Cancers, Vol. 12, Pages 1604: Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer

Cancers, Vol. 12, Pages 1604: Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer Cancers doi: 10.3390/cancers12061604 Authors: Mark Kriegsmann Christian Haag Cleo-Aron Weis Georg Steinbuss Arne Warth Christiane Zgorzelski Thomas Muley Hauke Winter Martin E. Eichhorn Florian Eichhorn Joerg Kriegsmann Petros Christopolous Michael Thomas Mathias Witzens-Harig Peter Sinn Moritz von Winterfeld Claus Peter Heussel Felix J. F. Herth Frederick Klauschen Albrecht Stenzinger Katharina Kriegsmann Reliable entity subtyping is paramount for therapy stratification in lung cancer. Morphological evaluation remains the basis for entity subtyping and directs the application of additional methods such as immunohistochemistry (IHC). The decision of whether to perform IHC for subtyping is subjective, and access to IHC is not available worldwide. Thus, the application of additional methods to support morphological entity subtyping is desirable. Therefore, the ability of convolutional neuronal networks (CNNs) to classify the most common lung cancer subtypes, pulmonary adenocarcinoma (ADC), pulmonary squamous cell carcinoma (SqCC), and small-cell lung cancer (SCLC), was evaluated. A cohort of 80 ADC, 80 SqCC, 80 SCLC, and 30 skeletal muscle specimens was assembled; slides were scanned; tumor areas were annotated; image patches were extracted; and cases were randomly assigned to a training, validation or test set. Multiple...
Source: Cancers - Category: Cancer & Oncology Authors: Tags: Article Source Type: research