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: 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 W Tags: Article Source Type: research
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