Aida Laurinaviciene, Linas Petkevicius, Arvydas Laurinavicius
IntroductionBreast cancer (BC) presents diverse malignancies with varying biological and clinical behaviors, driven by an interplay between cancer cells and tumor microenvironment. Deciphering these interactions is crucial for personalized diagnostics and treatment. This study explores the prognostic impact of tumor proliferation and immune response patterns, assessed by computational pathology indicators, on breast cancer-specific survival (BCSS) models in estrogen receptor-positive HER2-negative (ER+HER2–) and triple-negative BC (TNBC) patients.
Materials and methodsWhole-slide images of tumor surgical excision samples from 252 ER+HER2– patients and 63 TNBC patients stained for estrogen and progesterone receptors, Ki67, HER2, and CD8 were analyzed. Digital image analysis (DIA) was performed for tumor tissue segmentation and quantification of immunohistochemistry (IHC) markers; the DIA outputs were subsampled by hexagonal grids to assess the spatial distributions of Ki67-positive tumor cells and CD8-positive (CD8+) cell infiltrates, expressed as Ki67-entropy and CD8-immunogradient indicators, respectively. Prognostic models for BCSS were generated using multivariable Cox regression analysis, integrating clinicopathological and computational IHC indicators.
ResultsIn the ER+HER2– BC, multivariable Cox regression revealed that high CD8+ density within the tumor interface zone (IZ) (HR: 0.26, p = 0.0056), low immunodrop indicator of CD8+ density (HR: 2.93, p = 0.0051), and low Ki67-entropy (HR: 5.95, p = 0.0.0061) were independent predictors of better BCSS, while lymph node involvement predicted worse BCSS (HR: 3.30, p = 0.0013). In TNBC, increased CD8+ density in the IZ stroma (HR: 0.19, p = 0.0119) and Ki67-entropy (HR: 3.31, p = 0.0250) were independent predictors of worse BCSS. Combining these independent indicators enhanced prognostic stratification in both BC subtypes.
ConclusionsComputational biomarkers, representing spatial properties of the tumor proliferation and immune cell infiltrates, provided independent prognostic information beyond conventional IHC markers in BC. Integrating Ki67-entropy and CD8-immunogradient indicators into prognostic models can improve patient stratification with regard to BCSS.