Michaela Cellina, Giuseppe De Padova, Nazarena Caldarelli, Dario Libri, Maurizio Cè, Carlo Martinenghi, Marco Alì, Sergio Papa, Gianpaolo Carrafiello
{"title":"Artificial Intelligence in lung cancer imaging: from data to therapy","authors":"Michaela Cellina, Giuseppe De Padova, Nazarena Caldarelli, Dario Libri, Maurizio Cè, Carlo Martinenghi, Marco Alì, Sergio Papa, Gianpaolo Carrafiello","doi":"10.1615/critrevoncog.2023050439","DOIUrl":null,"url":null,"abstract":"Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer. AI may have different potential applications in lung cancer characterization and outcome prediction. Manual segmentation is a time-consuming task, with high inter-observer variability, that can be replaced by AI-based approaches, including deep learning models such as U-Net, BCDU-Net, and others, to quantify lung nodules and cancers objectively and to extract radiomics features for the characterization of the tissue. AI models have also demonstrated their ability to predict treatment responses, such as immunotherapy and targeted therapy, by integrating radiomic features with clinical data. Additionally, AI-based prognostic models have been developed to identify patients at higher risk and personalize treatment strategies. In conclusion, this review article provides a comprehensive overview of the current state of AI applications in lung cancer management, spanning from segmentation and Virtual Biopsy to outcome prediction. The evolving role of AI in improving the precision and effectiveness of lung cancer diagnosis and treatment underscores its potential to significantly impact clinical practice and patient outcomes.","PeriodicalId":35617,"journal":{"name":"Critical Reviews in Oncogenesis","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Critical Reviews in Oncogenesis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1615/critrevoncog.2023050439","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 0
Abstract
Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer. AI may have different potential applications in lung cancer characterization and outcome prediction. Manual segmentation is a time-consuming task, with high inter-observer variability, that can be replaced by AI-based approaches, including deep learning models such as U-Net, BCDU-Net, and others, to quantify lung nodules and cancers objectively and to extract radiomics features for the characterization of the tissue. AI models have also demonstrated their ability to predict treatment responses, such as immunotherapy and targeted therapy, by integrating radiomic features with clinical data. Additionally, AI-based prognostic models have been developed to identify patients at higher risk and personalize treatment strategies. In conclusion, this review article provides a comprehensive overview of the current state of AI applications in lung cancer management, spanning from segmentation and Virtual Biopsy to outcome prediction. The evolving role of AI in improving the precision and effectiveness of lung cancer diagnosis and treatment underscores its potential to significantly impact clinical practice and patient outcomes.
期刊介绍:
The journal is dedicated to extensive reviews, minireviews, and special theme issues on topics of current interest in basic and patient-oriented cancer research. The study of systems biology of cancer with its potential for molecular level diagnostics and treatment implies competence across the sciences and an increasing necessity for cancer researchers to understand both the technology and medicine. The journal allows readers to adapt a better understanding of various fields of molecular oncology. We welcome articles on basic biological mechanisms relevant to cancer such as DNA repair, cell cycle, apoptosis, angiogenesis, tumor immunology, etc.