Recent Applications of Artificial Intelligence from Histopathologic Image-Based Prediction of Microsatellite Instability in Solid Cancers: A Systematic Review

• Published in: Cancers Vol. 14; no. 11; p. 2590
• Main Authors: Alam, Mohammad Rizwan, Abdul-Ghafar, Jamshid, Yim, Kwangil, Thakur, Nishant, Lee, Sung Hak, Jang, Hyun-Jong, Jung, Chan Kwon, Chong, Yosep
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.05.2022; MDPI
• Subjects: Artificial intelligence; Cancer; Colorectal carcinoma; Datasets; Deep learning; Endometrium; Genetic aspects; Genomes; Health aspects; Identification and classification; Immunotherapy; Lymphocytes; Melanoma; Metastasis; Microsatellite instability; Mutation; Online data bases; Ovarian cancer; Pathology; Prediction models; Prognosis; Prostate; Systematic Review; South Korea; United States > US; Netherlands; deep learning; microsatellite instability; neoplasm; systematic review; artificial intelligence; whole slide images
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers14112590

Cancers with high microsatellite instability (MSI-H) have a better prognosis and respond well to immunotherapy. However, MSI is not tested in all cancers because of the additional costs and time of diagnosis. Therefore, artificial intelligence (AI)-based models have been recently developed to evaluate MSI from whole slide images (WSIs). Here, we aimed to assess the current state of AI application to predict MSI based on WSIs analysis in MSI-related cancers and suggest a better study design for future studies. Studies were searched in online databases and screened by reference type, and only the full texts of eligible studies were reviewed. The included 14 studies were published between 2018 and 2021, and most of the publications were from developed countries. The commonly used dataset is The Cancer Genome Atlas dataset. Colorectal cancer (CRC) was the most common type of cancer studied, followed by endometrial, gastric, and ovarian cancers. The AI models have shown the potential to predict MSI with the highest AUC of 0.93 in the case of CRC. The relatively limited scale of datasets and lack of external validation were the limitations of most studies. Future studies with larger datasets are required to implicate AI models in routine diagnostic practice for MSI prediction.