Advancing personalized oncology: a systematic review on the integration of artificial intelligence in monitoring neoadjuvant treatment for breast cancer patients. - 2024
Advancing personalized oncology: a systematic review on the integration of artificial intelligence in monitoring neoadjuvant treatment for breast cancer patients.
Artificial intelligence; Breast cancer; Machine and deep learning; Neoadjuvant treatment; Predicting complete pathological response; Humans; Female; Prognosis; Machine Learning; Medical Oncology/methods; Breast Neoplasms/drug therapy; Breast Neoplasms/therapy; Breast Neoplasms/pathology; Precision Medicine/methods; Neoadjuvant Therapy/methods; Artificial Intelligence; Breast Neoplasms; Medical Oncology; Neoadjuvant Therapy; Precision Medicine; Oncology; Genetics; Cancer Research
Abstract :
[en] [en] PURPOSE: Despite suffering from the same disease, each patient exhibits a distinct microbiological profile and variable reactivity to prescribed treatments. Most doctors typically use a standardized treatment approach for all patients suffering from a specific disease. Consequently, the challenge lies in the effectiveness of this standardized treatment and in adapting it to each individual patient. Personalized medicine is an emerging field in which doctors use diagnostic tests to identify the most effective medical treatments for each patient. Prognosis, disease monitoring, and treatment planning rely on manual, error-prone methods. Artificial intelligence (AI) uses predictive techniques capable of automating prognostic and monitoring processes, thus reducing the error rate associated with conventional methods.
METHODS: This paper conducts an analysis of current literature, encompassing the period from January 2015 to 2023, based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).
RESULTS: In assessing 25 pertinent studies concerning predicting neoadjuvant treatment (NAT) response in breast cancer (BC) patients, the studies explored various imaging modalities (Magnetic Resonance Imaging, Ultrasound, etc.), evaluating results based on accuracy, sensitivity, and area under the curve. Additionally, the technologies employed, such as machine learning (ML), deep learning (DL), statistics, and hybrid models, were scrutinized. The presentation of datasets used for predicting complete pathological response (PCR) was also considered.
CONCLUSION: This paper seeks to unveil crucial insights into the application of AI techniques in personalized oncology, particularly in the monitoring and prediction of responses to NAT for BC patients. Finally, the authors suggest avenues for future research into AI-based monitoring systems.
Disciplines :
Computer science
Author, co-author :
Hachache, Rachida; Department of Computer Sciences, LISAC Laboratory, Sidi Mohammed Ben Abdellah University, Fez, Morocco. rachida.hachache@usmba.ac.ma
Yahyaouy, Ali; Department of Computer Sciences, LISAC Laboratory, Sidi Mohammed Ben Abdellah University, Fez, Morocco ; USPN, La Maison Des Sciences Numériques, Paris, France
Riffi, Jamal; Department of Computer Sciences, LISAC Laboratory, Sidi Mohammed Ben Abdellah University, Fez, Morocco
Tairi, Hamid; Department of Computer Sciences, LISAC Laboratory, Sidi Mohammed Ben Abdellah University, Fez, Morocco
Abibou, Soukayna; Department of Computer Sciences, LISAC Laboratory, Sidi Mohammed Ben Abdellah University, Fez, Morocco
El adoui, Mohammed ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Benjelloun, Mohammed ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Language :
English
Title :
Advancing personalized oncology: a systematic review on the integration of artificial intelligence in monitoring neoadjuvant treatment for breast cancer patients.
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