Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review

SIMPLE SUMMARY: Lung cancer is one of the most common and deadly malignancies worldwide. Microscopic examination of histological and cytological lung specimens can be a challenging and time-consuming process. Most of the time, accurate diagnosis and classification require histochemical and specific immunohistochemical staining. Currently, Artificial Intelligence-based methods show remarkable advances and potential in Pathology and can guide lung cancer diagnosis, subtyping, prognosis prediction, mutational status characterization, and PD-L1 expression estimation, performing with high accuracy rates. This systematic review aims to provide an overview of the current advances in Deep Learning-based methods on lung cancer by using histological and cytological images. ABSTRACT: Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists’ routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist’s routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.