Lung cancer prediction by Deep Learning to identify benign lung nodules

Adequate differentiation of benign and malignant small-to-intermediate sized, 5 – 15 mm, pulmonary nodules detected by computed tomography (CT) is a challenge for radiologists. With the improvement of CT scanners, an increasing number of these pulmonary nodules are detected, both in routine clinical care and in a lung cancer screening setting. Approximately 50% of smokers h ave a pulmonary nodule,[1] and 25% have more than one, although less than 1% of these nodules are malignant.[1] Nodule classification for both incidentally detected and screening detected nodules are based on nodule type, size, and growth, according to Fleischner and Lung-RADS™ guidelines.[2,3] De spite their widespread adoption, these nodule management protocols still result in a rather high false-positive rate.

Source: Lung Cancer - January 31, 2021 Category: Cancer & Oncology Authors: Marjolein A. Heuvelmans, Peter M.A. van Ooijen, Sarim Ather, Carlos Francisco Silva, Daiwei Han, Claus-Peter Heussel, William Hickes, Hans-Ulrich Kauczor, Petr Novotny, Heiko Peschl, Mieneke Rook, Roman Rubtsov, Oyunbileg von Stackelberg, Maria T. Tsakok, Source Type: research