Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/22/3483 |
| _version_ | 1797459603994181632 |
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| author | Hayley Higgins Abanoub Nakhla Andrew Lotfalla David Khalil Parth Doshi Vandan Thakkar Dorsa Shirini Maria Bebawy Samy Ammari Egesta Lopci Lawrence H. Schwartz Michael Postow Laurent Dercle |
| author_facet | Hayley Higgins Abanoub Nakhla Andrew Lotfalla David Khalil Parth Doshi Vandan Thakkar Dorsa Shirini Maria Bebawy Samy Ammari Egesta Lopci Lawrence H. Schwartz Michael Postow Laurent Dercle |
| author_sort | Hayley Higgins |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:53:45Z |
| format | Article |
| id | doaj.art-648f7bf2dccc420ea0c062b058051239 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T16:53:45Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-648f7bf2dccc420ea0c062b0580512392023-11-24T14:37:48ZengMDPI AGDiagnostics2075-44182023-11-011322348310.3390/diagnostics13223483Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous MelanomaHayley Higgins0Abanoub Nakhla1Andrew Lotfalla2David Khalil3Parth Doshi4Vandan Thakkar5Dorsa Shirini6Maria Bebawy7Samy Ammari8Egesta Lopci9Lawrence H. Schwartz10Michael Postow11Laurent Dercle12Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USADepartment of Clinical Medicine, American University of the Caribbean School of Medicine, 33027 Cupecoy, Sint Maarten, The NetherlandsDepartment of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USADepartment of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USADepartment of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USADepartment of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USADepartment of Radiology, Shahid Beheshti University of Medical Sciences, Tehran 1981619573, IranDepartment of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USADépartement d’Imagerie Médicale Biomaps, UMR1281 INSERM, CEA, CNRS, Gustave Roussy, Université Paris-Saclay, 94800 Villejuif, FranceNuclear Medicine Unit, IRCCS Humanitas Research Hospital, 20089 Rozzano, ItalyDepartment of Radiology, New York-Presbyterian, Columbia University Irving Medical Center, New York, NY 10032, USAMelanoma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USADepartment of Radiology, Shahid Beheshti University of Medical Sciences, Tehran 1981619573, IranStandard-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.https://www.mdpi.com/2075-4418/13/22/3483metastatic melanomaartificial intelligenceimmunotherapyradiology |
| spellingShingle | Hayley Higgins Abanoub Nakhla Andrew Lotfalla David Khalil Parth Doshi Vandan Thakkar Dorsa Shirini Maria Bebawy Samy Ammari Egesta Lopci Lawrence H. Schwartz Michael Postow Laurent Dercle Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma Diagnostics metastatic melanoma artificial intelligence immunotherapy radiology |
| title | Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma |
| title_full | Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma |
| title_fullStr | Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma |
| title_full_unstemmed | Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma |
| title_short | Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma |
| title_sort | recent advances in the field of artificial intelligence for precision medicine in patients with a diagnosis of metastatic cutaneous melanoma |
| topic | metastatic melanoma artificial intelligence immunotherapy radiology |
| url | https://www.mdpi.com/2075-4418/13/22/3483 |
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