Prediction of pathological response after neoadjuvant chemotherapy using baseline FDG PET heterogeneity features in breast cancer

Abstract

Complete pathological response to neoadjuvant systemic treatment (NAST) in some subtypes of breast cancer (BC) has been used as a surrogate of long-term outcome. The possibility of predicting BC pathological response to NAST based on the baseline 18F-Fluorodeoxyglucose positron emission tomography (FDG PET), without the need of an interim study, is a focus of recent discussion. This review summarises the characteristics and results of the available studies regarding the potential impact of heterogeneity features of the primary tumour burden on baseline FDG PET in predicting pathological response to NAST in BC patients. Literature search was conducted on PubMed database and relevant data from each selected study were collected. A total of 13 studies were eligible for inclusion, all of them published over the last 5 years. Eight out of 13 analysed studies indicated an association between FDG PET-based tumour uptake heterogeneity features and prediction of response to NAST. When features associated with predicting response to NAST were derived, these varied between studies. Therefore, definitive reproducible findings across series were difficult to establish. This lack of consensus may reflect the heterogeneity and low number of included series. The clinical relevance of this topic justifies further investigation about the predictive role of baseline FDG PET.

Image quality evaluation for a clinical organ-targeted PET camera

“…Accurate quantitation of SUV holds potential for multifaceted roles in evaluating neoadjuvant treatment effectiveness with 18F-FDG PET. These include: 1) predicting pCR based on pre-treatment (baseline) FDG PET ( 32 , 33 ); 2) monitoring the decrease in FDG uptake between baseline and interim PET scans (performed during treatment cycles) as predictive of pCR ( 34 – 37 ); and 3) detecting residual primary tumors after NST or identifying exceptional responders in whom breast cancer surgery can be eliminated following NST ( 38 , 39 ). Here, we focus on selected performance indicators relevant in this context, with special attention to evaluating the recovery coefficient as a major indicator of a PET system’s capabilities for quantitative image assessment.…”

Comparison of MRI vs. [18F]FDG PET/CT for Treatment Response Evaluation of Primary Breast Cancer after Neoadjuvant Chemotherapy: Literature Review and Future Perspectives

“…Finally, several authors already investigated the potential usefulness of radiomics analysis extracted from baseline [ 18 F]FDG PET/CT prior to the start of NAC to predict both pCR and survival [ 57 , 58 , 59 ]. Despite very promising results, the main limit to the wide use of radiomics in clinical practice is related to the lack of reproducibility and standardization [ 60 ]. The training of artificial intelligence systems could represent a way to overcome these issues, although a large amount of data is needed to obtain reliable algorithms [ 61 ].…”

An Innovative Non-Linear Prediction Model for Clinical Benefit in Women with Newly Diagnosed Breast Cancer Using Baseline FDG-PET/CT and Clinical Data

“…There has been a growing inclination in recent literature toward the use of FDG-PET/CT as a non-invasive hybrid imaging tool for forecasting treatment response in women with newly diagnosed breast cancer, especially in the context of neoadjuvant systemic therapy [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. A vast majority of the existing models for treatment response prediction in breast cancer have traditionally leaned toward linear statistical methodologies, notably the Cox proportional hazards analysis.…”

“…Recent investigations have spotlighted the utility of baseline FDG-PET/CT for predicting outcomes in melanoma and non-small-cell lung cancer (NSCLC) patients [18][19][20]. In the context of breast cancer, there has also been a rising interest in recent literature in using FDG-PET/CT as hybrid imaging to predict response to treatment, particularly in the context of neoadjuvant systemic therapy [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Despite a significant heterogeneity of variables defined as outcomes, most of the published models predicting treatment response in breast cancer have predominantly relied on linear statistical approaches, such as Cox proportional hazards analysis.…”