Advances in breast cancer risk modeling: integrating clinics, imaging, pathology and artificial intelligence for personalized risk assessment

Breast cancer risk models represent the likelihood of developing breast cancer based on risk factors. They enable personalized interventions to improve screening programs. Radiologists identify mammographic density as a significant risk factor and test new imaging techniques. Pathologists provide da...

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Published inFuture oncology (London, England) Vol. 19; no. 38; pp. 2547 - 2564
Main Authors Pesapane, Filippo, Battaglia, Ottavia, Pellegrino, Giuseppe, Mangione, Elisa, Petitto, Salvatore, Fiol Manna, Eliza Del, Cazzaniga, Laura, Nicosia, Luca, Lazzeroni, Matteo, Corso, Giovanni, Fusco, Nicola, Cassano, Enrico
Format Journal Article
LanguageEnglish
Published England Future Medicine Ltd 01.12.2023
Subjects
Artificial Intelligence
breast
Breast - diagnostic imaging
Breast Neoplasms - diagnostic imaging
Breast Neoplasms - genetics
Early Detection of Cancer - methods
Female
Humans
imaging
Mammography - methods
pathology
Risk Assessment
Risk Factors
screening
women's health
risk factors
screening
pathology
breast
imaging
women's health
Online AccessGet full text
ISSN1479-6694
1744-8301
1744-8301
DOI10.2217/fon-2023-0365

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Abstract Breast cancer risk models represent the likelihood of developing breast cancer based on risk factors. They enable personalized interventions to improve screening programs. Radiologists identify mammographic density as a significant risk factor and test new imaging techniques. Pathologists provide data for risk assessment. Clinicians conduct individual risk assessments and adopt prevention strategies for high-risk subjects. Tumor genetic testing guides personalized screening and treatment decisions. Artificial intelligence (AI) in mammography integrates imaging, clinical, genetic and pathological data to develop risk models. Emerging imaging technologies, genetic testing and molecular profiling improve risk model accuracy. The complexity of the disease, limited data availability and model inputs are discussed. A multidisciplinary approach is essential for earlier detection and improved outcomes.
AbstractList Breast cancer risk models represent the likelihood of developing breast cancer based on risk factors. They enable personalized interventions to improve screening programs. Radiologists identify mammographic density as a significant risk factor and test new imaging techniques. Pathologists provide data for risk assessment. Clinicians conduct individual risk assessments and adopt prevention strategies for high-risk subjects. Tumor genetic testing guides personalized screening and treatment decisions. Artificial intelligence (AI) in mammography integrates imaging, clinical, genetic and pathological data to develop risk models. Emerging imaging technologies, genetic testing and molecular profiling improve risk model accuracy. The complexity of the disease, limited data availability and model inputs are discussed. A multidisciplinary approach is essential for earlier detection and improved outcomes.
Breast cancer risk models represent the likelihood of developing breast cancer based on risk factors. They enable personalized interventions to improve screening programs. Radiologists identify mammographic density as a significant risk factor and test new imaging techniques. Pathologists provide data for risk assessment. Clinicians conduct individual risk assessments and adopt prevention strategies for high-risk subjects. Tumor genetic testing guides personalized screening and treatment decisions. Artificial intelligence in mammography integrates imaging, clinical, genetic and pathological data to develop risk models. Emerging imaging technologies, genetic testing and molecular profiling improve risk model accuracy. The complexity of the disease, limited data availability and model inputs are discussed. A multidisciplinary approach is essential for earlier detection and improved outcomes.Breast cancer risk models represent the likelihood of developing breast cancer based on risk factors. They enable personalized interventions to improve screening programs. Radiologists identify mammographic density as a significant risk factor and test new imaging techniques. Pathologists provide data for risk assessment. Clinicians conduct individual risk assessments and adopt prevention strategies for high-risk subjects. Tumor genetic testing guides personalized screening and treatment decisions. Artificial intelligence in mammography integrates imaging, clinical, genetic and pathological data to develop risk models. Emerging imaging technologies, genetic testing and molecular profiling improve risk model accuracy. The complexity of the disease, limited data availability and model inputs are discussed. A multidisciplinary approach is essential for earlier detection and improved outcomes.
Breast cancer risk models represent the likelihood of developing breast cancer based on risk factors. They enable personalized interventions to improve screening programs. Radiologists identify mammographic density as a significant risk factor and test new imaging techniques. Pathologists provide data for risk assessment. Clinicians conduct individual risk assessments and adopt prevention strategies for high-risk subjects. Tumor genetic testing guides personalized screening and treatment decisions. Artificial intelligence in mammography integrates imaging, clinical, genetic and pathological data to develop risk models. Emerging imaging technologies, genetic testing and molecular profiling improve risk model accuracy. The complexity of the disease, limited data availability and model inputs are discussed. A multidisciplinary approach is essential for earlier detection and improved outcomes.
Author Battaglia, Ottavia
Lazzeroni, Matteo
Pesapane, Filippo
Fiol Manna, Eliza Del
Cassano, Enrico
Nicosia, Luca
Corso, Giovanni
Pellegrino, Giuseppe
Mangione, Elisa
Petitto, Salvatore
Fusco, Nicola
Cazzaniga, Laura
AuthorAffiliation 8Department of Oncology and Hemato-Oncology, University of Milan, Milan, 20141, Italy
7Department of Health Sciences, Medical Genetics, University of Milan, Milan, 20142, Italy
9European Cancer Prevention Organization (ECP), Milan, 20141, Italy
6Division of Cancer Prevention and Genetics, IEO European Institute of Oncology IRCCS, Milan, 20141, Italy
2Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Milan, 20141, Italy
3Division of Pathology, IEO European Institute of Oncology IRCCS, Milan, 20141, Italy
4School of Pathology, University of Milan, Milan, 20141, Italy
5Division of Breast Surgery, IEO European Institute of Oncology, IRCCS, Milan, 20141, Italy
1Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, 20141, Italy
AuthorAffiliation_xml – name: 5Division of Breast Surgery, IEO European Institute of Oncology, IRCCS, Milan, 20141, Italy
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– name: 8Department of Oncology and Hemato-Oncology, University of Milan, Milan, 20141, Italy
– name: 4School of Pathology, University of Milan, Milan, 20141, Italy
– name: 2Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Milan, 20141, Italy
– name: 7Department of Health Sciences, Medical Genetics, University of Milan, Milan, 20142, Italy
– name: 9European Cancer Prevention Organization (ECP), Milan, 20141, Italy
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Snippet Breast cancer risk models represent the likelihood of developing breast cancer based on risk factors. They enable personalized interventions to improve...
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SubjectTerms Artificial Intelligence
breast
Breast - diagnostic imaging
Breast Neoplasms - diagnostic imaging
Breast Neoplasms - genetics
Early Detection of Cancer - methods
Female
Humans
imaging
Mammography - methods
pathology
Risk Assessment
Risk Factors
screening
women's health
Title Advances in breast cancer risk modeling: integrating clinics, imaging, pathology and artificial intelligence for personalized risk assessment
URI http://dx.doi.org/10.2217/fon-2023-0365
https://www.ncbi.nlm.nih.gov/pubmed/38084492
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Volume 19
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