Dual time point ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography fusion imaging (¹⁸F-FDG PET/CT) in primary breast cancer

Abstract

Background

To evaluate the clinicopathological and prognostic significance of the percentage change between maximum standardized uptake value (SUV_max) at 60 min (SUV_max1) and SUV_max at 120 min (SUV_max2) (ΔSUV_max%) using dual time point ¹⁸F-fluorodeoxyglucose emission tomography/computed tomography (¹⁸F-FDG PET/CT) in breast cancer.

Methods

Four hundred and sixty-four patients with primary breast cancer underwent ¹⁸F-FDG PET/CT for preoperative staging. ΔSUV_max% was defined as (SUV_max2 − SUV_max1) / SUV_max1 × 100. We explored the optimal cutoff value of SUV_max parameters (SUV_max1 and ΔSUV_max%) referring to the event of relapse by using receiver operator characteristic curves. The clinicopathological and prognostic significances of the SUV_max1 and ΔSUV_max% were analyzed by Cox’s univariate and multivariate analyses.

Results

The optimal cutoff values of SUV_max1 and ΔSUV_max% were 3.4 and 12.5, respectively. Relapse-free survival (RFS) curves were significantly different between high and low SUV_max1 groups (P = 0.0003) and also between high and low ΔSUV_max% groups (P = 0.0151). In Cox multivariate analysis for RFS, SUV_max1 was an independent prognostic factor (P = 0.0267) but ΔSUV_max% was not (P = 0.152). There was a weak correlation between SUV_max1 and ΔSUV_max% (P < 0.0001, R² = 0.166). On combining SUV_max1 and ΔSUV_max%, the subgroups of high SUV_max1 and high ΔSUV_max% showed significantly worse prognosis than the other groups in terms of RFS (P = 0.0002).

Conclusion

Dual time point ¹⁸F-FDG PET/CT evaluation can be a useful method for predicting relapse in patients with breast cancer. The combination of SUV_max1 and ΔSUV_max% was able to identify subgroups with worse prognosis more accurately than SUV_max1 alone.

Background

Breast cancer is the most frequent malignant disease and the fifth leading cause of cancer death in Japanese women. Most of these breast cancers are detected at relatively early stages, and the 5- and 10-year survival rates are reported to be > 90 and 80%, respectively [1]. However, even among stage I or node-negative cases, relapse or distant metastases can occur after initial therapies, and early detection of cases with high recurrence risk would be helpful in improving the overall prognosis of patients with breast cancer.

Conventional modalities for imaging diagnosis comprise mammography, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and bone scintigraphy. It was reported that dynamic contrast enhanced MRI and diffusion weighted imaging were correlated with the status of hormone receptors and Ki-67 in primary breast cancer [2, 3]. In recent years, ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) has come to play an increasing role in the diagnosis of biological properties as well as staging, treatment monitoring of residual disease, and detection of disease recurrence in breast cancer patients [4, 5]. For that purpose, the maximum standardized uptake values (SUV_max) of ¹⁸F-FDG has been shown to be correlated with tumor size, nuclear grade (NG), and Ki-67 labeling index (LI) [6, 7]. Furthermore, several studies [8,9,10,11] have shown that the SUV_max of primary tumor, that reflects its metabolic activity, on ¹⁸F-FDG PET/CT can predict patients’ poor prognosis.

In malignant tumors, glucose metabolism is usually enhanced, and the uptake of ¹⁸F-FDG increases. Therefore, a higher level of ¹⁸F-FDG accumulation in PET/CT should reflect higher proliferative activity of the tumor cells. Recently, several studies and meta-analyses have been performed on the relationships between PET/CT and histopathological findings in the field of diagnostic oncology [6, 12,13,14,15,16]. Especially, the uptake of ¹⁸F-FDG was shown to be correlated with expressions of histopathological markers, e.g., Ki-67 LI, vascular endothelial growth factor, and hypoxia induced factor 1α, in head and neck cancer, lung cancer, and lymphoma [12,13,14,15,16].

Because the SUV_max is usually measured at a single time point, such as 1 h after ¹⁸F-FDG administration, the dynamic index of the tumor is not included in routine examination. Some articles reported the utility of measurement of ¹⁸F-FDG uptake levels at dual time points [17, 18]. The ¹⁸F-FDG uptake level at a later point has a tendency to increase in malignant lesions but to decrease in benign lesions, such as inflammatory reactions [19]. Therefore, the measurement of ¹⁸F-FDG uptake in dual time point ¹⁸F-FDG PET/CT may be able to estimate biological properties and predict patient prognosis more accurately.

The aim of this study was to investigate the clinicopathological significance of dual time point ¹⁸F-FDG PET/CT in patients with primary breast cancer. In addition, we assessed the prognostic significance of the measurement of dynamic ¹⁸F-FDG uptake levels.

Methods

This was a retrospective study in a single institute.

Ethics approval and consent to participate

This study was performed in accordance with the Declaration of Helsinki and was approved by the institutional review board of National Defense Medical College (registration number: 2695). All patients agreed to participate in this study, and written informed consent was obtained from all these patients.

Eligible patients

Between September 2008 and December 2017, ¹⁸F-FDG PET/CT was performed for 820 consecutive preoperative patients with primary breast carcinoma. Of these, 356 patients were excluded from the study because of (1) history of malignant diseases other than breast cancer within 5 years, (2) preoperative medication therapy, (3) diabetes mellitus, (4) previous treatment of ipsilateral or contralateral breast cancer, (5) presence of distant metastases, (6) acquisition of only single time point data of ¹⁸F-FDG PET/CT, and/or (7) difficulty in measuring SUV_max due to low ¹⁸F-FDG accumulation. Finally, 464 female patients were eligible.

In all cases, diagnosis of breast cancer was made based on cytopathological and/or histopathological examination before surgery. ¹⁸F-FDG PET/CT was performed before surgery, and the interval between the PET/CT examination and surgery was 42 days on an average, ranging from 7 to 71 days. Postoperative surveillance for 5 years was performed through examination every 3 months and mammography every year. After 5 years, patients underwent mammography every year and were followed up to 10 years after surgery. If relapse was suspected in these tests, it was confirmed using CT or PET/CT.

Quantification of ¹⁸F-FDG uptake in primary breast cancer

All 464 patients underwent ¹⁸F-FDG PET/CT at the Tokorozawa PET Diagnostic Imaging Clinic (Tokorozawa, Japan). Patients fasted for at least 4 h before the examination. One hour after intravenous administration of 3.7 Mbq/kg ¹⁸F-FDG, the first scanning was performed. The first examination involved whole-body imaging from the head to thigh, and the second scanning involved the chest only, within 50–60 min of the first examination.

After image reconstruction, the region of interest (ROI) was placed in primary breast cancer. The SUV is defined as decay-corrected tissue activity divided by the injected dose per patient body and is calculated using the formula,

$$ \mathrm{SUV}=\mathrm{activity}\ \mathrm{in}\ \mathrm{ROI}\ \left(\mathrm{MBq}/\mathrm{ml}\right)/\mathrm{injected}\ \mathrm{dose}\ \left(\mathrm{MBq}/\mathrm{kg}\ \mathrm{body}\ \mathrm{weight}\right). $$

The SUV_max1 and SUV_max2 were obtained at dual time points, and the ΔSUV_max% was calculated using the formula,

$$ {\Delta \mathrm{SUV}}_{\mathrm{max}}\%=\left[\left({\mathrm{SUV}}_{\mathrm{max}}2-{\mathrm{SUV}}_{\mathrm{max}}1\right)/{\mathrm{SUV}}_{\mathrm{max}}1\right]\times 100, $$

where the SUV_max1 and SUV_max2 were the SUV_max at the initial phase (60 min) and SUV_max at delayed phase (120 min), respectively.

Histological study

Two observers (H.T., Y.Y.) performed pathological diagnosis. NG was defined according to the General Rules for Clinical and Pathological Recording of Breast Cancer, 17th edition [20]. NG was determined by the sum of the nuclear atypia score and the mitosis count score. Estrogen receptor (ER) and progesterone receptor (PgR) expression was assessed by immunohistochemistry and defined as positive if ≥1% of carcinoma cells were immunoreactive [21]. Human epidermal growth factor receptor 2 (HER2) positivity was determined according to the American Society of Clinical Oncology/College of American Pathologists guideline 2013 [22]. According to the recommendation of the Breast Cancer Working Group, Ki-67 LI was defined as high if 14% or higher of constituent carcinoma cells were immunoreactive [23, 24]. Pathological stage was determined by the clinical and pathological recording of breast cancer, 8th edition, by Union for International Cancer Control (UICC).

Evaluation of ¹⁸F-FDG PET/CT results as prognostic factor

Receiver operating characteristic (ROC) curves were drawn to determine the optimal cutoff values of SUV_max1 and ΔSUV_max%. Furthermore, the Youden index [= sensitivity – (1 – specificity)] of each cutoff value was calculated, and the highest value was taken as the optimal cutoff point.

Statistical analysis according to clinicopathological factors and prognosis

The correlations between SUV_max parameters (SUV_max1, SUV_max2, and ΔSUV_max%) and clinicopathological factors were evaluated using the non-parametric Wilcoxon and the Kruskal–Wallis tests. All statistical analyses were two-sided, with significance defined as P value of < 0.05. The Kaplan-Meier curves for relapse-free survival (RFS) and overall survival (OS) were drawn, and their differences were tested by the log-rank test. A Cox proportional hazards model was used for univariate and multivariate analyses for RFS. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the accuracies of SUV_max1, ΔSUV_max%, and their combination for RFS were calculated. Statistical analyses were performed using JMP® 13 (SAS Institute Inc., Cary, NC, USA).

Results

Patient characteristics

Data obtained from the 464 patients on age, tumor invasion size, histological type, NG, lymphatic invasion, hormonal receptor status, HER2 status, Ki-67 LI, pathological stage, SUV_max1 and SUV_max2, ΔSUV_max%, RFS, and OS are summarized in Table 1. Mean SUV_max1, mean SUV_max2, and mean ΔSUV_max% were 4.6 (± 3.5 standard deviation [SD]), 5.6 (± 4.9 SD), and 15.6% (± 20.2 SD), respectively. SUV_max1 and SUV_max2 did not show normal distribution whereas ΔSUV_max% showed normal distribution (Additional file 1 Figure S1). Five and 10-year RFS rates were 92.0 and 84.9%, respectively. Five and 10-year overall survival rates were 97.3 and 88.5%, respectively (median follow up 4.9 years).

Table 1 Patient characteristics

Setting of optimal cutoff values for patient prognostication

According to the Youden index, the optimal cutoff value of SUV_max1 was 3.4, and area under the curve (AUC) was 0.627 (95% confidence interval [CI] 0.536–0.719) (Fig. 1A). The patients were divided into the low SUV_max1 (< 3.4) (n = 223) and high SUV_max1 groups (≥ 3.4) (n = 241). The optimal cutoff value of ΔSUV_max% was 12.5, and AUC was 0.594 (95% CI 0.505–0.683) (Fig. 1B). The patients were divided into the low ΔSUV_max% (< 12.5) (n = 202) and high ΔSUV_max% groups (≥ 12.5) (n = 262).

Fig. 1

Determinations of the cutoff point for maximum standardized uptake value at 60 min (SUV_max1) and ΔSUV_max% with reference to relapse events. (a) Receiver operator characteristic (ROC) curves of SUV_max1 for relapse-free survival (n = 464). SUV_max1 at the cutoff value was 3.4, area under the curve (AUC) was 0.627 (95% CI: 0.536–0.719). (b) ROC curves of ΔSUV_max% for relapse-free survival (n = 464). At the ΔSUV_max% cutoff value of 12.5, AUC was 0.594 (95% CI: 0.505–0.683)

Patient characteristics between high and low groups divided by SUV_max1 and ΔSUV_max%

The correlations between high and low SUV_max1 groups and clinicopathological parameters are presented in Table 2. Tumor size, pathological T factor, NG, lymphatic invasion, pathological N factor, pathological stage, and SUV parameters (SUV_max1, SUV_max2, ΔSUV_max%) were significantly different between high and low SUV_max1 groups. High Ki-67 LI was more frequent in the high SUV_max1 group than in the low SUV_max1 group (P < 0.0001) whereas ER, PgR, HER2, and subtype were not correlated with SUV_max1. The correlations between the high and low ΔSUV_max% groups and clinicopathological parameters are presented in Table 3. The factors correlated with SUV_max1 were significantly different between the high and low ΔSUV_max% groups. High Ki-67 LI was more frequent in the high ΔSUV_max% group than in the low ΔSUV_max% group (P = 0.0336) whereas ER, PgR and subtype were not correlated with ΔSUV_max%. HER2 status was significantly different between high and low ΔSUV_max% groups (P = 0.0304). Two patients with HER2-positive ductal carcinoma in situ (DCIS) were classified into the low ΔSUV_max% group. Therefore, when these DCIS cases were excluded from the analysis, HER2 status showed no significant difference between these two groups.

Table 2 Patient characteristics between high and low SUV_max1 groups

Table 3 Patient characteristics between high and low ΔSUV_max% groups

Correlation between SUV_max1 and ΔSUV_max%

There was a weak correlation between SUV_max1 and ΔSUV_max% (P < 0.0001, R² = 0.166). In the high SUV_max1 group (≥ 3.4) (n = 241), 179 patients (68.3%) with high ΔSUV_max% (≥ 12.5) were included. In contrast, in the low SUV_max1 group (< 3.4) (n = 223), 83 patients (31.7%) with high ΔSUV_max% were included.

Comparison of survival curves

The RFS curves for the high and low SUV_max1 groups were significantly different between these curves (P = 0.0003) (Fig. 2A). Although there was no significant difference in OS curves for the high and low SUV_max1 groups, the high SUV_max1 group tended to show worse prognosis (P = 0.0553) (data not shown). The RFS curves for the high and low ΔSUV_max% groups were significantly different (P = 0.0151) (Fig. 2B). Although, there was no significant difference in OS curves between the high and low ΔSUV_max% groups, the former groups tended to show worse prognosis (P = 0.141) (data not shown). Because the correlation of SUV_max2 with RFS was weaker than that of SUV_max1 (P = 0.0012), we did not use SUV_max2 for prognostic analysis (data not shown).

Fig. 2

Relapse-free survival (RFS) curves for (a) patient groups with high and low SUV_max1 values and (b) for patient groups with high and low ΔSUV_max%. (a) RFS curves were significantly different between two patient groups (P = 0.0003). (b) RFS curves were significantly different between two patient groups (P = 0.0151)

Prognostication by the combination of SUV_max1 and ΔSUV_max%

The 464 patients were classified into three subgroups (group A, B, and C) by the combination of SUV_max1 and ΔSUV_max%. Group A was SUV_max1 ≥ 3.4 and ΔSUV_max% ≥ 12.5 (n = 179), group B was SUV_max1 ≥ 3.4 and ΔSUV_max% <  12.5 (n = 62), and group C was SUV_max1 <  3.4 (n = 223). Although group C could also be subclassified into the high ΔSUV_max% (n = 83) and low ΔSUV_max% subgroups (n = 140), no significant difference in RFS was observed between these two subgroups (P = 0.625, data not shown).

There were significant differences in RFS curves between these three subgroups (P = 0.0006), and between groups A and C (P = 0.0001). On the other hand, there were no significant differences between groups A and B (P = 0.285), and between groups B and C (P = 0.146) (Fig. 3A). The 10-year RFS rates were 90.6% in group B and 89.0% in group C, whereas the rate was 78.8% in group A. Furthermore, RFS curves were significantly different between group A and group “B + C” (P = 0.0002) (Fig. 3B). By the combination of the ΔSUV_max% and the SUV_max1, it was possible to predict a group with the worse prognosis more sensitively than SUV_max1 or ΔSUV_max% alone.

Fig. 3

(a) RFS curves for the patients of subgroups a, b and c classified by the combination of SUV_max1 and ΔSUV_max%. RFS curves were significantly different among these three groups (P = 0.0006). (b) RFS curves for the patients of subgroup A and subgroup “B + C”. RFS curves were significantly different between these two groups (P = 0.0002). Ten-year RFS rates were 78.8% in group A and 89.0% in group “B + C”

In the subgroup analyses, there were significant differences in RFS between group A and group B/C in node-negative patients (n = 334) and in node-positive patients (n = 130) (P = 0.0126 and P = 0.0455, respectively). In the pTis/pT1 (n = 297) and pT2/pT3 groups (n = 167), there were no significant differences in RFS between group A and group B/C (P = 0.120 and P = 0.131, respectively). With regard to subtype, group A showed a significantly lower RFS than group B/C in the ER-positive/HER2-negative group (P = 0.0008, n = 345), but such a relationship was not found in the ER-positive/HER2-positive, ER-negative/HER2-positive, and ER-negative/HER2-negative patient groups (P = 0.0614, P = 0.358, P = 0.823, respectively).

Univariate and multivariate analyses

By Cox’s univariate analyses to estimate relapse risk, five clinicopathological parameters, invasive tumor size, lymph node metastasis, NG, lymphatic invasion, and Ki-67 LI, as well as SUV_max1 and ΔSUV_max% were statistically significant factors. The combined SUV_max1 and ΔSUV_max% was also a significant prognostic factor in RFS (P = 0.0007) (Table 4). Because SUV_max1 and ΔSUV_max% were correlated with together, we performed the Cox’s multivariate analyses including these five clinicopathological parameters with either SUV_max1, ΔSUV_max%, or the combination of SUV_max1 and ΔSUV_max%. In the multivariate analyses, SUV_max1 or the combination of SUV_max1 and ΔSUV_max% was an independent prognostic factor (P = 0.0267 and P = 0.0283, respectively, Table 4). As the test to detect relapse, the combined measurement of SUV_max1 and ΔSUV_max% showed higher specificity, PPV, and accuracy than the measurement of SUV_max1 or ΔSUV_max% alone (Table 5).

Table 4 The univariate and multivariate analyses for relapse

Table 5 Accuracy of SUV_max1, ΔSUV_max%, and their combination for prediction of relapse

Discussion

In malignant tumors, glucose metabolism is usually enhanced, and the extent of increase in glucose consumption was shown to be correlated with higher proliferation rates of cancer cells. Therefore, a higher level of accumulation of ¹⁸F-FDG in PET/CT is a sign of the primary breast cancer with high proliferative activities [8,9,10, 25], and ¹⁸F-FDG PET/CT has been used not only for cancer diagnosis but also for functional assessments of breast cancer, i.e., clinical aggressiveness and higher sensitivity to neoadjuvant therapies [26, 27]. In fact, Deng et al. and Surov et al. summarized that the uptake of ¹⁸F-FDG was associated with Ki-67 LI in their meta-analyses [6, 7]. We were able to confirm their results in this study.

For the evaluation of PET/CT images, the most commonly used parameter is the SUV_max, which is usually measured 60 min after the injection of ¹⁸F-FDG. It has also been believed that the addition of information of the later phase can be used to determine the biological properties of the examined cancers in more detail. Some reported that ¹⁸F-FDG uptake in malignancy continued to increase until approximately 4–5 h after injection, but the uptake decreased in the benign lesion 30 min after the injection [18, 28]. Furthermore, the ΔSUV_max% was correlated with the grade of malignancy in lung cancer and lymphoma [29, 30]. Although the usefulness of ΔSUV_max% was generally considered acceptable, few reports have been published on its relationship with the prognosis of breast cancer.

In this report, we confirmed that SUV_max1 was an independent prognostic factor for RFS. Furthermore, we showed that ΔSUV_max% was a significant prognostic indicator of RFS and that the combination of SUV_max1 and ΔSUV_max% was possible to predict a group with poorer prognosis more sensitively than SUV_max1 alone. With the optimal cutoff value (12.5 of ΔSUV_max%), the subgroup with better prognosis can be detected among from the high SUV_max1 (≥ 3.4) group. In contrast, the effectiveness of SUV_max1 and ΔSUV_max% for OS could not be demonstrated. In the present patient cohort, follow up period is still short, and the number of events appears too small to analyze the effectiveness of ΔSUV_max% for OS prediction.

The RFS rate of patients with breast cancers of the ER-positive/HER2-negative subtype was significantly lower in the high-SUV_max/high-ΔSUV_max% group than in the other groups (P = 0.0008). SUV_max was shown to be correlated with 21-gene recurrence score in ER-positive/HER2-negative breast cancer [31]. Therefore, SUV-related parameters might be clinically useful, in addition to the 21-gene recurrence score, for the selection of high-risk node-negative luminal breast cancers, although a larger-scale study is necessary. Furthermore, the combination of SUV_max1 and ΔSUV_max% would be able to increase the accuracy of preoperative diagnoses of lymph node metastasis and therapeutic response to neoadjuvant therapies.

In this study, patients with previous treatment were excluded. In these patient groups, 24 ER-negative/HER2-positive patients and 54 ER-negative/HER2-negative patients were included. Therefore, only 10.8% (50/464) were HER2-positive type and 11.6% (56/464) were ER-negative/HER2-negative type. These types of breast cancers were reported to have a higher SUV value than ER-positive types and to have worse prognosis than the ER-positive types [32,33,34]. Furthermore, we excluded the 109 patients whose ¹⁸F-FDG accumulation was not visible and SUV_max was not measurable from the study. These cases appear to show very low SUV_max values and accordingly, were also expected to have a good prognosis. For these reasons, it seemed that the true efficacy of ΔSUV_max% and combined measurement of SUV_max1 and ΔSUV_max% as prognostic indicators might be higher than the present results.

The pN factor was a very strong prognostic factor in the univariate analysis but did not have an independent prognostic power in the multivariate analysis. In these analyses, pN was divided into pN0 and pN1–3. Because pN1 was shown to reveal relatively good prognosis and a majority of pN-positive patients showed pN1 in this study, the impact of node-positivity might have been diluted by the good-prognosis effect in pN1 cases. Lymphatic invasion and pT might also have been confounding factors along with pN.

The limitations of this study include its retrospective nature, single center data, and a relatively small number of events. A multicenter, prospective study is needed to highlight the effectiveness of ΔSUV_max% in the prognostication of primary breast cancer.

Nonetheless, the strength of the present study involves the large number of images reviewed, the correlation between relevant clinicopathological and prognostic data, and exclusion of patients with diabetes. Furthermore, SUV_max parameters were easy to compute and reproducible, and dual time point imaging could be performed in a relatively short time with minimal inconvenience to the patient and be readily performed at most centers.

Conclusions

In conclusion, dual time point ¹⁸F-FDG PET/CT can be a useful modality for prediction of relapse in patients with breast cancer. The combination of SUV_max1 and ΔSUV_max% was able to identify the patient groups with worse prognosis more accurately than SUV_max1 alone.