Lung cancer prediction by Deep Learning to identify benign lung nodules

INTRODUCTION: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity. METHODS: The LCP-CNN has been trained to generate a malignancy score for each nodule using CT data from the U.S. National Lung Screening Trial (NLST), and validated on CT scans containing 2106 nodules (205 lung cancers) detected in patients from from the Early Lung Cancer Diagnosis Using Artificial Intelligence and Big Data (LUCINDA) study, recruited from three tertiary referral centers in the UK, Germany and Netherlands. We pre-defined a benign nodule rule-out test, to identify benign nodules whilst maintaining a high sensitivity, by calculating thresholds on the malignancy score that achieve at least 99 % sensitivity on the NLST data. Overall performance per validation site was evaluated using Area-Under-the-ROC-Curve analysis (AUC). RESULTS: The overall AUC across the European centers was 94.5 % (95 %CI 92.6-96.1). With a high sensitivity of 99.0 %, malignancy could be ruled out in 22.1 % of the nodules, enabling 18.5 % of the patients to avoid follow-up scans. The two false-negative results both represented small typical carcinoids. CONCLUSION: The LCP-CNN, trained on participants with lung nodules from the US NLST dataset, showed excellent performance on identification of benign lung nodules in a multi-center external dataset, ruling out malignancy with high accuracy in about one fifth of the patients with 5-15 mm nodules

Translations from Greek and Latin classics, Part 1: 1550–1700: a revised bibliography

This is the first instalment of a two-part revision of the classical translation sections of the second edition of The Cambridge Bibliography of English Literature, Vols 2–3. The recent discontinuation of the revised edition of CBEL deprives the scholarly world of an up-to-date version of the most complete bibliography of its kind; this contribution makes good that loss for this topic. Over its eventual two parts 1550–1800 it runs to some 1,500 items of translation for what might be held to constitute the golden age of the English classical translating tradition. Checking of existing entries in the listings has led to a large number of internal corrections, including deletions, but the records have been expanded by a net 20%, with several minor classical authors added. As compared to the previous CBEL editions of the 1940s, this reflects the availability of digital-era resources such as the English Short Title Catalogue