Deep learning in detecting tumor spread through air spaces

Abstract

Lung cancer is a leading cause of death on a global scale. Professor I-Fang Chung leads a team of researchers at the Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taiwan, investigating how machine learning can help predict the risk of tumour recurrence following lung cancer surgery. The standard treatment for early-stage lung cancer patients is complete surgical resection of the tumour but disease recurrence within the first five years following surgery is common. The route of this recurrence is usually tumour spread through the air spaces (STAS) and the presence of STAS has recently been identified as a risk factor for recurrence of tumours. The researchers are exploring the importance of STAS and how targeting this phenomenon and using machine learning methods to aid in the analysis of medical imaging of diseased tissues can help in predicting the recurrence risk of post-surgery lung cancer patients. Dr Yi-Chen Yeh, a pathologist from the Taipei Veterans General Hospital, is working with Chung to employ a variety of deep-learning object detection methodologies to detect STAS in pathology images. Regions of interest (ROI) images extracted from pathology whole slide images (WSI) are marked and annotated, which provides location information for STAS. Deep learning object detection methods are used to train a model which can find STAS, then additional techniques including using pre-trained model parameters, augmenting random image data and modifying loss function are used to improve the detection rates for the model. Their model helps pathologists identify STAS and accurately predicts patient outcomes.