التفاصيل البيبلوغرافية
| العنوان: |
Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. |
| المؤلفون: |
Prelaj, A.1,2,3 (AUTHOR) arsela.prelaj@istitutotumori.mi.it, Miskovic, V.1,2 (AUTHOR), Zanitti, M.4 (AUTHOR), Trovo, F.2 (AUTHOR), Genova, C.5,6 (AUTHOR), Viscardi, G.7 (AUTHOR), Rebuzzi, S.E.6,8 (AUTHOR), Mazzeo, L.1,2 (AUTHOR), Provenzano, L.1 (AUTHOR), Kosta, S.4 (AUTHOR), Favali, M.2 (AUTHOR), Spagnoletti, A.1 (AUTHOR), Castelo-Branco, L.9,10 (AUTHOR), Dolezal, J.11 (AUTHOR), Pearson, A.T.11 (AUTHOR), Lo Russo, G.1 (AUTHOR), Proto, C.1 (AUTHOR), Ganzinelli, M.1 (AUTHOR), Giani, C.1 (AUTHOR), Ambrosini, E.2 (AUTHOR) |
| المصدر: |
Annals of Oncology. Jan2024, Vol. 35 Issue 1, p29-65. 37p. |
| مصطلحات موضوعية: |
*ARTIFICIAL intelligence, *IMMUNE checkpoint inhibitors, *NON-small-cell lung carcinoma, *MACHINE learning, *BIOMARKERS |
| مستخلص: |
The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice. • ML and DL (AI techniques) have been used to analyse big genomic and proteomic data in immuno-oncology. • This systematic review highlights the potential of AI in identifying new biomarkers that can personalise immunotherapy. • AI algorithms are promising in optimising treatment selection, and improving overall patient outcomes. • The article emphasises the need for further research and validation of the AI algorithm to switch from research to clinic. • Trustworthy explainable AI can be of great utility by providing explanations of the algorithms and discovery of biomarkers. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
Academic Search Index |
