The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis.

Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.

Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy.

Radiomics can interpret radiological images with more detail and in less time compared to the human eye. Some challenges in managing esophageal cancer can be addressed by incorporating radiomics into image interpretation, treatment planning, and predicting response and survival. This systematic review and meta-analysis provides a summary of the evidence of radiomics in esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE, and Ovid EMBASE databases-articles describing radiomics in esophageal cancer were included. A meta-analysis was also performed; 50 studies were included. For the assessment of treatment response using 18F-FDG PET/computed tomography (CT) scans, seven studies (443 patients) were included in the meta-analysis. The pooled sensitivity and specificity were 86.5% (81.1-90.6) and 87.1% (78.0-92.8). For the assessment of treatment response using CT scans, five studies (625 patients) were included in the meta-analysis, with a pooled sensitivity and specificity of 86.7% (81.4-90.7) and 76.1% (69.9-81.4). The remaining 37 studies formed the qualitative review, discussing radiomics in diagnosis, radiotherapy planning, and survival prediction. This review explores the wide-ranging possibilities of radiomics in esophageal cancer management. The sensitivities of 18F-FDG PET/CT scans and CT scans are comparable, but 18F-FDG PET/CT scans have improved specificity for AI-based prediction of treatment response. Models integrating clinical and radiomic features facilitate diagnosis and survival prediction. More research is required into comparing models and conducting large-scale studies to build a robust evidence base.

Novel Multi-Modal Therapies and Their Prognostic Potential in Gastric Cancer.

BACKGROUND: Gastric cancer has a poor prognosis and involves metastasis to the peritoneum in over 40% of patients. The optimal treatment modalities have not been established for gastric cancer patients with peritoneal carcinomatosis (GC/PC). Although studies have reported favourable prognostic factors, these have yet to be incorporated into treatment guidelines. Hence, our review aims to appraise the latest diagnostic and treatment developments in managing GC/PC. METHODS: A systematic review of the literature was performed using MEDLINE, EMBASE, the Cochrane Review, and Scopus databases. Articles were evaluated for the use of hyperthermic intraperitoneal chemotherapy (HIPEC) and pressurised intraperitoneal aerosolised chemotherapy (PIPAC) in GC/PC. A meta-analysis of studies reporting on overall survival (OS) in HIPEC and comparing the extent of cytoreduction as a prognostic factor was also carried out. RESULTS: The database search yielded a total of 2297 studies. Seventeen studies were included in the qualitative and quantitative analyses. Eight studies reported the short-term OS at 1 year as the primary outcome measure, and our analysis showed a significantly higher OS for the HIPEC/CRS cohort compared to the CRS cohort (pooled OR = 0.53; p = 0.0005). This effect persisted longer term at five years as well (pooled OR = 0.52; p < 0.0001). HIPEC and CRS also showed a longer median OS compared to CRS (pooled SMD = 0.61; p < 0.00001). Three studies reporting on PIPAC demonstrated a pooled OS of 10.3 (2.2) months. Prognostic factors for longer OS include a more complete cytoreduction (pooled OR = 5.35; p < 0.00001), which correlated with a peritoneal carcinomatosis index below 7. CONCLUSIONS: Novel treatment strategies, such as HIPEC and PIPAC, are promising in the management of GC/PC. Further work is necessary to define their role within the treatment algorithm and identify relevant prognostic factors that will assist patient selection.

New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study.

OBJECTIVES: The suspension of elective surgery during the COVID-19 pandemic is unprecedented and has resulted in record volumes of patients waiting for operations. Novel approaches that maximise capacity and efficiency of surgical care are urgently required. This study applies Markov multiscale community detection (MMCD), an unsupervised graph-based clustering framework, to identify new surgical care models based on pooled waiting-lists delivered across an expanded network of surgical providers. DESIGN: Retrospective observational study using Hospital Episode Statistics. SETTING: Public and private hospitals providing surgical care to National Health Service (NHS) patients in England. PARTICIPANTS: All adult patients resident in England undergoing NHS-funded planned surgical procedures between 1 April 2017 and 31 March 2018. MAIN OUTCOME MEASURES: The identification of the most common planned surgical procedures in England (high-volume procedures (HVP)) and proportion of low, medium and high-risk patients undergoing each HVP. The mapping of hospitals providing surgical care onto optimised groupings based on patient usage data. RESULTS: A total of 7 811 891 planned operations were identified in 4 284 925 adults during the 1-year period of our study. The 28 most common surgical procedures accounted for a combined 3 907 474 operations (50.0% of the total). 2 412 613 (61.7%) of these most common procedures involved 'low risk' patients. Patients travelled an average of 11.3 km for these procedures. Based on the data, MMCD partitioned England into 45, 16 and 7 mutually exclusive and collectively exhaustive natural surgical communities of increasing coarseness. The coarser partitions into 16 and seven surgical communities were shown to be associated with balanced supply and demand for surgical care within communities. CONCLUSIONS: Pooled waiting-lists for low-risk elective procedures and patients across integrated, expanded natural surgical community networks have the potential to increase efficiency by innovatively flexing existing supply to better match demand.

Protocol for LAsting Symptoms after Oesophageal Resectional Surgery (LASORS): multicentre validation cohort study.

INTRODUCTION: Surgery is the primary curative treatment for oesophageal cancer, with considerable recent improvements in long-term survival. However, surgery has a long-lasting impact on patient's health-related quality of life (HRQOL). Through a multicentre European study, our research group was able to identify key symptoms that affect patient's HRQOL. These symptoms were combined to produce a tool to identify poor HRQOL following oesophagectomy (LAsting Symptoms after Oesophageal Resection (LASOR) tool). The objective of this multicentre study is to validate a six-symptom clinical tool to identify patients with poor HRQOL for use in everyday clinical practice. METHODS AND ANALYSIS: Included patients will: (1) be aged 18 years or older, (2) have undergone an oesophagectomy for cancer between 2015 and 2019, and (3) be at least 12 months after the completion of adjuvant oncological treatments. Patients will be given the previously created LASOR questionnaire. Each symptom from the LASOR questionnaire will be graded according to impact on quality of life and frequency of the symptom, with a composite score from 0 to 5. The previously developed LASOR symptom tool will be validated against HRQOL as measured by the European Organisation for Research and Treatment of Cancer QLQC30 and OG25. SAMPLE SIZE: With a predicted prevalence of poor HRQOL of 45%, based on the previously generated LASOR clinical symptom tool, to validate this tool with a sensitivity and specificity of 80%, respectively, a minimum of 640 patients will need to be recruited to the study. ETHICS AND DISSEMINATION: NHS Health Research Authority (North East-York Research Ethics Committee) approval was gained 8 November 2019 (REC reference 19/NE/0352). Multiple platforms will be used for the dissemination of the research data, including international clinical and patient group presentations and publication of research outputs in a high impact clinical journal.