Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis - Yuan, Lei; Shen, Zhiming; Shan, Yibo; Zhu, Jianwei; Wang, Qi; Lu, Yi; Shi, Hongcan (Frontiers in Oncology, 2024)
Background: Pathomics has emerged as a promising biomarker that could facilitate personalized immunotherapy in lung cancer. It is essential to elucidate the global research trends and emerging prospects in this domain. Methods: The annual distribution, journals, authors, countries, institutions, and keywords of articles published between 2018 and 2023 were visualized and analyzed using CiteSpace and other bibliometric tools. Results: A total of 109 relevant articles or reviews were included, demonstrating an overall upward trend; The terms "deep learning", "tumor microenvironment", "biomarkers", "image analysis", "immunotherapy", and "survival prediction", etc. are hot keywords in this field.In future research endeavors, advanced methodologies involving artificial intelligence and pathomics will be deployed for the digital analysis of tumor tissues and the tumor microenvironment in lung cancer patients, leveraging histopathological tissue sections. Through the integration of comprehensive multi-omics data, this strategy aims to enhance the depth of assessment, characterization, and understanding of the tumor microenvironment, thereby elucidating a broader spectrum of tumor features. Consequently, the development of a multimodal fusion model will ensue, enabling precise evaluation of personalized immunotherapy efficacy and prognosis for lung cancer patients, potentially establishing a pivotal frontier in this domain of investigation.