Multinomial Classification of Patterns in Lung Cancer Biopsy Slides Using Customized Convolutional Neural Network

Abstract

\n Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackles this issue would be helpful for diagnoses and subtyping of lung carcinoma. In this study, we developed AI models to classify multinomial patterns of lung carcinoma (adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell neuroendocrine carcinoma) and non-neoplastic lung tissue based on convolutional neural networks (CNN or ConvNet). Four CNNs that were pre-trained using transfer learning and one CNN built from scratch were used to classify patch images from pathology whole-slide images (WSIs). We evaluated the diagnostic performance of each model in the test sets. The Xception model achieved the highest performance among pre-trained CNNs with an accuracy of 0.86 and an area under the curve (AUC) of 0.97. The built from scratch CNN model obtained an accuracy of 0.92 and an AUC ranging from 0.99 to 1.00 for subtyping lung carcinoma tasks. These results demonstrate how promising CNN models are for developing improved diagnostic workflow systems for diagnosis and subtyping of lung carcinoma. Of particular note is the fact that the built from scratch CNN described in this paper achieves prompt and consistent results so has the potential to be applied in working hospitals for pathological diagnoses.