Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis.

Background: Methods to improve stratification of small (≤15 mm) lung nodules are needed. We aimed to develop a radiomics model to assist lung cancer diagnosis.

Methods: Patients were retrospectively identified using health records from January 2007 to December 2018.

Recovering from COVID-19: lessons learnt from an intensive secondary care follow-up service.

In response to the first COVID-19 surge in 2020, secondary care outpatient services were rapidly reconfigured to provide specialist review for disease sequelae. At our institution, comprising hospitals across three sites in London, we initially implemented a COVID-19 follow-up pathway that was in line with expert opinion at the time but more intensive than initial clinical guidelines suggested. We retrospectively evaluated the resource requirements for this service, which supported 526 patients from April 2020 to October 2020.

A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules.

Background: Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk.

Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.

The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation. To automate lung nodule identification in a tertiary cancer centre.

Symptomatic, biochemical and radiographic recovery in patients with COVID-19.

Background: The symptoms, radiography, biochemistry and healthcare utilisation of patients with COVID-19 following discharge from hospital have not been well described.

Methods: Retrospective analysis of 401 adult patients attending a clinic following an index hospital admission or emergency department attendance with COVID-19. Regression models were used to assess the association between characteristics and persistent abnormal chest radiographs or breathlessness.