Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types

Authors

Cristina Renzi (ORCID: https://orcid.org/0000-0003-3845-9493)
Matthew Barclay (ORCID: https://orcid.org/0000-0003-1148-1922)
Hannah Harrison
Giovanni Emanuele Ricciardi (ORCID: https://orcid.org/0000-0002-6903-655X)
Stefania Borlini
Georgios Lyratzopoulos (ORCID: https://orcid.org/0000-0002-2873-7421)
Anna Carole D’Amelio (ORCID: https://orcid.org/0000-0003-0636-7199)
Flavia Pennisi (ORCID: https://orcid.org/0009-0001-9185-9747)
Rita Cuciniello

Institutions

Department of Health and Social Care (GB)
Vita-Salute San Raffaele University (IT)
University of Cambridge (GB)
University of Pavia (IT)
Cambridge School (PT)
University College London (GB)

Publication Details

Journal: Cancer Medicine
Published: 2025-12-01
DOI: https://doi.org/10.1002/cam4.71463
Citations: 2
Primary Topic: Artificial Intelligence in Healthcare and Education
Type: article
Field-Weighted Citation Impact: 1.31

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article

Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types

Cristina Renzi, Matthew Barclay, Hannah Harrison, Giovanni Emanuele Ricciardi et al.

Dec 1, 2025

2 citations

Cancer Medicine

Artificial Intelligence in Healthcare and Education

1.31

article

Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types

Cristina Renzi, Matthew Barclay, Hannah Harrison, Giovanni Emanuele Ricciardi, Stefania Borlini, Georgios Lyratzopoulos, Anna Carole D’Amelio, Flavia Pennisi, Rita Cuciniello

December 1, 2025article en

2 citations

Abstract

No abstract available for this paper.

Cancer MedicineVol. 14(24)

Department of Health and Social Care (GB), Vita-Salute San Raffaele University (IT), University of Cambridge (GB), University of Pavia (IT), Cambridge School (PT), University College London (GB)

Openalex Percentile: Top 13%

Artificial Intelligence in Healthcare and Education

1.31

DOI Open Access PDF OpenAlex

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