Machine learning-based automated sponge cytology for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction: a nationwide, multicohort, prospective study.

BACKGROUND: Oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction have a dismal prognosis, and early detection is key to reduce mortality. However, early detection depends on upper gastrointestinal endoscopy, which is not feasible to implement at a population level. We aimed to develop and validate a fully automated machine learning-based prediction tool integrating a minimally invasive sponge cytology test and epidemiological risk factors for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction before endoscopy. METHODS: For this multicohort prospective study, we enrolled participants aged 40-75 years undergoing upper gastrointestinal endoscopy screening at 39 tertiary or secondary hospitals in China for model training and testing, and included community-based screening participants for further validation. All participants underwent questionnaire surveys, sponge cytology testing, and endoscopy in a sequential manner. We trained machine learning models to predict a composite outcome of high-grade lesions, defined as histology-confirmed high-grade intraepithelial neoplasia and carcinoma of the oesophagus and oesophagogastric junction. The predictive features included 105 cytological and 15 epidemiological features. Model performance was primarily measured with the area under the receiver operating characteristic curve (AUROC) and average precision. The performance measures for cytologists with AI assistance was also assessed. FINDINGS: Between Jan 1, 2021, and June 30, 2022, 17 498 eligible participants were involved in model training and validation. In the testing set, the AUROC of the final model was 0·960 (95% CI 0·937 to 0·977) and the average precision was 0·482 (0·470 to 0·494). The model achieved similar performance to consensus of cytologists with AI assistance (AUROC 0·955 [95% CI 0·933 to 0·975]; p=0·749; difference 0·005, 95% CI, -0·011 to 0·020). If the model-defined moderate-risk and high-risk groups were referred for endoscopy, the sensitivity was 94·5% (95% CI 88·8 to 97·5), specificity was 91·9% (91·2 to 92·5), and the predictive positive value was 18·4% (15·6 to 21·6), and 90·3% of endoscopies could be avoided. Further validation in community-based screening showed that the AUROC of the model was 0·964 (95% CI 0·920 to 0·990), and 92·8% of endoscopies could be avoided after risk stratification. INTERPRETATION: We developed a prediction tool with favourable performance for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction. This approach could prevent the need for endoscopy screening in many low-risk individuals and ensure resource optimisation by prioritising high-risk individuals. FUNDING: Science and Technology Commission of Shanghai Municipality.

Analysis of 2,297 expressed sequence tags (ESTs) from a cDNA library of flax (Linum ustitatissimum L.) bark tissue.

Bast fibre crops are the second most important natural fibre crops following cotton. Of these, flax (Linum ustitatissimum L.) is the most widely planted in the world, with its fibre used for high quality linen textile. A cDNA library of flax bark tissues was constructed with the purpose of identifying genes involved in the Bast fibre development. A total of 2,297 unigene sequences were obtained from 3,200 randomly selected clones of the cDNA library. These sequences were grouped into 155 clusters and 2,142 singletons, which have been submitted to the GenBank databases. By putative functional annotation, 23.3% of these sequences were similar to known proteins in GenBank, 44.0% of these sequences were similar to unknown proteins, and 32.7% of these sequences showed no significant similarity to any other protein sequences in existing databases. Classified by the Gene Ontology, 24.8, 23.1 and 14.3% were assigned to molecular function, biological process, and cellular component GO terms, respectively. By further bioinformatics approaches, about 110 ESTs matched cell wall related genes in the MAIZEWALL database, representing 16 functional categories of all 19 categories, of which, the most abundant category was protein synthesis. Based on the PlantTFDB database, 39 of the 64 transcription factor families in the Arabidopsis thaliana genome were identified as being involved in flax cell wall formation. The sequences and bioinformatics analysis data generated in this paper will be useful for gene expression, cloning and genetic engineering studies to characterize bast fibre development and improve the properties of the bast fibres.