Artificial intelligence in lung cancer pathology image analysis

Shidan Wang, Donghan M. Yang, Ruichen Rong, Xiaowei Zhan, Junya Fujimoto, Hongyu Liu, John Minna, Ignacio Ivan Wistuba, Yang Xie, Guanghua Xiao

Research output: Contribution to journalReview articlepeer-review

14 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number1673
JournalCancers
Volume11
Issue number11
DOIs
StatePublished - Nov 2019

Keywords

  • Computer-aided diagnosis
  • Deep learning
  • Digital pathology
  • Lung cancer
  • Pathology image
  • Whole-slide imaging

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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