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

Cancers

Katharina Kriegsmann¹  Arne Warth²  Christian Haag³  Mark Kriegsmann³  Albrecht Stenzinger³  Christiane Zgorzelski³  Peter Sinn³  Georg Steinbuss³  Moritzvon Winterfeld³  Frederick Klauschen⁴  Cleo-Aron Weis⁵  Mathias Witzens-Harig⁶  Joerg Kriegsmann⁷  Thomas Muley⁸  Petros Christopolous⁸  ClausPeter Heussel⁸  MartinE. Eichhorn⁸  Florian Eichhorn⁸  Michael Thomas⁸  Hauke Winter⁸  FelixJ. F. Herth⁸ 

[1]Department Hematology, Oncology and Rheumatology, Heidelberg University, 69120 Heidelberg, Germany

[2]Institute of Pathology, Cytopathology, and Molecular Pathology, UEGP MVZ Gießen/Wetzlar/Limburg

[3]Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany

[4]Institute of Pathology, University Hospital Charité, 10117 Berlin, Germany

[5]Institute of Pathology, University Medical Centre Mannheim, Heidelberg University, 68782 Mannheim, Germany

[6]Medical Faculty Heidelberg University, 69120 Heidelberg, Germany

[7]Molecular Pathology Trier, 54296 Trier, Germany

[8]Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany

关键词: Artificial intelligence;    deep learning;    lung cancer;    histology;    non-small cell lung cancer;    small cell lung cancer;   

DOI  :  10.3390/cancers12061604

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown