Melanoma Clinical Decision Support System: An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients
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, enab...
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| Veröffentlicht in: | Cancers 2023-04, Vol.15 (7), p.2174 |
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| creator | Diaz-Ramón, Jose Luis Gardeazabal, Jesus Izu, Rosa Maria Garrote, Estibaliz Rasero, Javier Apraiz, Aintzane Penas, Cristina Seijo, Sandra Lopez-Saratxaga, Cristina De la Peña, Pedro Maria Sanchez-Diaz, Ana Cancho-Galan, Goikoane Velasco, Veronica Sevilla, Arrate Fernandez, David Cuenca, Iciar Cortes, Jesus María Alonso, Santos Asumendi, Aintzane Boyano, María Dolores |
| description | 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. |
| doi_str_mv | 10.3390/cancers15072174 |
| format | Article |
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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.</description><identifier>ISSN: 2072-6694</identifier><identifier>EISSN: 2072-6694</identifier><identifier>DOI: 10.3390/cancers15072174</identifier><identifier>PMID: 37046835</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Algorithms ; Artificial intelligence ; Bcl-2 protein ; Biomarkers ; Biopsy ; Care and treatment ; Clinical decision making ; Colony-stimulating factor ; Cytokines ; Decision support systems ; Deep learning ; Dermatology ; Diagnosis ; Disease prevention ; Granulocyte-macrophage colony-stimulating factor ; Health aspects ; Image processing ; Immunohistochemistry ; Interleukin 10 ; Interleukin 4 ; Interleukin 6 ; Lymphocytes ; Medical prognosis ; Melanoma ; Metastases ; Metastasis ; Patient outcomes ; Patients ; Prognosis ; Serology ; Serum levels ; Skin cancer ; Skin diseases ; Skin lesions ; Surgery ; γ-Interferon</subject><ispartof>Cancers, 2023-04, Vol.15 (7), p.2174</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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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.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Bcl-2 protein</subject><subject>Biomarkers</subject><subject>Biopsy</subject><subject>Care and treatment</subject><subject>Clinical decision making</subject><subject>Colony-stimulating factor</subject><subject>Cytokines</subject><subject>Decision support systems</subject><subject>Deep learning</subject><subject>Dermatology</subject><subject>Diagnosis</subject><subject>Disease prevention</subject><subject>Granulocyte-macrophage colony-stimulating factor</subject><subject>Health aspects</subject><subject>Image processing</subject><subject>Immunohistochemistry</subject><subject>Interleukin 10</subject><subject>Interleukin 4</subject><subject>Interleukin 6</subject><subject>Lymphocytes</subject><subject>Medical prognosis</subject><subject>Melanoma</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Patient outcomes</subject><subject>Patients</subject><subject>Prognosis</subject><subject>Serology</subject><subject>Serum levels</subject><subject>Skin cancer</subject><subject>Skin diseases</subject><subject>Skin 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Clinical Decision Support System: An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients</title><author>Diaz-Ramón, Jose Luis ; Gardeazabal, Jesus ; Izu, Rosa Maria ; Garrote, Estibaliz ; Rasero, Javier ; Apraiz, Aintzane ; Penas, Cristina ; Seijo, Sandra ; Lopez-Saratxaga, Cristina ; De la Peña, Pedro Maria ; Sanchez-Diaz, Ana ; Cancho-Galan, Goikoane ; Velasco, Veronica ; Sevilla, Arrate ; Fernandez, David ; Cuenca, Iciar ; Cortes, Jesus María ; Alonso, Santos ; Asumendi, Aintzane ; Boyano, María Dolores</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c489t-844c563ba9f898eb7ab576c873638aef8e5e90c277627bffb69c4ab838418b5d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Bcl-2 protein</topic><topic>Biomarkers</topic><topic>Biopsy</topic><topic>Care and 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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. 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| fulltext | fulltext |
| identifier | ISSN: 2072-6694 |
| ispartof | Cancers, 2023-04, Vol.15 (7), p.2174 |
| issn | 2072-6694 2072-6694 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10093614 |
| source | PubMed Central Open Access; MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Algorithms Artificial intelligence Bcl-2 protein Biomarkers Biopsy Care and treatment Clinical decision making Colony-stimulating factor Cytokines Decision support systems Deep learning Dermatology Diagnosis Disease prevention Granulocyte-macrophage colony-stimulating factor Health aspects Image processing Immunohistochemistry Interleukin 10 Interleukin 4 Interleukin 6 Lymphocytes Medical prognosis Melanoma Metastases Metastasis Patient outcomes Patients Prognosis Serology Serum levels Skin cancer Skin diseases Skin lesions Surgery γ-Interferon |
| title | Melanoma Clinical Decision Support System: An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T11%3A20%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Melanoma%20Clinical%20Decision%20Support%20System:%20An%20Artificial%20Intelligence-Based%20Tool%20to%20Diagnose%20and%20Predict%20Disease%20Outcome%20in%20Early-Stage%20Melanoma%20Patients&rft.jtitle=Cancers&rft.au=Diaz-Ram%C3%B3n,%20Jose%20Luis&rft.date=2023-04-06&rft.volume=15&rft.issue=7&rft.spage=2174&rft.pages=2174-&rft.issn=2072-6694&rft.eissn=2072-6694&rft_id=info:doi/10.3390/cancers15072174&rft_dat=%3Cgale_pubme%3EA746926364%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2799556711&rft_id=info:pmid/37046835&rft_galeid=A746926364&rfr_iscdi=true |