The role of basal 18F-FDG PET/CT maximum standard uptake value and maximum standard uptake change in predicting pathological response in breast cancer patients receiving neoadjuvant chemotherapy

Innovations in Positron Emission Tomography and State of the Art in the Evaluation of Breast Cancer Treatment Response

The advent of hybrid Positron Emission Tomography/Computed Tomography (PET/CT) and PET/Magnetic Resonance Imaging (MRI) scanners resulted in an increased clinical relevance of nuclear medicine in oncology. The use of [18F]-Fluorodeoxyglucose ([18F]FDG) has also made it possible to study tumors (including breast cancer) from not only a dimensional perspective but also from a metabolic point of view. In particular, the use of [18F]FDG PET allowed early confirmation of the efficacy or failure of therapy. The purpose of this review was to assess the literature concerning the response to various therapies for different subtypes of breast cancer through PET. We start by summarizing studies that investigate the validation of PET/CT for the assessment of the response to therapy in breast cancer; then, we present studies that compare PET imaging (including PET devices dedicated to the breast) with CT and MRI, focusing on the identification of the most useful parameters obtainable from PET/CT. We also focus on novel non-FDG radiotracers, as they allow for the acquisition of information on specific aspects of the new therapies.

Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT

OBJECTIVES

The aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after Neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC).

INTRODUCTION

NAC is the standard treatment for locally advanced breast cancer (LABC). Pathological complete response (pCR) after NAC is considered a good predictor of disease-free survival (DFS) and overall survival (OS).Therefore, there is a need to develop methods that can predict the pCR at the time of diagnosis.

METHODS

This article was designed as a retrospective chart study.For the convolutional neural network model, a total of 355 PET/CT images of 31 patients were used. All patients had primary breast surgery after completing NAC.

RESULTS

Pathological complete response was obtained in a total of 9 patients. The study results show that our proposed deep convolutional neural networks model achieved a remarkable success with an accuracy of 84.79% to predict pathological complete response.

CONCLUSION

It was concluded that deep learning methods can predict breast cancer treatment.

The influence of receptor expression and clinical subtypes on baseline [18F]FDG uptake in breast cancer: systematic review and meta-analysis

BACKGROUND

To quantify the relationship between [18F]FDG uptake of the primary tumour measured by PET-imaging with immunohistochemical (IHC) expression of ER, PR, HER2, Ki-67, and clinical subtypes based on these markers in breast cancer patients.

METHODS

PubMed and Embase were searched for studies that compared SUVmax between breast cancer patients negative and positive for IHC expression of ER, PR, HER2, Ki-67, and clinical subtypes based on these markers. Two reviewers independently screened the studies and extracted the data. Standardized mean differences (SMD) and 95% confidence intervals (CIs) were estimated by using DerSimonian-Laird random-effects models. P values less than or equal to 5% indicated statistically significant results.

RESULTS

Fifty studies were included in the final analysis. SUVmax is significantly higher in ER-negative (31 studies, SMD 0.66, 0.56-0.77, P < 0.0001), PR-negative (30 studies, SMD 0.56; 0.40-0.71, P < 0.0001), HER2-positive (32 studies, SMD - 0.29, - 0.49 to - 0.10, P = 0.0043) or Ki-67-positive (19 studies, SMD - 0.77; - 0.93 to - 0.61, P < 0.0001) primary tumours compared to their counterparts. The majority of clinical subtypes were either luminal A (LA), luminal B (LB), HER2-positive or triple negative breast cancer (TNBC). LA is associated with significantly lower SUVmax compared to LB (11 studies, SMD - 0.49, - 0.68 to - 0.31, P = 0.0001), HER2-positive (15 studies, SMD - 0.91, - 1.21 to - 0.61, P < 0.0001) and TNBC (17 studies, SMD - 1.21, - 1.57 to - 0.85, P < 0.0001); and LB showed significantly lower uptake compared to TNBC (10 studies, SMD - 0.77, - 1.05 to - 0.49, P = 0.0002). Differences in SUVmax between LB and HER2-positive (9 studies, SMD - 0.32, - 0.88 to 0.24, P = 0.2244), and HER2-positive and TNBC (17 studies, SMD - 0.29, - 0.61 to 0.02, P = 0.0667) are not significant.

CONCLUSION

Primary tumour SUVmax is significantly higher in ER-negative, PR-negative, HER2-positive and Ki-67-positive breast cancer patients. Luminal tumours have the lowest and TNBC tumours the highest SUVmax. HER2 overexpression has an intermediate effect.

Effect of radioactivity outside the field of view on image quality of dedicated breast positron emission tomography: preliminary phantom and clinical studies

OBJECTIVE

Semi-quantitative positron emission tomography (PET) values, such as the maximum standardized uptake value (SUV_max), are widely used to identify malignant lesions and evaluate the response to treatment. The image quality of ring-shaped dedicated breast positron emission tomography (dbPET) has been known to decrease the closer it is to the detector's edge. This study aimed to investigate the effect of radioactivity (RI) outside the detector field of view (FOV) on the image quality of the ring-shaped dbPET.

METHODS

A breast phantom containing the left myocardium, which was prepared using a 3D printer, filled with ¹⁸F-fluorodeoxyglucose (FDG) solution with various RI concentration ratios (RCRs) of myocardium to background and scanned with the edge of an apex positioned exactly in line with the edge of the FOV of the dbPET scanner. The phantom image quality was visually and quantitatively evaluated. Following the phantom study, left-right breast differences (the left breast uptake ratio to the right breast (LUR)) on clinical dbPET images of 74 women were quantitatively evaluated. The relationships between these parameters, clinical indices, and FDG uptake in the left myocardium on PET/computed tomography (CT) images were analyzed.

RESULTS

The phantom study showed that the higher the RCR of the myocardium and the closer it is to the top edge of the phantom, the higher is the pixel value of the dbPET images. In a clinical study, LUR was significantly correlated with myocardial SUV_max (r = 0.96, p < 0.0001) and metabolic myocardial volume (r = 0.63, p = 0.001) for whole-body PET/CT imaging. Although no significant correlations were found between LUR and age (r = 0.05, p = 0.6865), body mass index (r = 0.03, p = 0.8178), or distance between the left myocardial apex and chest wall (r = 0.16, p = 0.1667).

CONCLUSIONS

FDG uptake in the myocardium affected dbPET images of the left breast, especially near the chest wall. Further, the effect of RI outside the FOV, such as in the myocardium, must be considered in the quantitative evaluation of breast cancer using dbPET.

Image quality evaluation of real low-dose breast PET

Purpose

To evaluate the clinical feasibility of high-resolution dedicated breast positron emission tomography (dbPET) with real low-dose ¹⁸F-2-fluorodeoxy-d-glucose (¹⁸F-FDG) by comparing images acquired with full-dose FDG.

Materials and methods

Nine women with no history of breast cancer and previously scanned by dbPET injected with a clinical ¹⁸F-FDG dose (3 MBq/kg) were enrolled. They were injected with 50% of the clinical ¹⁸F-FDG dose and scanned with dbPET for 10 min for each breast 60 and 90 min after injection. To investigate the effect of the scan start time and acquisition time on image quality, list-mode data were divided into 1, 3, 5, and 7 min (and 10 min with 50% FDG injected) from the start of acquisition and reconstructed. The reconstructed images were visually and quantitatively compared for contrast between mammary gland and fat (contrast) and for coefficient of variation (CV) in the mammary gland.

Results

In visual evaluation, the contrast between the mammary gland and fat acquired at a 50% dose for 7 min was comparable and even better in smoothness than that in the images acquired at a 100% dose. No visual difference between the images with a 50% dose was found with scan start times 60 and 90 min after injection. Quantitative evaluation showed a slightly lower contrast in the image at 60 min after 50% dosing, with no difference between acquisition times. There was no difference in CV between conditions; however, smoothness decreased with shorter acquisition time in all conditions.

Conclusions

The quality of dbPET images with a 50% FDG dose was high enough for clinical application. Although the optimal scan start time for improved lesion-to-background mammary gland contrast remained unknown in this study, it will be clarified in future studies of breast cancer patients.

Value of 18F-FDG PET/CT for predicting axillary pathologic complete response following neoadjuvant systemic therapy in breast cancer patients: emphasis on breast cancer subtype

Background

Neoadjuvant systemic therapy (NST) is a widely accepted initial treatment modality that can lead to pathologic downstaging of the axillary disease burden in breast cancer patients. Axillary response as well as baseline ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) uptake on positron emission tomography with computed tomography (PET/CT) differ between breast cancer subtypes. The value of baseline ¹⁸F-FDG PET/CT in predicting axillary response to NST is not yet established, possibly since breast cancer subtype was not taken into account. The purpose of this study was to investigate the value of baseline ¹⁸F-FDG PET/CT in predicting axillary response to NST with a specific emphasis on subtype.

Methods

PET-parameters derived from the primary tumor as well as the most FDG-avid axillary lymph node were measured on baseline ¹⁸F-FDG PET/CT. Overall imaging findings were compared with the gold standard of histopathology of the axillary surgery specimen. Analyses for ER-positive/HER2-negative were performed separately from HER2-positive and TN patients. In addition, separate analyses for clinically node-positive patients were performed.

Results

Sixty-six patients with 69 primary tumors were included in this study. Thirty-three axillae contained ER-positive/HER2-negative, 16 HER2-positive, and 20 TN breast cancer. No significant difference in PET-parameters between patients with axillary residual disease and axillary pathologic complete response were found for ER-positive/HER2-negative breast cancer. In the combined HER2-positive/TN subgroup, the SUV_max was significantly lower in patients without residual axillary disease in both the entire cohort and in patients with clinically node-positive disease. In this combined subgroup, a cut-off of 4.89 SUV_max measured on the most FDG-avid axillary lymph node could predict residual axillary disease with a sensitivity, specificity, PPV, and NPV of 90%, 69%, 53%, and 95%, respectively.

Conclusions

Predicting axillary response following NST with baseline ¹⁸F-FDG PET/CT can be performed when focusing on breast cancer subtypes. The easily computed PET-parameter SUV_max can predict axillary response in HER2-positive and TN breast cancer. This study adds to the accumulating evidence that studies investigating the value of ¹⁸F-FDG PET/CT in breast cancer should always take subtypes into account.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-021-00861-z.