TY - JOUR
T1 - Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma
AU - Higgins, Hayley
AU - Nakhla, Abanoub
AU - Lotfalla, Andrew
AU - Khalil, David
AU - Doshi, Parth
AU - Thakkar, Vandan
AU - Shirini, Dorsa
AU - Bebawy, Maria
AU - Ammari, Samy
AU - Lopci, Egesta
AU - Schwartz, Lawrence H.
AU - Postow, Michael
AU - Dercle, Laurent
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - immunotherapy
KW - metastatic melanoma
KW - radiology
UR - http://www.scopus.com/inward/record.url?scp=85178366970&partnerID=8YFLogxK
U2 - 10.3390/diagnostics13223483
DO - 10.3390/diagnostics13223483
M3 - Review article
AN - SCOPUS:85178366970
SN - 2075-4418
VL - 13
JO - Diagnostics
JF - Diagnostics
IS - 22
M1 - 3483
ER -