81 results found
Pati S, Baid U, Edwards B, et al., 2023, Author Correction: Federated learning enables big data for rare cancer boundary detection., Nature Communications, Vol: 14, Pages: 436-436, ISSN: 2041-1723
Muirhead R, Dean C, Diez P, et al., 2023, Launch of the UK SABR Consortium Pelvic Stereotactic Ablative Radiotherapy Re-irradiation Guidelines and National Audit, CLINICAL ONCOLOGY, Vol: 35, Pages: 29-32, ISSN: 0936-6555
Dumba M, Fry A, Shelton J, et al., 2022, Imaging in patients with glioblastoma: A national cohort study, Neuro-Oncology Practice, Vol: 9, Pages: 487-495, ISSN: 2054-2585
BackgroundGlioblastoma is the most common malignant brain tumor in adults and has a poor prognosis. This cohort of patients is diverse and imaging is vital to formulate treatment plans. Despite this, there is relatively little data on patterns of use of imaging and imaging workload in routine practice.MethodsWe examined imaging patterns for all patients aged 15–99 years resident in England who were diagnosed with a glioblastoma between 1st January 2013 and 31st December 2014. Patients without imaging and death-certificate-only registrations were excluded.ResultsThe analytical cohort contained 4,307 patients. There was no significant variation in pre- or postdiagnostic imaging practice by sex or deprivation quintile. Postdiagnostic imaging practice was varied. In the group of patients who were treated most aggressively (surgical debulking and chemoradiation) and were MRI compatible, only 51% had a postoperative MRI within 72 hours of surgery. In patients undergoing surgery who subsequently received radiotherapy, only 61% had a postsurgery and preradiotherapy MRI.ConclusionsPrediagnostic imaging practice is uniform. Postdiagnostic imaging practice was variable. With increasing evidence and clearer recommendations regarding debulking surgery and planning radiotherapy imaging, the reason for this is unclear and will form the basis of further work.
Zhang K, Toni F, Williams M, 2022, A federated cox model with non-proportional hazards, The 6th International Workshop on Health Intelligence, Publisher: Springer, ISSN: 1860-949X
Recent research has shown the potential for neural networksto improve upon classical survival models such as the Cox model, whichis widely used in clinical practice. Neural networks, however, typicallyrely on data that are centrally available, whereas healthcare data arefrequently held in secure silos. We present a federated Cox model thataccommodates this data setting and also relaxes the proportional hazardsassumption, allowing time-varying covariate effects. In this latter respect,our model does not require explicit specification of the time-varying ef-fects, reducing upfront organisational costs compared to previous works.We experiment with publicly available clinical datasets and demonstratethat the federated model is able to perform as well as a standard model.
Plaha P, Camp S, Cook J, et al., 2022, FUTURE-GB: functional and ultrasound-guided resection of glioblastoma - a two-stage randomised control trial., BMJ Open, Vol: 12, Pages: 1-10, ISSN: 2044-6055
INTRODUCTION: Surgery remains the mainstay for treatment of primary glioblastoma, followed by radiotherapy and chemotherapy. Current standard of care during surgery involves the intraoperative use of image-guidance and 5-aminolevulinic acid (5-ALA). There are multiple other surgical adjuncts available to the neuro-oncology surgeon. However, access to, and usage of these varies widely in UK practice, with limited evidence of their use. The aim of this trial is to investigate whether the addition of diffusion tensor imaging (DTI) and intraoperative ultrasound (iUS) to the standard of care surgery (intraoperative neuronavigation and 5-ALA) impacts on deterioration free survival (DFS). METHODS AND ANALYSIS: This is a two-stage, randomised control trial (RCT) consisting of an initial non-randomised cohort study based on the principles of the IDEAL (Idea, Development, Exploration, Assessment and Long-term follow-up) stage-IIb format, followed by a statistically powered randomised trial comparing the addition of DTI and iUS to the standard of care surgery. A total of 357 patients will be recruited for the RCT. The primary outcome is DFS, defined as the time to either 10-point deterioration in health-related quality of life scores from baseline, without subsequent reversal, progressive disease or death. ETHICS AND DISSEMINATION: The trial was registered in the Integrated Research Application System (Ref: 264482) and approved by a UK research and ethics committee (Ref: 20/LO/0840). Results will be published in a peer-reviewed journal. Further dissemination to participants, patient groups and the wider medical community will use a range of approaches to maximise impact. TRIAL REGISTRATION NUMBER: ISRCTN38834571.
Islam S, Inglese M, Aravind P, et al., 2022, 18F-Fluoropivalate PET/MRI: imaging of treatment naive patients and patients treated with radiosurgery, 34th EORTC-NCI-AACR Symposium on Molecular Targets and Cancer Therapeutics, Publisher: Elsevier, Pages: S49-S49, ISSN: 0959-8049
Rozati H, Williams M, 2022, SURVIVAL OUTCOMES OF STEREOTACTIC RADIOTHERAPY FOR TEN OR MORE BRAIN METASTASES, Publisher: OXFORD UNIV PRESS INC, Pages: 9-9, ISSN: 1522-8517
Kanso N, Le Calvez K, Mauricaite R, et al., 2022, THE COST OF INPATIENT CARE FOR ADULT PRIMARY BRAIN TUMOUR PATIENTS IN ENGLAND, 17th Meeting of the European-Association-of-Neuro-Oncology, Publisher: OXFORD UNIV PRESS INC, ISSN: 1522-8517
Chen J, Williams M, Huang Y, et al., 2022, Identifying Topics and Evolutionary Trends of Literature on Brain Metastases Using Latent Dirichlet Allocation, FRONTIERS IN MOLECULAR BIOSCIENCES, Vol: 9
Chen J, Sinclair G, Rozati H, et al., 2022, Improving on whole-brain radiotherapy in patients with large brain metastases: a planning study to support the AROMA clinical trial, Radiotherapy and Oncology, Vol: 170, Pages: 176-183, ISSN: 0167-8140
PURPOSE: To develop a novel dose-escalated volumetric modulated arc therapy (VMAT) strategy for patients with single or multiple large brain metastases which can deliver a higher dose to individual lesions for better local control (LC), and to compare dosimetry between whole brain radiotherapy (WBRT), hippocampal-sparing whole brain radiotherapy (HS-WBRT) and different VMAT-based focal radiotherapy approaches. METHODS AND MATERIALS: We identified 20 patients with one to ten brain metastases and at least one lesion larger than 15 cm3 who had received WBRT as part of routine care. For each patient, we designed and evaluated five radiotherapy treatment plans, including WBRT, HS-WBRT and three VMAT dosing models. A dose of 20 Gy in 5 fractions was prescribed to the whole brain or target volumes depending on the plan, with higher doses to smaller lesions and dose-escalated inner planning target volumes (DE-iPTV) in VMAT plans, respectively. Treatment plans were evaluated using the efficiency index, mean dose and D0.1cc to the target volumes and organs at risk. RESULTS: Compared with WBRT, VMAT plans achieved a significantly more efficient dose distribution in brain lesions, especially with our DE-iPTV model, while minimising the dose to the normal brain and other organs at risks (OARs) (p < 0.05). CONCLUSIONS: VMAT plans obtained higher doses to brain metastases and minimised doses to OARs. Dose-escalated VMAT for larger lesions allows higher radiotherapy doses to be delivered to larger lesions while maintaining safe doses to OARs.
Pati S, Baid U, Edwards B, et al., 2022, Federated learning enables big data for rare cancer boundary detection, Publisher: arXiv
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing.
Hallows R, Glazier L, Katz MS, et al., 2022, Safe and Ethical Artificial Intelligence in Radiotherapy - Lessons Learned From the Aviation Industry, CLINICAL ONCOLOGY, Vol: 34, Pages: 99-101, ISSN: 0936-6555
- Author Web Link
- Citations: 4
Mi E, Mauricaite R, Pakzad-Shahabi L, et al., 2021, Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma, British Journal of Cancer, Vol: 126, Pages: 196-203, ISSN: 0007-0920
BackgroundGlioblastoma is the commonest malignant brain tumour. Sarcopenia is associated with worse cancer survival, but manually quantifying muscle on imaging is time-consuming. We present a deep learning-based system for quantification of temporalis muscle, a surrogate for skeletal muscle mass, and assess its prognostic value in glioblastoma.MethodsA neural network for temporalis segmentation was trained with 366 MRI head images from 132 patients from 4 different glioblastoma data sets and used to quantify muscle cross-sectional area (CSA). Association between temporalis CSA and survival was determined in 96 glioblastoma patients from internal and external data sets.ResultsThe model achieved high segmentation accuracy (Dice coefficient 0.893). Median age was 55 and 58 years and 75.6 and 64.7% were males in the in-house and TCGA-GBM data sets, respectively. CSA was an independently significant predictor for survival in both the in-house and TCGA-GBM data sets (HR 0.464, 95% CI 0.218–0.988, p = 0.046; HR 0.466, 95% CI 0.235–0.925, p = 0.029, respectively).ConclusionsTemporalis CSA is a prognostic marker in patients with glioblastoma, rapidly and accurately assessable with deep learning. We are the first to show that a head/neck muscle-derived sarcopenia metric generated using deep learning is associated with oncological outcomes and one of the first to show deep learning-based muscle quantification has prognostic value in cancer.
Patel M, Zhan J, Natarajan K, et al., 2021, Artificial intelligence for early prediction of treatment response in glioblastoma, Neuro-Oncology, Vol: 23, Pages: iv1-iv1, ISSN: 1522-8517
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Aims</jats:title> <jats:p>Treatment response assessment in glioblastoma is challenging. Patients routinely undergo conventional magnetic resonance imaging (MRI), but it has a low diagnostic accuracy for distinguishing between true progression (tPD) and pseudoprogression (psPD) in the early post-chemoradiotherapy time period due to similar imaging appearances. The aim of this study was to use artificial intelligence (AI) on imaging data, clinical characteristics and molecular information within machine learning models, to distinguish between and predict early tPD from psPD in patients with glioblastoma.</jats:p> </jats:sec> <jats:sec> <jats:title>Method</jats:title> <jats:p>The study involved retrospective analysis of patients with newly-diagnosed glioblastoma over a 3.5 year period (n=340), undergoing surgery and standard chemoradiotherapy treatment, with an increase in contrast-enhancing disease on the baseline MRI study 4-6 weeks post-chemoradiotherapy. Studies had contrast-enhanced T1-weighted imaging (CE-T1WI), T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences, acquired at 1.5 Tesla with 6-months follow-up to determine the reference standard outcome. 76 patients (mean age 55 years, range 18-76 years, 39% female, 46 tPD, 30 psPD) were included. Machine learning models utilised information from clinical characteristics (age, gender, resection extent, performance status), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and 307 quantitative imaging features; extracted from baseline study CE-T1WI/ADC and T2WI sequences using semi-automatically segmented enhancing disease and perilesional oedema masks respectively. Feature selection was performed within bootstrapped cross-validate
Sinclair H, Le Calvez K, Chen J, et al., 2021, Estimating population-based incidence of brain metastases–a comprehensive incident cohort study, BNOS 2021 Meeting, Publisher: Oxford University Press, Pages: iv21-iv21, ISSN: 1522-8517
Wang JW, Williams M, 2021, Exploring definitions of radiological sarcopenia in cancer: a protocol for a scoping review, BMJ Open, Vol: 11, ISSN: 2044-6055
Introduction Sarcopenia is the loss of skeletal muscle volume or quality, a concept previously established in age-related frailty. Sarcopenia is part of the cancer cachexia syndrome and has therefore been explored as biomarker through the opportunistic measurement of skeletal muscle from routine cancer imaging. However, there is inconsistency in diagnostic landmarks and cut-offs. The most common assessment method is skeletal muscle area at the slice level of the third lumbar vertebrae divided by height squared. Alternative sarcopenia measures have been derived from morphological descriptions of the psoas, thoracic and cervical muscles, driven by tumour-specific anatomical imaging.Current tumour-site specific reviews suggest a link between heterogeneously defined sarcopenia on tumour site-specific outcomes. Because lack of uniformity, a scoping review is best suited to streamline anatomically based definitions and map the evidence to outcomes. The aim of this article is to describe a protocol for a scoping review that will homogenise the evidence of radiological sarcopenia in cancer. The extent, range and nature of reports will be examined, after which possible titles for potential systematic reviews identified.Methods and analysis We will apply methods based on the Joanna Briggs Institute scoping review manual. Predefined search terms compiled with a librarian experienced in systematic reviews will be used to search PubMed/Medline, Embase, Scopus and Cochrane databases studies correlating cross-sectional cancer sarcopenia biomarkers with clinical outcomes. Studies will be mapped according to whether they have defined new sarcopenia measures or applied previous definitions to new populations, both with reported outcomes. This review will generate a numerical analysis on the extent of cancer sarcopenia measures as well as a narrative synthesis to describe the applications of radiologically derived sarcopenia in cancer.
Varughese M, Treece S, Drinkwater K, et al., 2021, Failing to Close the Gap between Evidence and Clinical Practice in Radical Bladder Cancer Radiotherapy: A Critical Unmet Need, CLINICAL ONCOLOGY, Vol: 33, Pages: 340-341, ISSN: 0936-6555
Williams M, Mi E, Le Calvez K, et al., 2021, Estimating the risk of death from COVID-19 in adult cancer patients, Clinical Oncology, Vol: 33, Pages: e172-e79, ISSN: 0936-6555
AIMS: During the coronavirus disease 2019 (COVID-19) pandemic, organisations have produced management guidance for cancer patients and the delivery of cytotoxic chemotherapy, but none has offered estimates of risk or the potential impact across populations. MATERIALS AND METHODS: We combined data from four countries to produce pooled age-banded case fatality rates, calculated the sex difference in survival and used data from four recent studies to convert case fatality rates into age/sex-stratified infection fatality rates (IFRs). We estimated the additional risk of death in cancer patients and in those receiving chemotherapy. We illustrate the impact of these by considering the impact on a national incident cancer cohort and analyse the risk-benefit in some clinical scenarios. RESULTS: We obtained data based on 412 985 cases and 41 854 deaths. The pooled estimate for IFR was 0.92%. IFRs for patients with cancer ranged from 0 to 29% and were higher in patients receiving chemotherapy (0.01-46%). The risk was significantly higher with age and in men compared with women. 37.5% of patients with a new diagnosis of cancer in 2018 had an IFR ≥5%. Survival benefits from adjuvant chemotherapy ranged from 5 to 10% in some common cancers, compared with the increased risk of death from COVID-19 of 0-3%. CONCLUSIONS: Older male patients are at a higher risk of death with COVID-19. Patients with cancer are also at a higher risk, as are those who have recently received chemotherapy. We provide well-founded estimates to allow patients and clinicians to better balance these risks and illustrate the wider impact in a national incident cohort.
Spencer K, Hall P, Henry A, et al., 2020, Fractionation and early mortality in palliative radiotherapy across the English NHS, Publisher: ELSEVIER IRELAND LTD, Pages: S285-S286, ISSN: 0167-8140
Loveday C, Sud A, Jones ME, et al., 2020, Prioritisation by FIT to mitigate the impact of delays in the 2-week wait colorectal cancer referral pathway during the COVID-19 pandemic: a UK modelling study, Gut, Vol: 70, Pages: 1053-1060, ISSN: 0017-5749
OBJECTIVE: To evaluate the impact of faecal immunochemical testing (FIT) prioritisation to mitigate the impact of delays in the colorectal cancer (CRC) urgent diagnostic (2-week-wait (2WW)) pathway consequent from the COVID-19 pandemic. DESIGN: We modelled the reduction in CRC survival and life years lost resultant from per-patient delays of 2-6 months in the 2WW pathway. We stratified by age group, individual-level benefit in CRC survival versus age-specific nosocomial COVID-19-related fatality per referred patient undergoing colonoscopy. We modelled mitigation strategies using thresholds of FIT triage of 2, 10 and 150 µg Hb/g to prioritise 2WW referrals for colonoscopy. To construct the underlying models, we employed 10-year net CRC survival for England 2008-2017, 2WW pathway CRC case and referral volumes and per-day-delay HRs generated from observational studies of diagnosis-to-treatment interval. RESULTS: Delay of 2/4/6 months across all 11 266 patients with CRC diagnosed per typical year via the 2WW pathway were estimated to result in 653/1419/2250 attributable deaths and loss of 9214/20 315/32 799 life years. Risk-benefit from urgent investigatory referral is particularly sensitive to nosocomial COVID-19 rates for patients aged >60. Prioritisation out of delay for the 18% of symptomatic referrals with FIT >10 µg Hb/g would avoid 89% of these deaths attributable to presentational/diagnostic delay while reducing immediate requirement for colonoscopy by >80%. CONCLUSIONS: Delays in the pathway to CRC diagnosis and treatment have potential to cause significant mortality and loss of life years. FIT triage of symptomatic patients in primary care could streamline access to colonoscopy, reduce delays for true-positive CRC cases and reduce nosocomial COVID-19 mortality in older true-negative 2WW referrals. However, this strategy offers benefit only in short-term rationalisation of limited endoscopy services: the a
Sud A, Jones ME, Broggio J, et al., 2020, Collateral damage: the impact on outcomes from cancer surgery of the COVID-19 pandemic, Annals of Oncology, Vol: 31, Pages: 1065-1074, ISSN: 0923-7534
BackgroundCancer diagnostics and surgery have been disrupted by the response of health care services to the coronavirus disease 2019 (COVID-19) pandemic. Progression of cancers during delay will impact on patients' long-term survival.Patients and methodsWe generated per-day hazard ratios of cancer progression from observational studies and applied these to age-specific, stage-specific cancer survival for England 2013–2017. We modelled per-patient delay of 3 and 6 months and periods of disruption of 1 and 2 years. Using health care resource costing, we contextualise attributable lives saved and life-years gained (LYGs) from cancer surgery to equivalent volumes of COVID-19 hospitalisations.ResultsPer year, 94 912 resections for major cancers result in 80 406 long-term survivors and 1 717 051 LYGs. Per-patient delay of 3/6 months would cause attributable death of 4755/10 760 of these individuals with loss of 92 214/208 275 life-years, respectively. For cancer surgery, average LYGs per patient are 18.1 under standard conditions and 17.1/15.9 with a delay of 3/6 months (an average loss of 0.97/2.19 LYGs per patient), respectively. Taking into account health care resource units (HCRUs), surgery results on average per patient in 2.25 resource-adjusted life-years gained (RALYGs) under standard conditions and 2.12/1.97 RALYGs following delay of 3/6 months. For 94 912 hospital COVID-19 admissions, there are 482 022 LYGs requiring 1 052 949 HCRUs. Hospitalisation of community-acquired COVID-19 patients yields on average per patient 5.08 LYG and 0.46 RALYGs.ConclusionsModest delays in surgery for cancer incur significant impact on survival. Delay of 3/6 months in surgery for incident cancers would mitigate 19%/43% of LYGs, respectively, by hospitalisation of an equivalent volume of admissions for community-acquired COVID-19. This rises to 26%/59%, respectively, when considering RALYGs. To avoid a downstream public health crisis of avoidable cancer deaths, cancer diagnosti
Sud A, Torr B, Jones M, et al., 2020, Effect of delays in the UK two-week wait cancer referral pathway during the COVID-19 pandemic on cancer survival: a modelling study, The Lancet Oncology, Vol: 21, Pages: 1035-1044, ISSN: 1213-9432
Background: During the COVID-19 lockdown, referrals via the 2 Week Wait (2WW) urgent pathway for suspected cancer in England are reported to have dropped by up to 84%. We aimed to examine the impact on cancer survival of different scenarios of lockdown accumulated-backlog. We also aimed to examine by tumour-referral-group and age, survival benefit per referred patient considering survival decrement from delayed referral versus risk of death from nosocomial SARS-CoV-2 infection.Methods: To construct the underlying models, we used age- and stage-stratified 10 year cancer survival estimates for England 2007-2017 for 20 common tumour-types. We applied per-day hazard ratios for cancer progression generated from observational studies of delay to-treatment. We quantified the annual numbers of cancers diagnosed via the 2WW-pathway using the 2WW age- and stage-specific breakdowns. From these, for per-patient delays of 1- 6 months, we estimated aggregate number of lives lost and life-years lost in England. Using referral-to-diagnosis conversion rates and COVID-19 case fatality rates, we also estimated the survival increment per patient referred. Findings: Per month across England in 2013-2016, on average 6,281 patients with Stage 1- 3 cancer were diagnosed via the 2WW pathway of whom 1,691 would be predicted to die within 10 years from their disease. We estimated 2WW-pathway presentational-delay from three months of lockdown will result in total in 181/361/542 attributable additional deaths (if % reduction in referrals was 25/50/75% respectively). Limited diagnostic capacity to address the backlog may result in additional delays: 401/811/1,231 attributable additional deaths are estimated if additional diagnostic capacity is delayed until months 3-8 post-lockdown. 2-month delay in 2WW investigatory referral results in average loss of life-years per-referred-patient of between 0 and 0.7, depending on age and tumour-type. Interpretation: Prompt provision of additional capacity f
Macnair A, Sharkey A, Le Calvez K, et al., 2020, The Trigger Project: The Challenge of Introducing Electronic Patient-Reported Outcome Measures Into a Radiotherapy Service, CLINICAL ONCOLOGY, Vol: 32, Pages: E76-E79, ISSN: 0936-6555
- Author Web Link
- Citations: 3
Booth TC, Williams M, Luis A, et al., 2020, Machine learning and glioma imaging biomarkers, Clinical Radiology, Vol: 75, Pages: 20-32, ISSN: 0009-9260
AIM: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. MATERIALS AND METHODS: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high-grade glioma biomarkers for treatment response monitoring, prognosis, and prediction. RESULTS: Magnetic resonance imaging (MRI) is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging). CONCLUSION: Although pioneering, most of the evidence is of a low level, having been obtained retrospectively and in single centres. Studies applying ML to build neuro-oncology monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. Development and validation of ML models applied to neuro-oncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary.
Sharkey A, MacNair A, Le Calvez K, et al., 2019, The Trigger project: Introducing electronic patient reported outcome measures into radiotherapy services, National Cancer Research Institute and Leader in Healthcare Conferences
BackgroundPatients receiving pelvic radiotherapy can experience long term gastrointestinal side effects post-radiotherapy. The Trigger project identifies patients experiencing symptoms of radiation-related bowel toxicity using the ALERT-B questionnaire, and directs them to the appropriate clinician.Trigger is a service evaluation project, aiming to prove the utility of electronic patient reported outcome measures (PROMs), and to demonstrate the feasibility of a low-resource project as a model for collecting PROMs. It is a collaboration between Macmillan Cancer Support, the Royal College of Radiologists, and three NHS Trusts: Velindre, Imperial College Healthcare and Brighton and Sussex University Hospitals.MethodPatients register on the Trigger website, hosted by My Clinical Outcomes, and receive periodic emails to complete the short ALERT-B questionnaire electronically, to screen for long-term bowel symptoms which could have been caused by pelvic radiotherapy. If answering “yes” to any of the questions, patients are directed to appropriate services. Six months following the completion of their radiotherapy, patients are sent a separate questionnaire to evaluate the utility of the project. Results336 patients registered in first the 9 months across the 3 sites. Patients with a range of different cancers signed up: anal (2%), bladder (1%), prostate (87%), rectal (4%) and gynaecological (6%). 43 patients (of 65 eligible (uptake 66%)) have answered their 6-month post treatment questionnaire thus far, and 72% answered "yes" to at least one of the ALERT-B questions. 85% of responding patients reported they found the Trigger project helpful.ConclusionThese promising results show that electronic PROMS can be introduced in radiotherapy departments using a low resource model. The Trigger project works as a feasibility model, showing patients engage with electronic PROMs projects, and find them useful. PROMs for other tumour types could be collected in a
Sharkey A, MacNair A, Le Calvez K, et al., 2019, The Trigger project: Introducing electronic patient reported outcome measures into radiotherapy services, Cancer Conference of the National-Cancer-Research-Institute (NCRI), Publisher: NATURE PUBLISHING GROUP, Pages: 26-26, ISSN: 0007-0920
Goel A, Shahabi L, Narenthiran G, et al., 2019, FACTORS AFFECTING TREATMENT STRATEGY, COMPLETION OF PLANNED TREATMENT AND SURVIVAL IN OLDER PATIENTS WITH GLIOBLASTOMA, Meeting of the British-Neuro-Oncology-Society (BNOS), Publisher: OXFORD UNIV PRESS INC, Pages: 14-14, ISSN: 1522-8517
Dadhania S, Wang JW, Yu B, et al., 2019, EARLY EFFECTS OF SURGERY AND RADIOTHERAPY ON ACTIVITY LEVELS IN PATIENTS WITH BRAIN TUMOURS: PRELIMINARY DATA FROM THE BRAINWEAR TRIAL, Meeting of the British-Neuro-Oncology-Society (BNOS), Publisher: OXFORD UNIV PRESS INC, Pages: 12-12, ISSN: 1522-8517
Brodbelt AR, Barclay ME, Greenberg D, et al., 2019, The outcome of patients with surgically treated meningioma in England: 1999-2013. A cancer registry data analysis., British Journal of Neurosurgery, Vol: 33, Pages: 641-647, ISSN: 0268-8697
Purpose: Meningiomas are the commonest predominantly non-malignant brain tumour in adults. The use of surgery appears to be increasing, and outcomes are thought to be good, but whole nation data for England is scarce. The aim of this report is to examine the epidemiology of patients operated for cranial and spinal meningioma in England, and to assess associations between outcomes and gender, age, meningioma site (cranial or spinal), and grade. Material and methods: A search strategy encompassing all patients coded with cranial and spinal meningioma treated between January 1999 and December 2013 was obtained from data linkage between the National Cancer Registration and Analysis Service and Hospital Episode Statistics for England. Results: 25,694 patients were diagnosed with meningioma in England between 1999 and 2013, in whom 24,302 were cranial and 1392 spinal. Of these patients, 14,229 (60%) cranial and 1188 (85%) spinal meningioma received surgery. Of those operated on 70.1% were women, and, where the tumour grade was recorded, 79.5% were WHO grade I, 18.4% grade II, and 2.1% grade III. Five and ten year net survival rates for surgically treated cranial meningiomas were respectively 90% and 81% for those with WHO grade I, 80% and 63% for grade II, and 30% and 15% for WHO grade III tumours. Overall survival after surgery is better in women, younger adults, and people with spinal or lower grade meningiomas. Outcomes have improved over the time period examined. Conclusion: The outcome for patients with meningioma is good and is improving. However, there remains a significant mortality related to the disease process.
Laaniste L, Srivastava P, Stylianou T, et al., 2019, Integrated systems-genetic analyses reveal a network target for delaying glioma progression, Annals of Clinical and Translational Neurology, Vol: 6, Pages: 1616-1638, ISSN: 2328-9503
ObjectiveTo identify a convergent, multitarget proliferation characteristic for astrocytoma transformation that could be targeted for therapy discovery.MethodsUsing an integrated functional genomics approach, we prioritized networks associated with astrocytoma progression using the following criteria: differential co‐expression between grade II and grade III IDH1‐mutated and 1p/19q euploid astrocytomas, preferential enrichment for genetic risk to cancer, association with patient survival and sample‐level genomic features. Drugs targeting the identified multitarget network characteristic for astrocytoma transformation were computationally predicted using drug transcriptional perturbation data and validated using primary human astrocytoma cells.ResultsA single network, M2, consisting of 177 genes, was associated with glioma progression on the basis of the above criteria. Functionally, M2 encoded physically interacting proteins regulating cell cycle processes and analysis of genome‐wide gene‐regulatory interactions using mutual information and DNA–protein interactions revealed the known regulators of cell cycle processes FoxM1, B‐Myb, and E2F2 as key regulators of M2. These results suggest functional disruption of M2 via gene mutation or altered expression as a convergent pathway regulating astrocytoma transformation. By considering M2 as a multitarget drug target regulating astrocytoma transformation, we identified several drugs that are predicted to restore M2 expression in anaplastic astrocytoma toward its low‐grade profile and of these, we validated the known antiproliferative drug resveratrol as down‐regulating multiple nodes of M2 including at nanomolar concentrations achievable in human cerebrospinal fluid by oral dosing.InterpretationOur results identify M2 as a multitarget network characteristic for astrocytoma progression and encourage M2‐based drug screening to identify new compounds for preventing glioma transformation.
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