An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients
Rattachement africain : it. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
BACKGROUND: Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end, the application of artificial intelligence (AI) could help clinicians to better calculate the recurrence risk and choose whether to perform adjuvant therapy. METHODS: We made use of AI to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients. In detail, we detected quantitative imaging information from H&E slides of a cohort of 71 SLN- melanoma patients, who registered at Istituto Tumori "Giovanni Paolo II" in Bari, Italy (investigational cohort, IC). For each slide, two expert pathologists firstly annotated two Regions of Interest (ROIs) containing tumor cells alone (TUMOR ROI) or with infiltrating cells (TUMOR + INF ROI). In correspondence of the two kinds of ROIs, two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided. This information was then used to predict RFS. Performances of the models were computed according to a 5-fold cross validation scheme. We further validated the prediction power of the two models on an independent external validation cohort of 23 SLN- melanoma patients (validation cohort, VC). RESULTS: The TUMOR ROIs have revealed more informative than the TUMOR + INF ROIs. An Area Under the Curve (AUC) value of 79.1% and 62.3%, a sensitivity value of 81.2% and 76.9%, a specificity value of 70.0% and 43.3%, an accuracy value of 73.2% and 53.4%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the IC cohort, respectively. An AUC value of 76.5% and 65.2%, a sensitivity value of 66.7% and 41.6%, a specificity value of 70.0% and 55.9%, an accuracy value of 70.0% and 56.5%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the VC cohort, respectively. CONCLUSIONS: Our approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé, mais le titre doit être comparé manuellement.
- Titre Crossref
- An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients
- Date Crossref
- 12/09/2024
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Istituto Tumori Bari pays non établi dans la noticeÉtablissement de santé
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University of Bari Aldo Moro pays non établi dans la noticeUniversité ou école supérieure
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Laboratorio di Biostatistica e Bioinformatica pays non établi dans la noticeInstitution
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Unità Operativa Complessa di Anatomia Patologica pays non établi dans la noticeInstitution
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Unità Operativa Tumori Rari e Melanoma pays non établi dans la noticeInstitution
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Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica pays non établi dans la noticeInstitution
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Unità Operativa Complessa di Chirurgica Plastica e Ricostruttiva pays non établi dans la noticeInstitution
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ASST dei Sette Laghi pays non établi dans la noticeInstitution
Istituto Tumori Bari, University of Bari Aldo Moro et Laboratorio di Biostatistica e Bioinformatica, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.