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Review
. 2024 Apr 24:12:100561.
doi: 10.1016/j.ejro.2024.100561. eCollection 2024 Jun.

Machine learning approaches in the prediction of positive axillary lymph nodes post neoadjuvant chemotherapy using MRI, CT, or ultrasound: A systematic review

Shirin Yaghoobpoor  1   2 , Mobina Fathi  1   2 , Hamed Ghorani  1   3 , Parya Valizadeh  1   4 , Payam Jannatdoust  1   4 , Arian Tavasol  1   2 , Melika Zarei  1   5 , Arvin Arian  1   6
Affiliations
Review

Machine learning approaches in the prediction of positive axillary lymph nodes post neoadjuvant chemotherapy using MRI, CT, or ultrasound: A systematic review

Shirin Yaghoobpoor et al. Eur J Radiol Open. .

Abstract

Background and objective: Neoadjuvant chemotherapy is a standard treatment approach for locally advanced breast cancer. Conventional imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound, have been used for axillary lymph node evaluation which is crucial for treatment planning and prognostication. This systematic review aims to comprehensively examine the current research on applying machine learning algorithms for predicting positive axillary lymph nodes following neoadjuvant chemotherapy utilizing imaging modalities, including MRI, CT, and ultrasound.

Methods: A systematic search was conducted across databases, including PubMed, Scopus, and Web of Science, to identify relevant studies published up to December 2023. Articles employing machine learning algorithms to predict positive axillary lymph nodes using MRI, CT, or ultrasound data after neoadjuvant chemotherapy were included. The review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, encompassing data extraction and quality assessment.

Results: Seven studies were included, comprising 1502 patients. Four studies used MRI, two used CT, and one applied ultrasound. Two studies developed deep-learning models, while five used classic machine-learning models mainly based on multiple regression. Across the studies, the models showed high predictive accuracy, with the best-performing models combining radiomics and clinical data.

Conclusion: This systematic review demonstrated the potential of utilizing advanced data analysis techniques, such as deep learning radiomics, in improving the prediction of positive axillary lymph nodes in breast cancer patients following neoadjuvant chemotherapy.

Keywords: Axillary lymph nodes; CT; MRI; Machine learning; Neoadjuvant chemotherapy; Ultrasound.

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Conflict of interest statement

Authors declare no conflict of interests.

Figures

Fig. 1
Fig. 1
PRISMA flowchart of the medical database search strategy.
Fig. 2
Fig. 2
Methodological quality analysis of the included studies through QUADAS-2. Green indicates low, yellow unclear, and red high risk of bias.
See this image and copyright information in PMC

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