1,721,024 research outputs found
Association of Upper Gastrointestinal Surgery of Great Britain and Ireland (AUGIS)/perioperative quality initiative (POQI) consensus statement on prehabilitation in oesophagogastric surgery
No full textBackground: Prehabilitation is safe, feasible and may improve a range of outcomes in patients with oesophago-gastric cancer (OGC). Recent studies have suggested the potential of prehabilitation to improve body composition, sarcopenia and physical fitness, reduce surgical complications and improve quality of life. Despite this, prehabilitation services are not offered throughout all OGC centres in the UK. Where prehabilitation is offered, delivery and definitions vary significantly, as do funding sources and access. Methods: A professional association endorsed series of consensus meetings were conducted using a modified Delphi process developed by the Peri-Operative Quality Initiative (POQI) to identify and refine consensus statements relating to the development and delivery of prehabilitation services for OGC patients. Participants from a variety of disciplines were identified based on a track record of published studies in the field of prehabilitation and/or practice experience encompassing prehabilitation of OGC patients. Approval from the POQI board was obtained and independent supervision provided by POQI. Results: A total of 20 statements were developed and agreed by 26 participants over a preliminary meeting and 2 semi-structured formal POQI meetings. Ten research themes were identified. In the case of one statement, consensus was not reached and the statement was recorded and developed into a research theme. A strong recommendation was made for the majority of the consensus statements (17 of 20). Discussion: Consensus statements encompassing the interventions and outcomes of prehabilitation services in oesophago-gastric cancer surgery have been developed to inform the implementation of programmes.</p
A machine-learning approach to predict upper gastrointestinal multidisciplinary team treatment decisions
No full textBackground: the complexity of the Upper Gastrointestinal (UGI) multidisciplinary team (MDT) is growing, leading to rising clinician workload, time pressures and demands. This increases heterogeneity or ‘noise’ within decision-making for patients with oesophageal cancer (OC) and may lead to inconsistent treatment decisions. In this context, machine learning (ML) approaches offer the potential to standardize, automate and produce more consistent, data-driven decisions. Such models benefit from incorporating information-rich, diverse datasets to increase predictive model accuracy.Aims: the aim of this study was to develop a model capable of predicting UGI MDT treatment decisions for OC patients (whether the patient should receive surgery alone (S), chemotherapy prior to surgery (C+S) or chemoradiotherapy prior to surgery (CRT+S)) using only variables available at the time of the first treatment decision. Methods: we conducted a retrospective analysis of patients who underwent oesophageal cancer resections between 2010-2016. Twenty pre-operative clinical variables available to the MDT were used to develop a predictive machine learning model using an L2 penalised Multinomial Logistic Regression (MLR) classifier with 10-fold cross-validation and 5 repeats. Results: a total of 399 cases were identified with a male: female ratio of 3.6:1, and median age of 66.1yrs (range 32 – 83 yrs). The overall model accuracy was 61.4% (Kappa 0.399). A Receiver Operator Characteristic analysis produced an Area Under Curve of 87.7% for Surgery vs. CRT+S, 86.9% for Surgery vs C+S and 66.1% for C+S vs CRT+S when trialled on a validation set. Conclusions: these results suggest even basic ML modelling techniques offer the potential to model and predict current UGI MDT treatment decisions when selecting patients for surgery or neoadjuvant therapy options. Such models may allow prioritization of caseload, improve efficiency, potentially reduce waiting times, and offer data-driven decisions where more complex cases pose challenges. While this study is the first step in such a process, future work will need to incorporate additional data modalities such as medical imaging and histopathology as well as expansion to other ML classifier algorithms to determine the best performing models in order to improve overall predictive accuracy.<br/
Prevalence and risk factors for malignant nodal involvement in early esophago-gastric adenocarcinoma: results from the multicenter retrospective CONGRESS study (endoscopic resection, esophagectomy or gastrectomy for early esophagogastric cancers)
No full textObjective: the aim of this study was to quantify LNM risk and outcomes following treatment of early esophago-gastric (EG) adenocarcinoma.Background: the standard of care for early T1N0 EG cancer is endoscopic resection (ER). Radical surgical resection is recommended for patients perceived to be at risk of lymph node metastasis (LNM). Current models to select organ-preserving vs. surgical treatment are inconsistent.Methods: CONGRESS is a UK-based multicentre retrospective cohort study. Patients diagnosed with clinical or pathological T1N0 EG adenocarcinoma from 2015-2022 were included. Outcomes and rates of LNM were assessed. Cox regression was performed to assess the impact of prognostic and treatment factors on overall survival.Results: : 1,601 patients from 26 centres were included, with median follow-up 32 months(IQR 14-53). 1285/1612(80.3%) underwent ER, 497/1601(31.0%) underwent surgery. Overall rate of LNM was 13.5%. On ER staging, tumour depth (T1bsm2-3 17.6% vs. T1a 7.1%), lymphovascular invasion (17.2% vs. 12.6%), or signet cells (28.6% vs. 13.0%) were associated with LNM. In multivariable regression analysis, these were not significantly associated with LNM rates or survival. Adjusting for demographic and tumour variables, surgery after ER was associated with significant survival benefit, HR 0.33(0.15-0.77),P=0.010.Conclusion: this large multicentre dataset suggests that early EG adenocarcinoma is associated with significant risk of LNM. This data is representative of current real clinical practice with ER-based staging, and suggests previously held beliefs regarding reliability of predictive factors for LNM may need to be reconsidered. Further research to identify patients who may benefit from organ-preserving vs. surgical treatment is urgently required
Impact of PET/CT on management of oesophageal cancer a multi-centre study 2 year follow-up
No full textTotal neoadjuvant therapy in oesophageal and gastro-oesophageal junctional adenocarcinoma
Full text linkAdenocarcinoma of the oesophagus and gastro-oesophageal junction represent a large burden of cancer death in the Western World with an increasing incidence. In the past two decades, the overall survival of patients on a potentially curative treatment pathway has more than doubled due to the addition of perioperative oncological therapies to surgery. However, patients often fail to respond to oncological treatment or struggle to complete their treatment after surgery. In this review, we discuss the current evidence for total neoadjuvant therapy and options for assessment of treatment response.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Introduction of minimally invasive oeosophagectomy with thoracoscopic oesophageal mobilisation into UK practice: outcome in 102 consecutive patients
No full textAbstract
The Annual Scientific Meeting of the Association of Upper Gastrointestinal Surgeons for Great Britain and Ireland takes place this year in Oxford on the 9th and 10th of September.</jats:p
The influence of age in oesophageal cancer treatment decisions: a machine-learning approach
No full textBackground: although patient age plays a crucial role in determining curative treatment options for Oesophageal cancer (OC), the exact extent of its influence is not well defined. We used a computational machine learning (ML) approach to model the impact of age in combination with other key decision drivers, such as tumour and patient characteristics, on the likelihood of receiving different types of curative treatment.Methods: retrospective analysis of 399 OC patients undergoing curative treatment between 2010-2020 at our tertiary unit. A random forests (RF) classifier model was trained to predict curative treatment decisions for OC patients (neoadjuvant chemotherapy (NACT) and surgery, neoadjuvant chemoradiotherapy (NACRT) and surgery or surgery alone). Variable importance and Partial Dependence analyses were used to assess the importance of age in the model, its influence on treatment decisions, and how that relationship was affected with other decision driver co-variates.Results: variable importance analysis confirmed age as most important in the RF model, (26% of total model performance). Partial dependence analysis demonstrate that predicted base probabilities for receiving surgery and NACT changed significantly in older patients. Patients above 70 years had a substantially higher probability of receiving curative surgery and lower probability of NACT. Moreover, the probability of receiving Surgery and NACT is driven by disease characteristics (T and N staging) in patients < 70 years but age becomes increasingly important in predicting a surgical decision >70 years. The base probability of surgery and NACRT decisions was also influenced by performance status and age, but only age for NACT patients.Conclusion: we have successfully applied ML modelling combined with partial dependence analysis to delineate the relationship between age and OC MDT treatment decisions. Age heavily influences curative decisions in OC patients but plays a greater role in patients with specific tumour characteristics. This study provides the basis for exploring subconscious decision drivers and allows teams to address any health inequality.<br/
The effect of triplet platinum based neo-adjuvant chemotherapy on peri-operative outcome after oesophagectomy
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