Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data
Abstract Breast cancer is a leading cause of mortality among women, with recurrence prediction remaining a significant challenge. In this context, artificial intelligence application and its resources can serve as a powerful tool in analyzing large amounts of data and predicting cancer recurrence, p...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
|
| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-01908-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823861855054987264 |
|---|---|
| author | Jaqueline Alvarenga Silveira Alexandre Ray da Silva Mariana Zuliani Theodoro de Lima |
| author_facet | Jaqueline Alvarenga Silveira Alexandre Ray da Silva Mariana Zuliani Theodoro de Lima |
| author_sort | Jaqueline Alvarenga Silveira |
| collection | DOAJ |
| description | Abstract Breast cancer is a leading cause of mortality among women, with recurrence prediction remaining a significant challenge. In this context, artificial intelligence application and its resources can serve as a powerful tool in analyzing large amounts of data and predicting cancer recurrence, potentially enabling personalized medical treatment and improving the patient’s quality of life. Thus, the systematic review examines the role of AI in predicting breast cancer recurrence using clinical data, imaging data, and combined datasets. Support Vector Machine (SVM) and Neural Networks, especially when applied to combined data, demonstrate strong potential in improving prediction accuracy. SVMs are effective with high-dimensional clinical data, while Neural Networks in genetic and molecular analysis. Despite these advancements, limitations such as dataset diversity, sample size, and evaluation standardization persist, emphasizing the need for further research. AI integration in recurrence prediction offers promising prospects for personalized care but requires rigorous validation for safe clinical application. |
| format | Article |
| id | doaj-art-efd57a00bfe948b88dc6950c12685142 |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-efd57a00bfe948b88dc6950c126851422025-02-09T12:43:35ZengSpringerDiscover Oncology2730-60112025-02-0116111610.1007/s12672-025-01908-6Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging dataJaqueline Alvarenga Silveira0Alexandre Ray da Silva1Mariana Zuliani Theodoro de Lima2OncoAI, Oncologia Inteligência ArtificialOncoAI, Oncologia Inteligência ArtificialOncoAI, Oncologia Inteligência ArtificialAbstract Breast cancer is a leading cause of mortality among women, with recurrence prediction remaining a significant challenge. In this context, artificial intelligence application and its resources can serve as a powerful tool in analyzing large amounts of data and predicting cancer recurrence, potentially enabling personalized medical treatment and improving the patient’s quality of life. Thus, the systematic review examines the role of AI in predicting breast cancer recurrence using clinical data, imaging data, and combined datasets. Support Vector Machine (SVM) and Neural Networks, especially when applied to combined data, demonstrate strong potential in improving prediction accuracy. SVMs are effective with high-dimensional clinical data, while Neural Networks in genetic and molecular analysis. Despite these advancements, limitations such as dataset diversity, sample size, and evaluation standardization persist, emphasizing the need for further research. AI integration in recurrence prediction offers promising prospects for personalized care but requires rigorous validation for safe clinical application.https://doi.org/10.1007/s12672-025-01908-6Breast cancer recurrence predictionBreast cancer survival analysis modelDeep learning in breast cancerMachine learning in breast cancer |
| spellingShingle | Jaqueline Alvarenga Silveira Alexandre Ray da Silva Mariana Zuliani Theodoro de Lima Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data Discover Oncology Breast cancer recurrence prediction Breast cancer survival analysis model Deep learning in breast cancer Machine learning in breast cancer |
| title | Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data |
| title_full | Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data |
| title_fullStr | Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data |
| title_full_unstemmed | Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data |
| title_short | Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data |
| title_sort | harnessing artificial intelligence for predicting breast cancer recurrence a systematic review of clinical and imaging data |
| topic | Breast cancer recurrence prediction Breast cancer survival analysis model Deep learning in breast cancer Machine learning in breast cancer |
| url | https://doi.org/10.1007/s12672-025-01908-6 |
| work_keys_str_mv | AT jaquelinealvarengasilveira harnessingartificialintelligenceforpredictingbreastcancerrecurrenceasystematicreviewofclinicalandimagingdata AT alexandreraydasilva harnessingartificialintelligenceforpredictingbreastcancerrecurrenceasystematicreviewofclinicalandimagingdata AT marianazulianitheodorodelima harnessingartificialintelligenceforpredictingbreastcancerrecurrenceasystematicreviewofclinicalandimagingdata |