Recent advances in the field of artificial intelligence for precision medicine in patients with a diagnosis of metastatic cutaneous melanoma Review
| Authors: | Higgins, H.; Nakhla, A.; Lotfalla, A.; Khalil, D.; Doshi, P.; Thakkar, V.; Shirini, D.; Bebawy, M.; Ammari, S.; Lopci, E.; Schwartz, L. H.; Postow, M.; Dercle, L. |
| Review Title: | Recent advances in the field of artificial intelligence for precision medicine in patients with a diagnosis of metastatic cutaneous melanoma |
| Abstract: | Standard-of-care medical imaging techniques such as CT, MRI, and PET play a critical role in managing patients diagnosed with metastatic cutaneous melanoma. Advancements in artificial intelligence (AI) techniques, such as radiomics, machine learning, and deep learning, could revolutionize the use of medical imaging by enhancing individualized image-guided precision medicine approaches. In the present article, we will decipher how AI/radiomics could mine information from medical images, such as tumor volume, heterogeneity, and shape, to provide insights into cancer biology that can be leveraged by clinicians to improve patient care both in the clinic and in clinical trials. More specifically, we will detail the potential role of AI in enhancing detection/diagnosis, staging, treatment planning, treatment delivery, response assessment, treatment toxicity assessment, and monitoring of patients diagnosed with metastatic cutaneous melanoma. Finally, we will explore how these proof-of-concept results can be translated from bench to bedside by describing how the implementation of AI techniques can be standardized for routine adoption in clinical settings worldwide to predict outcomes with great accuracy, reproducibility, and generalizability in patients diagnosed with metastatic cutaneous melanoma. © 2023 by the authors. |
| Keywords: | treatment response; review; treatment planning; cancer radiotherapy; cancer staging; nuclear magnetic resonance imaging; outcome assessment; positron emission tomography; cancer diagnosis; medical decision making; ipilimumab; cancer immunotherapy; computer assisted tomography; image analysis; tumor volume; clinical assessment; patient monitoring; diagnostic imaging; information processing; cancer therapy; cell heterogeneity; standardization; patient care; radiology; immunotherapy; artificial intelligence; molecular biology; health care delivery; cutaneous melanoma; personalized medicine; disease surveillance; metastatic melanoma; clinical trial (topic); b raf kinase inhibitor; single photon emission computed tomography; proof of concept; mitogen activated protein kinase kinase inhibitor; immune checkpoint inhibitor; cancer prognosis; nivolumab; human; pembrolizumab; radiomics; relatlimab |
| Journal Title: | Diagnostics |
| Volume: | 13 |
| Issue: | 22 |
| ISSN: | 2075-4418 |
| Publisher: | MDPI |
| Date Published: | 2023-11-02 |
| Start Page: | 3483 |
| Language: | English |
| DOI: | 10.3390/diagnostics13223483 |
| PROVIDER: | scopus |
| PMCID: | PMC10670510 |
| PUBMED: | 37998619 |
| DOI/URL: | |
| Notes: | Review -- Source: Scopus |
