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| Verfasst von: | Kriegsmann, Mark [VerfasserIn]  |
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| | Haag, Christian [VerfasserIn] |
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| | Weis, Cleo-Aron Thias [VerfasserIn] |
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| | Steinbuß, Georg [VerfasserIn] |
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| | Warth, Arne [VerfasserIn] |
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| | Zgorzelski, Christiane [VerfasserIn] |
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| | Muley, Thomas [VerfasserIn] |
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| | Winter, Hauke [VerfasserIn] |
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| | Eichhorn, Martin E. [VerfasserIn] |
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| | Eichhorn, Florian [VerfasserIn] |
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| | Kriegsmann, Jörg [VerfasserIn] |
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| | Christopolous, Petros [VerfasserIn] |
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| | Thomas, Michael [VerfasserIn] |
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| | Witzens-Harig, Mathias [VerfasserIn] |
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| | Sinn, Peter [VerfasserIn] |
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| | Winterfeld, Moritz von [VerfasserIn] |
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| | Heußel, Claus Peter [VerfasserIn] |
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| | Herth, Felix [VerfasserIn] |
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| | Klauschen, Frederick [VerfasserIn] |
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| | Stenzinger, Albrecht [VerfasserIn] |
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| | Kriegsmann, Katharina [VerfasserIn] |
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| Titel: | Deep learning for the classification of small-cell and non-small-cell lung cancer |
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| Verf.angabe: | 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 and Katharina Kriegsmann |
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| E-Jahr: | 2020 |
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| Jahr: | 17 June 2020 |
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| Umfang: | 16 S. |
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| Fussnoten: | Gesehen am 10.09.2020 |
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| Titel Quelle: | Enthalten in: Cancers |
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| Ort Quelle: | Basel : MDPI, 2009 |
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| Jahr Quelle: | 2020 |
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| Band/Heft Quelle: | 12(2020,6) Artikel-Nummer 1604, 16 Seiten |
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| ISSN Quelle: | 2072-6694 |
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| Abstract: | 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. |
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| DOI: | doi:10.3390/cancers12061604 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.3390/cancers12061604 |
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| | Volltext: https://www.mdpi.com/2072-6694/12/6/1604 |
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| | DOI: https://doi.org/10.3390/cancers12061604 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | artificial intelligence |
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| | deep learning |
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| | histology |
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| | lung cancer |
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| | non-small cell lung cancer |
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| | small cell lung cancer |
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| K10plus-PPN: | 1729854923 |
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| Verknüpfungen: | → Zeitschrift |
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Deep learning for the classification of small-cell and non-small-cell lung cancer / Kriegsmann, Mark [VerfasserIn]; 17 June 2020 (Online-Ressource)
