The state of the art for artificial intelligence in lung digital pathology

Abstract

Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)‐based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand‐crafted and deep learning‐based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID‐19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD‐L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP‐based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram

“…Currently, there are two types of artificial intelligence-based computational approaches for pathomics analysis: deep neural network-based approaches and handcrafted feature-based approaches. 18 , 44 The method used in our study was a handcrafted feature-based approach; these approaches are developed based on the close collaboration between pathologists and oncological surgeons and thus can be complex and time-consuming. 45 Deep neural network-based approaches are developed through unsupervised feature learning, which is dependent on the existence of learning sets and annotated exemplars from the categories of interest, and the network design usually focuses on fine-tuning the algorithm to maximize accuracy while minimizing processing time.…”

“… 18 However, in terms of interpretability, because they are more interpretable than deep neural network-based approaches, handcrafted feature-based approaches might be more likely to be used for high-level decision-making, such as that required for oncological prognosis; in contrast, deep neural network-based approaches might be more appropriate in situations where the need to “explain the decision” is reduced; such situations could include low-level tasks such as object detection or segmentation. 44 , 47 , 48 Considering the context of this study and that oncologists and pathologists were the primary users, we selected the handcrafted feature-based approach.…”

Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects

“…Globally, as pathology departments continue to ‘go digital’ in the upcoming years, the opportunities for computational pathology diagnostics will continuously increase. One of the main challenges with the imminent adoption of clinical computational pathology applications resides in the lack of appropriate regulatory approval pathways from agencies such as the FDA and the CE [27]. In the UK, the MHRA has announced the ‘Software and AI as a Medical Device Change Programme’ to ensure that regulatory requirements are fit for protecting patients and also encourage innovation by not burdening prospective ventures with high financial or regulatory costs.…”

Recent Advances in Pathology: the 2022 Annual Review Issue of The Journal of Pathology

“…The fourth article in this area comes from Anant Madabhushi and colleagues in Cleveland (Ohio, USA), who review the significant advances that digital pathology is providing for lung diseases [4]. These advances include the development of new AI tools for a wide spectrum of conditions such as cancer, idiopathic pulmonary fibrosis, tuberculosis, and COVID-19.…”