Determining the impact of an artificial intelligence tool on the management of pulmonary nodules detected incidentally on CT (DOLCE) study protocol: a prospective, non-interventional multicentre UK study.

INTRODUCTION: In a small percentage of patients, pulmonary nodules found on CT scans are early lung cancers. Lung cancer detected at an early stage has a much better prognosis. The British Thoracic Society guideline on managing pulmonary nodules recommends using multivariable malignancy risk prediction models to assist in management. While these guidelines seem to be effective in clinical practice, recent data suggest that artificial intelligence (AI)-based malignant-nodule prediction solutions might outperform existing models. METHODS AND ANALYSIS: This study is a prospective, observational multicentre study to assess the clinical utility of an AI-assisted CT-based lung cancer prediction tool (LCP) for managing incidental solid and part solid pulmonary nodule patients vs standard care. Two thousand patients will be recruited from 12 different UK hospitals. The primary outcome is the difference between standard care and LCP-guided care in terms of the rate of benign nodules and patients with cancer discharged straight after the assessment of the baseline CT scan. Secondary outcomes investigate adherence to clinical guidelines, other measures of changes to clinical management, patient outcomes and cost-effectiveness. ETHICS AND DISSEMINATION: This study has been reviewed and given a favourable opinion by the South Central-Oxford C Research Ethics Committee in UK (REC reference number: 22/SC/0142).Study results will be available publicly following peer-reviewed publication in open-access journals. A patient and public involvement group workshop is planned before the study results are available to discuss best methods to disseminate the results. Study results will also be fed back to participating organisations to inform training and procurement activities. TRIAL REGISTRATION NUMBER: NCT05389774.

Immunohistochemical evaluation of E-cadherin in oral epithelial dysplasia and squamous cell carcinoma.

Aim: To evaluate the expression of E-cadherin (E-cad) in oral epithelial dysplasia (OED) and oral squamous cell carcinoma (OSCC). Material and Method: Immunohistochemistry was used to detect E-cad expression in 20 cases each of normal oral mucosa, oral epithelial dysplasia and squamous cell carcinoma. Statistical Analysis Used: Inferential statistical methods for statistical analysis used were Chi-square test for comparison of the frequency between different severity of dysplasia and OSCC. Results: Upon assessing the expression of E-cad in OED and OSCC, increase in E-cad immunoreactivity was seen in early lesions. However, the expression of E-cad decreased significantly as the grade of dysplasia increased. Conclusion: We observed a significant decrease in E-cad expression from dysplasia to poorly differentiated squamous cell carcinoma suggesting that loss of expression of E-cad is closely related to carcinoma.