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BACKGROUND: The National Lung Screening Trial (ILST) and the NELSON study showed that lung cancer-specific mortality can be reduced by 20-24% using low-dose computed tomography (LDCT) screening in high-risk populations, due to an increase in early-stage diagnoses. RESEARCH QUESTION: How much lung cancer-related direct costs may be reduced using a LDCT screening programme based on the ILST protocol in a public healthcare system?STUDY DESIGN AND METHODS: Cost analysis of lung cancer screening (LCS) vs. usual care in the framework of the retail price of the Catalan public healthcare system. The LCS group included costs of screening (ILST protocol), treatment cost according to weighted average distribution of TNM staging in the NLST and NELSON trials, lung cancer detection rate (CDR) and smoking cessation intervention. The usual care group included treatment costs based on distribution of TNM staging registered in the Spanish index hospital. RESULTS: In the usual care group, treatment costs were €91,959. In the 5-year duration of the LCS programme, the average expected costs per subject were €1,342 (range €1,054-1,832 depending on malignancy in detected nodules) for screening and €32,431 for treatment, with an expected reduction of €952 based on an average CDR of 1.6%. The decrease in cost resulting from stage shift offsets 70.6% of the costs of the screening programme. INTERPRETATION: Baseline 5-year LCS costs are low according to the ILST protocol. In the Catalan public healthcare system, the expense reduction from the stage shift due to the LCS programme compensates a substantial part of its costs.

INTRODUCTION: COVID-19 pneumonia is associated with a high rate of pulmonary embolism (PE) and its diagnosis can be changeling. In patients with contraindications for CT pulmonary angiography (CTPA) or nondiagnostic on CTPA, perfusion single photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnosis option in patients with COVID-19 disease. Our aim was to develop an artificial intelligence (AI) model based on QSPECT/ CT images of patients during the COVID-19 pandemic which is able to classify lung lesions to optimize PE diagnosis. METHODS: Single center study with a prospective observational branch with patients who tested positive for COVID-19 and underwent a perfusion SPECT-CT study for diagnosis of PE, from April to September 2020. The second branch is retrospective, with patients in pre-pandemic period who underwent Q-SPECT/CT for diagnosis of PE from January to December 2018. For each patient, a Q-SPECT/CT based on intravenous administration of 6 mCi (222MBq) of 99mTC-macroaggregates of human-albumin (99mTC-MAA) was performed, with the subsequent tomoscintigraphy (SPECT) and a CT. The entire image pre-processing (volume exploration, segmentation analysis, registration analysis) was conducted with MATLAB. The final diagnosis for each patient and the different tissue type segments were analyzed and validated by two senior nuclear physicians. Our Intelligent Radiomic System for the identification of patients with PE (with or without pneumonia) is based on a local analysis of SPECT-CT volumes that considers both CT and SPECT values for each volume point. Volumes are first co-registered and their intensity is normalized in [0, 1] to account for differences due to acquisition. A support vector machine (SVM) model was trained to discriminate among 4 different types of tissue: no pneumonia without PE (control group), no pneumonia with PE, pneumonia without PE and pneumonia with PE. We followed a k-fold (with k=30) scheme for statistical analysis of results. RESULTS: We collected 133 patients, 63 in prospective branch (26 with PE, 22 without PE, 15 indeterminate- PE) and 70 in retrospective branch (31 healthy/control, 39 PE). Concerning the local analysis, for all cases we obtain an average sensitivity and positive predictive value over 92%. CONCLUSION: This study represents a first step towards a complete intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT. The capability to detect alterations in perfusion for COVID-19 pneumonia encourages developing a tool in the cloud for clinical use and further investigates if it can also predict long term complications.