Artificial Intelligence in Lung Cancer Pathology Image Analysis

• Published in: Cancers Vol. 11; no. 11; p. 1673
• Main Authors: Wang, Shidan, Yang, Donghan M., Rong, Ruichen, Zhan, Xiaowei, Fujimoto, Junya, Liu, Hongyu, Minna, John, Wistuba, Ignacio Ivan, Xie, Yang, Xiao, Guanghua
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.10.2019; MDPI
• Subjects: Algorithms; Artificial intelligence; Classification; Deep learning; Diagnosis; Image processing; Immunotherapy; Learning algorithms; Lung cancer; Lymphatic system; Lymphocytes; Medical diagnosis; Medical prognosis; Metastases; Metastasis; Morphology; Mutation; Pathology; Precision medicine; Prognosis; Review; Transfer learning; Tumor microenvironment; lung cancer; whole-slide imaging; deep learning; computer-aided diagnosis; digital pathology; pathology image
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers11111673

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.