AI-Enhanced PET and MR Imaging for Patients with Breast Cancer

Abstract

New challenges are currently faced by clinical and surgical oncologists in the management of patients with breast cancer, mainly related to the need for molecular and prognostic data. Recent technological advances in diagnostic imaging and informatics have led to the introduction of functional imaging modalities, such as hybrid PET/MR imaging, and artificial intelligence (AI) software, aimed at the extraction of quantitative radiomics data, which may reflect tumor biology and behavior. In this article, the most recent applications of radiomics and AI to PET/MR imaging are described to address the new needs of clinical and surgical oncology.

Keywords: Artificial intelligence; Breast cancer; PET/MR imaging; Radiomics.

Figure 1.

Clinical areas of interest for AI applications in breast cancer, represented by tumor characterization/molecular profiling, which, along with the preoperative assessment of axillary lymph node involvement and multigene tests, helps in defining clinical indications for neoadjuvant chemotherapy. An accurate definition of axillary status, in terms of the number of involved lymph nodes, and neoadjuvant chemotherapy allow for a more conservative surgical approach, the so called “de-escalation” treatment. One of the most promising and fascinating clinical applications of PET/MRI functional imaging coupled with AI is also the early prediction of the response to neoadjuvant chemotherapy, which would help in further selecting the ideal candidates among patients with breast cancer.

Figure 2.

Illustration of machine learning and deep learning radiomics pipelines.

Figure 3.

Examples of 2D ROI placement for the extraction of quantitative parameters (mean transit time; plasma flow; volume distribution; ADC mean; and SUVmax, mean, and minimum) (A–C), and whole tumor segmentation for radiomics features (first, second, and higher order) extraction (D–G) from primary breast cancer tumor lesions on DCE (A,E), DWI (B,F), PET (C,G), and T2‐weighted (D) images. Reprinted under a CC BY 4.0 license from: Romeo V, Kapetas P, Clauser P, Baltzer PAT, Rasul S, Gibbs P, Hacker M, Woitek R, Pinker K, Helbich TH. A Simultaneous multiparametric 18F-FDG PET/MRI Radiomics Model for the Diagnosis of Triple Negative Breast Cancer. Cancers (Basel). 2022 Aug 16;14(16):3944. doi: 10.3390/cancers14163944. PMID: 36010936; PMCID: PMC9406327.

Figure 4.

Diagram of image cropping for deep learning technique. The cubic shaped region-of-interest was selected at the largest cross-sectional area of the lesion and resized to 64 × 64 pixels. 18F-fluorodeoxyglucose (FDG) and apparent diffusion coefficient (ADC) images were obtained from positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) scans, respectively. Baseline images were defined as PET0 and ADC0, respectively, and interim images were defined as PET1 and ADC1, respectively. Reprinted under a CC BY 4.0 license from: Choi, J.H., Kim, HA., Kim, W. et al. Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning. Sci Rep 10, 21149 (2020). https://doi.org/10.1038/s41598-020-77875-5