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Article

Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer

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Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany
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Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany
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Department Hematology, Oncology and Rheumatology, Heidelberg University, 69120 Heidelberg, Germany
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Institute of Pathology, University Medical Centre Mannheim, Heidelberg University, 68782 Mannheim, Germany
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Institute of Pathology, Cytopathology, and Molecular Pathology, UEGP MVZ Gießen/Wetzlar/Limburg, 65549 Limburg, Germany
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Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
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Molecular Pathology Trier, 54296 Trier, Germany
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Danube Private University Krems, 3500 Krems, Austria
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Department of Thoracic Oncology, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
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Medical Faculty Heidelberg University, 69120 Heidelberg, Germany
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Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, Heidelberg University, 69120 Heidelberg, Germany
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Department of Diagnostic and Interventional Radiology, Thoraxklinik, Heidelberg University, 69120 Heidelberg, Germany
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Department of Pneumology and Critical Care Medicine, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
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Institute of Pathology, University Hospital Charité, 10117 Berlin, Germany
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Authors to whom correspondence should be addressed.
Cancers 2020, 12(6), 1604; https://doi.org/10.3390/cancers12061604
Received: 7 June 2020 / Revised: 14 June 2020 / Accepted: 15 June 2020 / Published: 17 June 2020
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 CNN architectures (VGG16, InceptionV3, and InceptionResNetV2) were trained and optimized to classify the four entities. A quality control (QC) metric was established. An optimized InceptionV3 CNN architecture yielded the highest classification accuracy and was used for the classification of the test set. Image patch and patient-based CNN classification results were 95% and 100% in the test set after the application of strict QC. Misclassified cases mainly included ADC and SqCC. The QC metric identified cases that needed further IHC for definite entity subtyping. The study highlights the potential and limitations of CNN image classification models for tumor differentiation. View Full-Text
Keywords: artificial intelligence; deep learning; lung cancer; histology; non-small cell lung cancer; small cell lung cancer artificial intelligence; deep learning; lung cancer; histology; non-small cell lung cancer; small cell lung cancer
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MDPI and ACS Style

Kriegsmann, M.; Haag, C.; Weis, C.-A.; Steinbuss, G.; Warth, A.; Zgorzelski, C.; Muley, T.; Winter, H.; Eichhorn, M.E.; Eichhorn, F.; Kriegsmann, J.; Christopoulos, P.; Thomas, M.; Witzens-Harig, M.; Sinn, P.; von Winterfeld, M.; Heussel, C.P.; Herth, F.J.F.; Klauschen, F.; Stenzinger, A.; Kriegsmann, K. Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer. Cancers 2020, 12, 1604. https://doi.org/10.3390/cancers12061604

AMA Style

Kriegsmann M, Haag C, Weis C-A, Steinbuss G, Warth A, Zgorzelski C, Muley T, Winter H, Eichhorn ME, Eichhorn F, Kriegsmann J, Christopoulos P, Thomas M, Witzens-Harig M, Sinn P, von Winterfeld M, Heussel CP, Herth FJF, Klauschen F, Stenzinger A, Kriegsmann K. Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer. Cancers. 2020; 12(6):1604. https://doi.org/10.3390/cancers12061604

Chicago/Turabian Style

Kriegsmann, Mark, Christian Haag, Cleo-Aron Weis, Georg Steinbuss, Arne Warth, Christiane Zgorzelski, Thomas Muley, Hauke Winter, Martin E. Eichhorn, Florian Eichhorn, Joerg Kriegsmann, Petros Christopoulos, Michael Thomas, Mathias Witzens-Harig, Peter Sinn, Moritz von Winterfeld, Claus P. Heussel, Felix J.F. Herth, Frederick Klauschen, Albrecht Stenzinger, and Katharina Kriegsmann. 2020. "Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer" Cancers 12, no. 6: 1604. https://doi.org/10.3390/cancers12061604

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