Artificial Intelligence in Lung Cancer Pathology Image Analysis

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.

Predictive molecular pathology in non–small cell lung cancer in France: The past, the present and the perspectives

“…Finally, other factors may influence the frequency with which these analyses are performed, including new approaches using artificial intelligence or innovative treatment strategies in oncology-including the use of chimeric antigen receptor T cells or agents targeting cancer stem cell pathways for cancer therapy. [54][55][56][57]…”

Introduction to digital pathology and computer-aided pathology

“…WSI scanners differ with respect to their functionality and features, and most image viewers are provided by scanner vendors [4]. When selecting a WSI scanner for clinical diagnosis, it is important to consider the following factors: (1) volume of slides, (2) type of specimen (eg, tissue section slides, cytology slides, or hematopathology smears), (3) feasibility of z-stack scanning (focus stacking), (4) laboratory needs for oil-immersion scanning, (5) laboratory needs for both bright field and fluorescence scanning, (6) type of glass slides (e.g., wet slides, unusual size), (7) slide barcode readability, (8) existing space constraints in the laboratory, (9) functionality of image viewer and management system provided by vendor, (10) bidirectional integration with existing information systems, (11) communication protocol (e.g., XML, HL7) between DPS and LIS, (12) whether image viewer software is installed on the server or on the local hard disk of each client workstation, (13) whether the viewer works on mobile devices, and (14) open or closed system.…”

“…CNN is classified as a DL algorithm that is most commonly applied to image analysis [11]. Successful computer-aided pathologic diagnostic tools are being actively devised using AI techniques, particularly DL models [12].…”

Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning

“…With tremendous success in recognition of natural images, convolutional neural networks have been quickly adopted for medical image analysis [7]. Therefore, analysis of pathology images using artificial intelligence (AI) is becoming more feasible recently [8][9][10][11][12][13][14][15][16][17][18][19]. Of note, a few articles described the success of AI in identifying metastatic breast cancer in lymph nodes [10,[12][13][14]16,19].…”