RT Journal Article
T1 Melanoma Clinical Decision Support System: 												An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients
A1 Diaz-Ramón, Jose Luis
A1 Gardeazabal, Jesus
A1 Izu, Rosa Maria
A1 Garrote, Estibaliz
A1 Rasero, Javier
A1 Apraiz, Aintzane
A1 Penas, Cristina
A1 Seijo, Sandra
A1 Lopez-Saratxaga, Cristina
A1 De la Peña, Pedro Maria
A1 Sanchez-Diaz, Ana
A1 Cancho-Galan, Goikoane
A1 Velasco, Veronica
A1 Sevilla, Arrate
A1 Fernandez, David
A1 Cuenca, Iciar
A1 Cortes, Jesus María
A1 Alonso, Santos
A1 Asumendi, Aintzane
A1 Boyano, María Dolores
AB This study set out to assess the performance of an artificial intelligence (AI) algorithm based on clinical data and dermatoscopic imaging for the early diagnosis of melanoma, and its capacity to define the metastatic progression of melanoma through serological and histopathological biomarkers, enabling dermatologists to make more informed decisions about patient management. Integrated analysis of demographic data, images of the skin lesions, and serum and histopathological markers were analyzed in a group of 196 patients with melanoma. The interleukins (ILs) IL-4, IL-6, IL-10, and IL-17A as well as IFNγ (interferon), GM-CSF (granulocyte and macrophage colony-stimulating factor), TGFβ (transforming growth factor), and the protein DCD (dermcidin) were quantified in the serum of melanoma patients at the time of diagnosis, and the expression of the RKIP, PIRIN, BCL2, BCL3, MITF, and ANXA5 proteins was detected by immunohistochemistry (IHC) in melanoma biopsies. An AI algorithm was used to improve the early diagnosis of melanoma and to predict the risk of metastasis and of disease-free survival. Two models were obtained to predict metastasis (including “all patients” or only patients “at early stages of melanoma”), and a series of attributes were seen to predict the progression of metastasis: Breslow thickness, infiltrating BCL-2 expressing lymphocytes, and IL-4 and IL-6 serum levels. Importantly, a decrease in serum GM-CSF seems to be a marker of poor prognosis in patients with early-stage melanomas.
SN 2072-6694
YR 2023
FD 2023-04
LK https://hdl.handle.net/11556/3184
UL https://hdl.handle.net/11556/3184
LA eng
NO Diaz-Ramón , J L , Gardeazabal , J , Izu , R M , Garrote , E , Rasero , J , Apraiz , A , Penas , C , Seijo , S , Lopez-Saratxaga , C , De la Peña , P M , Sanchez-Diaz , A , Cancho-Galan , G , Velasco , V , Sevilla , A , Fernandez , D , Cuenca , I , Cortes , J M , Alonso , S , Asumendi , A & Boyano , M D 2023 , ' Melanoma Clinical Decision Support System : An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients ' , Cancers , vol. 15 , no. 7 , 2174 . https://doi.org/10.3390/cancers15072174
NO Publisher Copyright: © 2023 by the authors.
NO This project was supported by grants to M.D.B. from the Basque Government (KK2017-041 and KK2020-00069); UPV/EHU (GIU17/066); H2020-ESCEL JTI (15/01); and MINECO (PCIN-2015-241) and to SA from Basque Government (IT693-22). CP holds a predoctoral fellowship from the Basque Government.
DS TECNALIA Publications
RD 26 jul 2024