Abstract 4447: Deep neural network for automatic histopathologic analysis of murine lung tumors
Authors: Westcott Peter Maxwell Kienitz , Pitkänen Tuomas , Blom Sami , Westerling Thomas , Ropponen Tuomas , Sacks Nathan , Wu Katherine , Bronson Roderick , Tammela Tuomas , Jacks Tyler 
Published: August 2019

Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide, and genetically-engineered mouse models (GEMMs) of cancer provide important mechanistic and preclinical insights into this deadly disease. In particular, the “KP” model enables lung-specific inducible activation of oncogenic Kras G12D, and loss of Trp53, the two most common driver events of human non-small cell lung cancer (NSCLC). Importantly, the KP model is widely used and faithfully recapitulates molecular and histopathological features of the human disease, including progression from early hyperplasia and adenoma to invasive adenocarcinoma. However, the KP model results in multi-focal and heterogeneous tumor burden, and there is a need for improved tools to increase throughput and decrease subjectivity of tumor burden quantification and histopathological analyses.
To this end, we trained a convolutional neural network (CNN) for semantic multi-class segmentation using the Aiforia(R) platform. The CNN was trained to classify and detect lung parenchyma, NSCLC tumors, and NSCLC tumor grades (grade 1-4). For supervised training, we used selected areas from 93 hematoxylin and eosin stained slides. For validation, we analyzed 34 slides completely independent of the CNN training. Tumor grades were manually annotated on the validation slides blinded to the CNN results. The overall F1 score of the CNN in grade classification was 98% and total area error 0.3%. The grade-specific F1-scores were 89%, 97%, 99%, and 98% for grades 1, 2, 3, and 4, respectively. Corresponding grade-specific total area errors were 0.4%, 0.2%, 0.4%, and 0.1%. Manual scoring independent of the training and CNN yielded similar tumor burden and grading results. In addition, the algorithm accurately recapitulates the increased tumor burden and grade seen in KP tumors harboring additional mutation of the tumor suppressor Keap1, and the delayed kinetics of KP tumors harboring a strong T cell antigen, in independent datasets. We have also extended this methodology to identification of tumors in a GEMM of small cell lung cancer, a distinct class of lung cancer with poor prognosis.
In conclusion, we demonstrate that deep neural networks can be used for automated analysis and grading of preclinical models of lung cancer. We anticipate that this powerful technology will increase the throughput, sensitivity and reproducibility of hypothesis-driven studies of factors influencing tumor progression and immune response in mouse models of lung cancer.
Citation Format: Peter Maxwell Kienitz Westcott, Tuomas Pitkänen, Sami Blom, Thomas Westerling, Tuomas Ropponen, Nathan Sacks, Katherine Wu, Roderick Bronson, Tuomas Tammela, Tyler Jacks. Deep neural network for automatic histopathologic analysis of murine lung tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4447.