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.
Highlights
Lung cancer is the leading cause of death from cancer in the United States and around the world [1,2,3]
non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer cases and can be further separated into lung adenocarcinoma (ADC), squamous cell carcinoma (SCC) lung cancer, and large cell lung cancer
Traditional image processing methods involve feature definition, feature extraction, and/or segmentation. These methods have been applied to segment lymphocytes and to analyze the spatial organization of tumor-infiltrating lymphocytes (TIL) [39] and stromal cells [9] in the Tumor Microenvironment (TME), quantitative characterization of lung cancer TME remains challenging for the following reasons: (1) The composition of lung cancer TME is complex and heterogeneous: In addition to the aforementioned cell types, other structures, including the bronchus, cartilage, and pleura, are often found in lung pathology slides
Summary
Lung cancer is the leading cause of death from cancer in the United States and around the world [1,2,3]. It requires the pathologist to recognize subtle histopathological patterns in the highly complex tissue images This process is time-consuming, subjective, and generates considerable inter- and intra-observer. Recent developments in pathology image analysis [7,8] have led to new algorithms and software tools for clinical diagnosis and research into disease mechanisms. Computer algorithms for pathology image analysis have been developed to facilitate cancer diagnosis [11,12,13,14,15], grading [16,17,18,19,20], and prognosis [21,22]. We discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer. We summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis
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