1. Introduction
Definitive concurrent chemoradiotherapy (CCRT) using a cisplatin-based regimen is currently the standard treatment for locally advanced cervical cancer (International Federation of Gynecology and Obstetrics (FIGO) stages IIB–IV) [1,2]. The contribution of CCRT to improving survival outcomes in locally advanced cervical cancer has been confirmed in previous studies, with a complete clinical response achieved in 70% to 90% patients [3,4]. However, about one-third of patients with cervical cancer experience recurrence, most such cases developing within two years after the completion of therapy [5]. The accurate prediction of tumor recurrence may help to improve survival outcomes and enable the personalization of treatment.
Various metabolic parameters of 18F-fluorodeoxyglucose positron-emission tomography/computed tomography (18F-FDG PET/CT) have been reported as prognostic factors, including the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Several studies have also evaluated the prognostic values of primary tumor SUVmax (tSUVmax), but the results were controversial. Some research suggested that tSUVmax can predict the prognosis and treatment response [6,7], whereas other studies cautioned that tSUVmax is a poor independent predictor of disease progression, recurrence, and death [8,9].
SUV measurements are the most widely used and generally accepted indices in the published literature for assessing disease activity in various cancers. However, many factors can affect the reliability of SUV, such as the time between injection and imaging acquisition, partial volume effects, extravasation of administered 18F-FDG at the site of injection, residual activity in the syringe, decay of the injected dose, and technological characteristics and parameters [10]. However, it has been repeatedly recognized that at least some of these issues can be reduced or eliminated if SUV is normalized to the SUV of a suitable reference region [11]. The liver and blood pool are the most widely used references because they maintain a nearly constant SUV level over time following the injection of 18F-FDG [12,13]. Recent studies have shown that the tumor-to–normal liver SUV ratio (TLR) and tumor-to–blood pool SUV ratio (TBR) are independent prognostic factors in several cancers [13,14]. However, the prognostic roles of the normalization of SUVmax to the liver or blood pool have not been reported previously in cervical cancer. Therefore, we hypothesized that the normalization of SUVmax to the liver or blood pool may enhance the prognostic values of SUVmax and predict tumor recurrence and death.
This study sought to evaluate whether the normalization of SUVmax to the liver or blood pool can reduce or eliminate the limitations of SUVmax and to examine the prognostic role of SUVmax in locally advanced cervical cancer treated with CCRT.
2. Materials and Methods
2.1. Patients
For this study, we enrolled 156 patients with biopsy-confirmed cervical cancer treated with CCRT between September 2005 and November 2017. Retrospective data collection and analysis were approved by the institutional review board of Kyungpook National University Chilgok Hospital, while the need for informed consent was waived because of the retrospective design of the study. Disease staging was conducted according to the FIGO staging system [15]. All patients had previously undergone 18F-FDG PET/CT for initial diagnosis, staging, and radiotherapy planning. Patients who exhibited evidence of distant metastatic disease or a history of previous surgery, radiotherapy, or chemotherapy were excluded from the study.
The clinical and pathological parameters were reviewed and retrieved, including age, histology, FIGO stage, tumor size, presence of lymph node metastasis, and serum squamous cell carcinoma (SCC) antigen.
2.2. Treatment
All patients were treated with a combination of external beam radiotherapy (EBRT), followed by high-dose-rate (HDR) intracavitary radiotherapy (ICR) with curative intent. EBRT was delivered to the whole pelvis via a three-dimensional conformal radiation therapy four-field box technique (1.8 Gy daily fractions, five times/week, for a total dose of 45 Gy). Extended-field radiotherapy encompassing the pelvis and para-aortic nodal area was administered to patients with para-aortic nodal involvement, and a four-field box technique was similarly used in this case. HDR ICR was initiated after the delivery of an EBRT dose of 39.6 Gy. An additional 5.4 Gy was administered with a midline block. A parametrial boost of 10 Gy in five fractions was also administered to patients with parametrial involvement. ICR was delivered twice per week in five fractions with a fractional dose of 6 Gy at point A. Weekly cisplatin at a dose of 40 mg/m2 was administered during radiotherapy. The first course of cisplatin was administered on the first day of radiotherapy.
2.3. 18F-FDG PET/CT Image Acquisition
All patients fasted for at least six hours, and their blood glucose levels were determined before the administration of 18F-FDG. Patients with blood glucose levels higher than 150 mg/dL were rescheduled for a later examination, and treatment was administered to maintain a blood glucose concentration of less than 150 mg/dL in all participants. Patients received intravenous injections of approximately 5.2 MBq of FDG per kilogram of body weight and were advised to rest for one hour before undergoing the acquisition of 18F-FDG PET/CT images. The 18F-FDG PET/CT scans were performed using Discovery 600 (GE Healthcare, Chicago, IL, USA). Before the PET scan, for attenuation correction, a low-dose CT scan was obtained without contrast enhancement from the skull base to the thigh while the patient was in the supine position and breathing quietly. PET scans were also obtained from the skull base to the thigh at 2.5 min per bed position. PET images were reconstructed with a 128 × 128 matrix and an ordered-subset expectation maximum iterative reconstruction algorithm.
2.4. Image Interpretation and PET Data Analyses
The PET/CT images were interpreted by two experienced nuclear medicine physicians, and a final consensus was achieved for all patients. A positive finding was defined as any focus with increased FDG uptake as compared with the surrounding normal tissue. Foci of FDG uptake due to normal physiology or benign variants, such as muscular exercise or an infectious pulmonary infiltration, were excluded from the analysis. The PET images were analyzed visually and semi-quantitatively by measuring the maximum SUV of the primary tumor (pSUVmax) and metastatic lymph node (nSUVmax), the TLR, the TBR, the metastatic lymph node–to–normal liver SUV ratio (NLR), and the metastatic lymph node–to–blood pool SUV ratio (NBR). The image display and analysis were achieved using the volume viewer software on an Advantage Workstation 4.5 (GE Medical Systems, Waukesha, WI, USA), which provides a convenient and automatic method by which to delineate the volume of interest (VOI) using an isocontour threshold method based on SUV. For each patient, the nSUVmax was designated as the highest SUVmax of all metastatic lymph nodes. The mean SUV of the mediastinal background plus two standard deviations was used as the threshold to automatically calculate SUVmax. If the SUVmax of the metastatic lymph node was lower than that of the threshold, we regarded SUVmax of the metastatic lymph node as 0. SUVmax was obtained using the following formula: SUVmax = maximum activity in the region of interest (mBq/g) / (injected dose (mBq)/body weight (g)). The mean SUV of the normal liver was obtained by taking the average of the three VOIs (two in the right lobe and one in the left lobe) of 1 cm in diameter. The mean SUV of the mediastinal blood pool was calculated using a 25-mm-diameter spherical VOI that was manually delineated in the aortic arch of each patient.
2.5. Clinical Follow-Up
Clinical follow-up assessments of patients were conducted every three months until two years after treatment, every six months from two years to five years after treatment, and annually thereafter. Failure was defined as a biopsy-proven recurrence or documentation of the progression of disease on serial imaging studies. Failure patterns were divided into four groups: (1) none, (2) isolated local failure that included the central pelvis and/or pelvic lymph nodes, (3) distant failure that included para-aortic and supraclavicular lymph nodes, and (4) combined local and distant failure.
2.6. Statistical Analysis
Continuous data are expressed as means ± standard deviations, and categorical data are presented as frequencies and percentages. The time to event was calculated as the time interval from the date of diagnosis to the date at which the first clinical or imaging findings were revealed that suggested disease recurrence. The differences between subsets were evaluated using a Student’s t-test, while differences between proportions were compared using the chi-squared test.
To identify the optimal cutoff of continuous variables for the prediction of recurrence and death, a receiver operating characteristic (ROC) curve analysis was performed. Meanwhile, the Kaplan–Meier method and the log-rank test were adopted in the survival analysis of prognostic factors, while a Cox proportional hazards model was used to evaluate prognostic variables for univariate and multivariate comparisons, and the estimated hazard ratios (HRs) are presented with 95% confidence intervals (CIs). The MedCalc version 19.2 statistical software program (MedCalc Software Ltd., Ostend, Belgium;
3. Results
3.1. Clinical Features and Treatment Outcomes
The clinical characteristics of the study participants are listed in Table 1. The mean age of the patients was 55.1 ± 12.9 years. The predominant FIGO stage was IIB (76.9%), followed by IIIB (10.3%), IIIA (9.0%), and IVA (3.8%). A total of 102 patients (65.4%) had metastases to the regional lymph node. The mean tSUVmax, TLR, and TBR were 6.0 ± 2.5, 5.9 ± 4.4, and 9.5 ± 6.0, and the mean nSUVmax, NLR, and NBR were 3.3 ± 7.3, 2.3 ± 4.0, and 3.1 ± 5.0, respectively. After a median follow-up of 49.5 months (range: 4–157 months), 55 patients (35.3%) had experienced recurrence and 30 patients (19.2%) had died of disease progression. Of the 55 patients who experienced disease recurrence, 17 showed local recurrence only, 29 showed distant recurrence only, and nine showed both local and distant recurrence.
3.2. Cutoff Value of Metabolic Parameters
The optimal cutoff values of tSUVmax, TLR, and TBR for predicting tumor recurrence were 4.81, 4.99, and 6.96, respectively (area under the curve (AUC) = 0.506, p = 0.6433 for tSUVmax; AUC = 0.610, p = 0.0041 for TLR; and AUC = 0.607, p = 0.0071 for TBR). The optimal cutoff values calculated for nSUVmax, NLR, and NBR for predicting tumor recurrence were 4.63, 2.02, and 1.97, respectively (AUC = 0.732, p < 0.0001 for nSUVmax; AUC = 0.726, p < 0.0001 for NLR; and AUC = 0.722, p < 0.0001 for NBR) (Supplementary Materials: Table S1).
The optimal cutoff values for tSUVmax, TLR, and TBR for predicting death were 6.43, 5.14, and 7.52, respectively (AUC = 0.523, p = 0.2636 for tSUVmax; AUC = 0.688, p < 0.0001 for TLR; and AUC = 0.686, p < 0.0001 for TBR). The optimal cutoff values calculated for nSUVmax, NLR, and NBR for predicting death were 4.91, 2.61, and 3.07, respectively (AUC = 0.724, p < 0.0001 for nSUVmax; AUC = 0.735, p < 0.0001 for NLR; and AUC = 0.740, p < 0.0001 for NBR) (Table S1).
3.3. Univariate and Multivariate Survival Analyses
In the univariate analysis, the FIGO stage (HR: 4.64; 95% CI: 2.31–9.32; p < 0.0001), tumor size (HR: 2.48; 95% CI: 1.41–4.34; p = 0.0016), lymph node metastasis (HR: 3.09; 95% CI: 1.79–5.32; p < 0.0001), serum SCC (HR: 3.71; 95% CI: 1.94–7.12; p = 0.0001), TLR (HR: 2.26; 95% CI: 1.37–3.87; p = 0.0031), TBR (HR: 2.23; 95% CI: 1.31–3.81; p = 0.0033), nSUVmax (HR: 6.86; 95% CI: 3.81–12.36; p < 0.0001), NLR (HR: 6.26; 95% CI: 3.51–11.14; p < 0.0001), and NBR (HR: 4.74; 95% CI: 2.76–8.15; p < 0.0001) were significant prognostic factors for disease-free survival (DFS). For the prediction of death, the age (HR: 2.24; 95% CI: 1.06–4.77; p = 0.0355), FIGO stage (HR: 7.30; 95% CI: 2.85–18.69; p < 0.0001), tumor size (HR: 3.09; 95% CI: 1.50–6.38; p = 0.0023), lymph node metastasis (HR: 2.96; 95% CI: 1.41–6.18; p = 0.0039), paraaortic lymph node metastasis (HR: 2.78; 95% CI: 1.10–6.99; p =0.0305), serum SCC (HR: 3.75; 95% CI: 1.55–9.08; p = 0.0035), TLR (HR: 4.23; 95% CI: 2.05–8.70; p = 0.0001), TBR (HR: 4.26; 95% CI: 2.07–8.79; p = 0.0001), nSUVmax (HR: 7.72; 95% CI: 3.53–16.88; p < 0.0001), NLR (HR: 10.27; 95% CI: 4.51–23.39; p < 0.0001), and NBR (HR: 8.02; 95% CI: 3.71–17.36; p < 0.0001) were significant prognostic factors (Table 2).
The multivariate analysis performed for DFS and overall survival (OS) following the adjustment for the effects of clinicopathologic variables, which were statistically significant in the univariate survival analyses, revealed that nSUVmax (HR: 4.06; 95% CI: 1.74–9.44; p = 0.0012), NLR (HR: 3.54; 95% CI: 1.53–8.19; p = 0.0032), and NBR (HR: 3.38; 95% CI: 1.02–11.19; p = 0.0457) were independent prognostic factors for DFS, while TLR (HR: 4.16; 95% CI: 1.19–14.50; p = 0.0252), TBR (HR: 3.01; 95% CI: 1.04–8.70; p = 0.0415), nSUVmax (HR: 6.99; 95% CI: 1.56–31.25; p = 0.0109), NLR (HR: 4.84; 95% CI: 1.58–14.81; p = 0.0057), and NBR (HR: 6.87; 95% CI: 1.55–30.54; p = 0.0113) were independent prognostic factors for OS (Table 3). The Kaplan–Meier survival plots revealed significant differences in DFS and OS when stratified by TLR, TBR, NLR, and NBR (Figure 1 and Figure 2).
3.4. Comparison ROC for the Prediction of Tumor Recurrence and Death
To enhance the discrimination for the prediction of tumor recurrence and death, SUVmax was normalized to the liver and blood pool. TLR and TBR showed significant improvements in discrimination for tumor recurrence when compared with tSUVmax (tSUVmax vs. TLR; p = 0.0056, and tSUVmax vs. TBR; p = 0.0099) (Figure 3A). Furthermore, TLR and TBR demonstrated significant improvements in the accuracy of risk prediction for OS relative to tSUVmax (tSUVmax vs. TLR; p < 0.0001, and tSUVmax vs. TBR; p = 0.0001) (Figure 3C). However, the normalization of the nSUVmax did not improve the discrimination for tumor recurrence and death (Figure 3B,D).
4. Discussion
There are two major findings in this study. First, the normalization of tSUVmax to the liver or blood pool enhanced the level of discrimination for the prediction of tumor recurrence and death in locally advanced cervical cancer treated with CCRT. Second, TLR, TBR, NLR, and NBR are independent prognostic factors for predicting survival outcomes.
Several studies have demonstrated that pretreatment tSUVmax can predict survival outcomes. The largest retrospective study of 287 patients with FIGO stages IA2 to IVB cervical cancer established the following three prognostic groups based on tSUVmax with five-year OS rates: 95% for a tSUVmax of 5.2 or less, 70% for a tSUVmax between 5.2 and 13.3, and 44% for a tSUVmax greater than 13.3 [6]. Moreover, in a recent multicenter retrospective study that enrolled 270 patients with locally advanced cervical cancer treated with CCRT, a tSUVmax of 12 or greater was an independent prognostic biomarker for predicting tumor recurrence (HR: 2.14; 95% CI: 1.32–3.47; p = 0.002) and death (HR: 3.06; 95% CI: 1.46–6.44; p = 0.003) [16].
However, other studies have shown that tSUVmax is not an independent prognostic metabolic parameter. A study of 53 patients with FIGO stages IB1 to IVA determined the optimal tSUVmax cutoff to be 16.0; however, this did not significantly predict the progression or DFS [8]. SUVmax has several limiting factors, such as it representing the highest metabolic voxel but not the metabolic activity of the entire tumor. Meanwhile, research suggests volume-based metabolic parameters such as MTV and TLG are of prognostic value in cervical cancer [17]. However, MTV and TLG might include inflammation around the primary tumor associated with weakness [18]. SUVmax exhibits a high degree of reproducibility and is a less time-consuming method to apply. Despite these advantages, SUVmax may not be an adequate surrogate marker for representing the metabolic rate of the tumor, so other metabolic parameters that can predict prognosis should be further explored. The mediastinum vessel and normal liver tissue are the most frequent candidates among normal tissues [19].
Among various metabolic parameters, the tSUVmax normalized to liver uptake after neoadjuvant chemotherapy was the best predictor of a pathologic complete response in locally advanced rectal cancer [13]. Moreover, the standardized uptake TBR was an independent predictor of recurrence in non-small-cell lung cancer [14]. In our study, tSUVmax had no significant impact in predicting tumor recurrence or death. After the normalization of tSUVmax to the liver or blood pool, however, tSUVmax demonstrated a significant prognostic performance. We confirmed the improvement in discrimination for the prediction of tumor recurrence and death using the normalization of tSUVmax to the liver or blood pool by comparing ROC curves among tSUVmax, TLR, and TBR. Moreover, the normalization of tSUVmax to the liver or blood pool showed a significant prognostic value for predicting tumor recurrence or death in univariate and multivariate analyses. These enhancements of the prognostic value may make it possible to personalize treatment in a way that may involve higher doses of radiation boosts, consolidation chemotherapy, or adjuvant hysterectomy.
The lymph node status on 18F-FDG PET/CT images has also been shown to predict tumor recurrence and survival outcomes in patients with cervical cancer. A prospective cohort study of 560 patients who underwent pretreatment 18F-FDG PET/CT imaging demonstrated that patients with PET-positive lymph nodes had significantly worse DFS as compared with those with PET-negative nodes [20]. Furthermore, it has also been documented that the level of nSUVmax correlates with survival outcomes [7,21]. Only a limited number of studies to date have evaluated the prognostic roles of NLR or NBR. Furthermore, only one study has evaluated the normalization of nSUVmax to the liver. Higher NLR on pretreatment 18F-FDG PET/CT images was shown to be an independent prognostic factor for worse distant metastasis-free survival in locally advanced nasopharyngeal carcinoma [22]. Similarly to our previous research [9,16], we found in the current study that nSUVmax was a powerful biomarker for predicting tumor recurrence and death and that NLR and LBR were significant prognostic factors. However, normalized nSUVmax to the liver or blood pool did not demonstrate superiority over conventional nSUVmax for predicting tumor recurrence or survival.
Our study has some noteworthy limitations that must be considered. First, it was a retrospective study including a limited number of patients. Second, a histopathological verification of the lymph nodes was not performed. Finally, some variables tended to have a wider 95% CI than p-values, especially when considering the OS. The wider 95% CI width resulted in a relatively small sample and event size in some variables. A larger patient cohort study involving multi-institutional cooperative groups is needed to improve the statistical results.
Despite these limitations, our study offers some unique and significant findings and differs from previous studies in several aspects. To the best of our knowledge, this is the first study to evaluate how the normalization of tSUVmax and nSUVmax to the liver or blood pool affects prognostic values in cervical cancer. Metabolic parameters that did not demonstrate a prognostic role had a significant prognostic value for predicting tumor recurrence and death when normalized to the liver or blood pool. Moreover, this is the first research to demonstrate the prognostic value of TLR, TBR, NLR, and NBR in locally advanced cervical cancer treated with CCRT. TLR, TBR, NLR, and NBR were found to be independent prognostic factors for both DFS and OS.
5. Conclusions
Normalized tSUVmax and nSUVmax to the liver or blood pool showed a significant prognostic value in patients with locally advanced cervical cancer treated with CCRT. Moreover, the normalization of metabolic parameters improved the level of discrimination for the prediction of tumor recurrence and death, especially in the case of tSUVmax. The normalization of metabolic parameters to the liver or blood pool may enable the standardization of metabolic parameters for multicenter studies.
Conceptualization, G.O.C. and S.Y.J.; methodology, S.Y.J., S.W.L., D.G.H., H.J.L. and Y.H.L.; software, G.O.C.; validation, S.Y.J., S.-W.L., H.J.L. and D.G.H.; formal analysis, G.O.C., S.-H.P. and S.Y.L.; investigation, Y.H.L. and H.J.L.; resources, Y.H.L. and H.J.L.; data curation, G.O.C., S.-H.P. and S.Y.L.; writing—original draft preparation, G.O.C., S.-H.P. and Y.H.L.; writing—review and editing, S.Y.J., S.-W.L., D.G.H. and Y.S.L.; visualization, H.J.L. and Y.H.L.; supervision, S.-W.L., D.G.H. and Y.S.L.; project administration, S.-W.L., S.-H.P., G.O.C. and S.Y.J.; funding acquisition, S.W.L. All authors have read and agreed to the published version of the manuscript.
This work was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant no.s HI16C1501).
The authors declare no conflict of interest.
| CCRT | concurrent chemoradiotherapy |
| CI | confidence interval |
| DFS | disease-free survival |
| 18F-FDG PET/CT | F-18 fluorodeoxyglucose positron emission tomography/computed tomography |
| FIGO | Advanced International Federation of Gynecology and Obstetrics |
| HF | heterogeneity factor |
| HR | hazard ratio |
| SUVmax | maximum standardized uptake value |
| MTV | metabolic tumor volume |
| ROC | receiver operating characteristic |
| TLG | total lesion glycolysis |
| WB | whole body |
Enlarge this image.Figure 1. Kaplan–Meier survival plots of DFS according to the normalized tSUVmax and nSUVmax to the liver or blood: (A) TLR, (B) TBR, (C) NLR, and (D) NBR.
Enlarge this image.Figure 2. Kaplan–Meier survival plots of OS according to the normalized tSUVmax and nSUVmax to the liver or blood: (A) TLR, (B) TBR, (C) NLR, and (D) NBR.
Enlarge this image.Figure 3. Comparison of ROC curve analyses. (A) tSUVmax versus TLR and TBR for DFS. (B) nSUVmax versus NLR and NBR for DFS. (C) tSUVmax versus TLR and TBR for OS. (D) nSUVmax versus NLR and NBR for OS.
Clinicopathologic characteristics and PET metabolic parameters of patients with and without recurrence.
| Variables | All |
No Recurrence |
Recurrence |
p-Value |
|---|---|---|---|---|
| Age (years) | 55.1 ± 12.9 | 55.5 ± 12.8 | 54.3 ± 13.0 | 0.5965 |
| Histology (n, %) | 0.1244 | |||
| SCC | 141 (90.4) | 94 (93.1) | 47 (85.5) | |
| AC/ASC | 15 (9.6) | 7 (6.9) | 8 (14.5) | |
| FIGO stage (n, %) | 0.0006 | |||
| IIB | 120 (76.9) | 87 (86.1) | 33 (60.0 | |
| IIIA | 14 (9.0) | 8 (7.9) | 6 (10.9) | |
| IIIB | 16 (10.3) | 5 (5.0) | 11 (20.0) | |
| IVA | 6 (3.8) | 1 (1.0) | 5 (9.1) | |
| Tumor size (cm) | 4.6 ± 1.5 | 4.4 ± 1.4 | 5.1 ± 1.6 | 0.0080 |
| LN metastasis (n, %) | 102 (65.4) | 55 (54.5) | 47 (85.5) | 0.0001 |
| Paraaortic LN metastasis (n, %) | 25 (16.0) | 18 (17.8) | 7 (12.7) | 0.4088 |
| SCC antigen (ng/mL) | 20.8 ± 36.0 | 16.8 ± 35.6 | 28.1 ± 35.9 | 0.0715 |
| tSUVmax | 6.0 ± 2.5 | 6.0 ± 2.7 | 6.0 ± 2.1 | 0.9542 |
| TLR | 5.9 ± 4.4 | 6.8 ± 4.1 | 7.5 ± 4.8 | 0.3356 |
| TBR | 9.5 ± 6.0 | 9.2 ± 6.0 | 10.1 ± 6.1 | 0.3717 |
| nSUVmax | 3.3 ± 7.3 | 3.5 ± 5.1 | 7.9 ± 9.5 | 0.0003 |
| NLR | 2.3 ± 4.0 | 1.5 ± 2.5 | 3.8 ± 5.6 | 0.0008 |
| NBR | 3.1 ± 5.0 | 2.1 ± 3.4 | 5.0 ± 6.7 | 0.0005 |
AC = adenocarcinoma; ASC = adenosquamous cell carcinoma; FIGO = International Federation of Gynecology and Obstetrics; LN = lymph node; NBR = node-to-blood ratio; NLR = node-to-liver ratio; nSUVmax = nodal maximum standardized uptake value; SCC = squamous cell carcinoma; SUVmax = maximum standardized uptake value; TBR = tumor-to-blood ratio; TLR = tumor-to-liver ratio; tSUVmax = tumor maximum standardized uptake value.
Univariate analyses of clinical variable and quantitative metabolic parameters for DFS and OS.
| Variables | Disease-Free Survival | Overall Survival | ||||||
|---|---|---|---|---|---|---|---|---|
| Level | HR | 95% CI | p-Value | Level | HR | 95% CI | p-Value | |
| Age (years) | >49 | 1.00 | - | - | >49 | 1.00 | - | - |
| ≤49 | 1.25 | 0.70–2.23 | 0.4584 | ≤49 | 2.24 | 1.06–4.77 | 0.0355 | |
| Histology | SCC | 1.00 | - | - | SCC | 1.00 | - | - |
| AC/ASC | 2.04 | 0.78–4.98 | 0.1531 | AC/ASC | 1.57 | 0.46–5.35 | 0.4699 | |
| FIGO stage | IIB | 1.00 | - | - | IIB | 1.00 | - | - |
| >IIB | 4.64 | 2.31–9.32 | <0.0001 | >IIB | 7.30 | 2.85–18.69 | <0.0001 | |
| Tumor size | <4 cm | 1.00 | - | - | <4.2 cm | 1.00 | - | - |
| ≥4 cm | 2.48 | 1.41–4.34 | 0.0016 | ≥4.2 cm | 3.09 | 1.50–6.38 | 0.0023 | |
| LN metastases | Negative | 1.00 | - | - | Negative | 1.00 | - | - |
| Positive | 3.09 | 1.79–5.32 | <0.0001 | Positive | 2.96 | 1.41–6.18 | 0.0039 | |
| Paraaortic LN metastases | Negative | 1.00 | 0.71–2.85 | 0.3245 | Negative | 1.00 | 1.10–6.99 | 0.0305 |
| Positive | 1.42 | Positive | 2.78 | |||||
| Serum SCC | <19.3 | 1.00 | - | - | <19.3 | 1.00 | - | - |
| ≥19.3 | 3.71 | 1.94–7.12 | 0.0001 | ≥19.3 | 3.75 | 1.55–9.08 | 0.0035 | |
| tSUVmax | ≤4.81 | 1.00 | - | - | ≤6.43 | 1.00 | - | - |
| >4.81 | 1.39 | 0.26–7.30 | 0.6975 | >6.43 | 2.05 | 0.60–7.03 | 0.2528 | |
| TLR | ≤4.99 | 1.00 | - | - | ≤5.14 | 1.00 | - | - |
| >4.99 | 2.26 | 1.37–3.87 | 0.0031 | >5.14 | 4.23 | 2.05–8.70 | 0.0001 | |
| TBR | ≤6.96 | 1.00 | - | - | ≤7.52 | 1.00 | - | |
| >6.96 | 2.23 | 1.31–3.81 | 0.0033 | >7.52 | 4.26 | 2.07–8.79 | 0.0001 | |
| nSUVmax | ≤4.63 | 1.00 | - | - | ≤4.91 | 1.00 | - | - |
| >4.63 | 6.86 | 3.81–12.36 | < 0.0001 | >4.91 | 7.72 | 3.53–16.88 | < 0.0001 | |
| NLR | ≤2.02 | 1.00 | - | - | ≤2.61 | - | - | |
| >2.02 | 6.26 | 3.51–11.14 | < 0.0001 | >2.61 | 10.27 | 4.51–23.39 | <0.0001 | |
| NBR | ≤1.97 | 1.00 | - | - | ≤3.07 | 1.00 | - | - |
| >1.97 | 4.74 | 2.76–8.15 | < 0.0001 | >3.07 | 8.02 | 3.71–17.36 | <0.0001 | |
AC = adenocarcinoma; ASC = adenosquamous cell carcinoma; CI = confidence interval; FIGO = International.
Multivariate analyses of PET parameters in relation to DFS and OS after adjusting for clinicopathologic factors.
| Variables | Disease-Free Survival | Overall Survival | ||||||
|---|---|---|---|---|---|---|---|---|
| Level | HR | 95% CI | p-Value | Level | HR | 95% CI | p-Value | |
| tSUVmax | ≤4.81 | 1.00 | - | - | ≤6.43 | 1.00 | - | - |
| >4.81 | 0.29 | 0.04–2.38 | 0.2478 | >6.43 | 0.66 | 0.08–5.85 | 0.7117 | |
| TLR | ≤4.9885 | 1.00 | - | - | ≤5.1368 | 1.00 | - | - |
| >4.9885 | 1.48 | 0.76–2.87 | 0.2509 | >5.1368 | 4.16 | 1.19–14.50 | 0.0252 | |
| TBR | ≤6.9612 | 1.00 | - | - | ≤7.5235 | 1.00 | - | - |
| >1.9612 | 1.42 | 0.75–2.70 | 0.2864 | >7.5235 | 3.01 | 1.04–8.70 | 0.0415 | |
| nSUVmax | ≤4.63 | 1.00 | - | - | ≤4.91 | 1.00 | - | - |
| >4.63 | 4.06 | 1.74–9.44 | 0.0012 | >4.91 | 6.99 | 1.56–31.25 | 0.0109 | |
| LNR | ≤2.0021 | 1.00 | - | - | ≤2.6098 | 1.00 | - | - |
| >2.0021 | 3.54 | 1.53–8.19 | 0.0032 | >2.6098 | 4.84 | 1.58–14.81 | 0.0057 | |
| NBR | ≤1.9681 | 1.00 | - | - | ≤3.0705 | 1.00 | - | - |
| >1.9681 | 3.38 | 1.02–11.19 | 0.0457 | >3.0705 | 6.87 | 1.55–30.54 | 0.0113 | |
CI = confidence interval; FIGO = International Federation of Gynecology and Obstetrics; HR = hazard ratio; NBR = node-to-blood ratio; NLR = node-to-liver ratio; nSUVmax = nodal maximum standardized uptake value; SUVmax = maximum standardized uptake value; TBR = tumor-to-blood ratio; TLR = tumor-to-liver ratio; tSUVmax = tumor maximum standardized uptake value.
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References
1. Green, J.A.; Kirwan, J.M.; Tierney, J.F.; Symonds, P.; Fresco, L.; Collingwood, M.; Williams, C.J. Survival and recurrence after concomitant chemotherapy and radiotherapy for cancer of the uterine cervix: A systematic review and meta-analysis. Lancet; 2001; 358, pp. 781-786. [DOI: https://dx.doi.org/10.1016/S0140-6736(01)05965-7]
2. Whitney, C.W.; Sause, W.; Bundy, B.N.; Malfetano, J.H.; Hannigan, E.V.; Fowler, W.C., Jr.; Clarke-Pearson, D.L.; Liao, S.Y. Randomized comparison of fluorouracil plus cisplatin versus hydroxyurea as an adjunct to radiation therapy in stage IIB-IVA carcinoma of the cervix with negative para-aortic lymph nodes: A Gynecologic Oncology Group and Southwest Oncology Group study. J. Clin Oncol.; 1991; 17, pp. 1339-1348. [DOI: https://dx.doi.org/10.1200/JCO.1999.17.5.1339] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10334517]
3. Collaboration Chemoradiotherapy for Cervical Cancer Meta-Analysis Collaboration. Reducing uncertainties about the effects of chemoradiotherapy for cervical cancer: A systematic review and metaanalysis of individual patient data from 18 randomized trials. J. Clin. Oncol.; 2008; 26, pp. 5802-5812. [DOI: https://dx.doi.org/10.1200/JCO.2008.16.4368]
4. Lee, D.W.; Kim, Y.T.; Kim, J.H.; Kim, S.; Kim, S.W.; Nam, E.J.; Kim, J.W. Clinical significance of tumor volume and lymph node involvement assessed by MRI in stage IIB cervical cancer patients treated with concurrent chemoradiation therapy. J. Gynecol. Oncol.; 2010; 21, pp. 18-23. [DOI: https://dx.doi.org/10.3802/jgo.2010.21.1.18] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20379443]
5. Endo, D.; Todo, Y.; Okamoto, K.; Ninobe, S.; Kato, H.; Nishiyama, N. Prognostic factors for patients with cervical cancer treated with concurrent chemoradiotherapy: A retrospective analysis in a Japanese cohort. J. Gynecol. Oncol.; 2015; 26, pp. 12-18. [DOI: https://dx.doi.org/10.3802/jgo.2015.26.1.12] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25310853]
6. Kidd, E.A.; Siegel, B.A.; Dehdashti, F.; Grigsby, P.W. Pelvic lymph node F-18 fluorodeoxyglucose uptake as a prognostic biomarker in newly diagnosed patients with locally advanced cervical cancer. Cancer; 2010; 116, pp. 1469-1475. [DOI: https://dx.doi.org/10.1002/cncr.24972]
7. Onal, C.; Guler, O.C.; Reyhan, M.; Yapar, A.F. Prognostic value of 18F-fluorodeoxyglucose uptake in pelvic lymph nodes in patients with cervical cancer treated with definitive chemoradiotherapy. Gynecol. Oncol.; 2015; 137, pp. 40-46. [DOI: https://dx.doi.org/10.1016/j.ygyno.2015.01.542]
8. Bollineni, V.R.; Kramer, G.; Liu, Y.; Melidis, C.; deSouza, N.M. A literature review of the association between diffusion-weighted MRI derived apparent diffusion coefficient and tumour aggressiveness in pelvic cancer. Cancer Treat. Rev.; 2015; 41, pp. 496-502. [DOI: https://dx.doi.org/10.1016/j.ctrv.2015.03.010]
9. Chong, G.O.; Jeong, S.Y.; Park, S.H.; Lee, Y.H.; Lee, S.W.; Hong, D.G.; Kim, J.C.; Lee, Y.S.; Cho, Y.L. Comparison of the Prognostic Value of F-18 Pet Metabolic Parameters of Primary Tumors and Regional Lymph Nodes in Patients with Locally Advanced Cervical Cancer Who Are Treated with Concurrent Chemoradiotherapy. PLoS ONE; 2015; 10, e0137743. [DOI: https://dx.doi.org/10.1371/journal.pone.0137743]
10. Kostakoglu, L.; Chauvie, S. Metabolic Tumor Volume Metrics in Lymphoma. Metabolic Tumor Volume Metrics in Lymphoma. Semin. Nucl. Med.; 2018; 48, pp. 50-66. [DOI: https://dx.doi.org/10.1053/j.semnuclmed.2017.09.005] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29195618]
11. Schulte, M.; Brecht-Krauss, D.; Werner, M.; Hartwig, E.; Sarkar, M.R.; Keppler, P.; Kotzerke, J.; Guhlmann, A.; Delling, G.; Reske, S.N. Evaluation of neoadjuvant therapy response of osteogenic sarcoma using FDG PET. J. Nucl. Med.; 1999; 40, pp. 1637-1643. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10520703]
12. Chiaravalloti, A.; Danieli, R.; Abbatiello, P.; Di Pietro, B.; Travascio, L.; Cantonetti, M.; Guazzaroni, M.; Orlacchio, A.; Simonetti, G.; Schillaci, O. Factors affecting intrapatient liver and mediastinal blood pool ¹⁸F-FDG standardized uptake value changes during ABVD chemotherapy in Hodgkin’s lymphoma. Eur. J. Nucl. Med. Mol. Imaging; 2014; 41, pp. 1123-1132. [DOI: https://dx.doi.org/10.1007/s00259-014-2703-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24562647]
13. Park, J.; Chang, K.J.; Seo, Y.S.; Byun, B.H.; Choi, J.H.; Moon, H.; Lim, I.; Kim, B.I.; Choi, C.W.; Lim, S.M. Tumor SUVmax Normalized to Liver Uptake on (18)F-FDG PET/CT Predicts the Pathologic Complete Response After Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Nucl. Med. Mol. Imaging; 2014; 48, pp. 295-302. [DOI: https://dx.doi.org/10.1007/s13139-014-0289-x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26396634]
14. Shin, S.; Pak, K.; Kim, I.J.; Kim, B.S.; Kim, S.J. Prognostic Value of Tumor-to-Blood Standardized Uptake Ratio in Patients with Resectable Non-Small-Cell Lung Cancer. Nucl. Med. Mol. Imaging; 2017; 51, pp. 233-239. [DOI: https://dx.doi.org/10.1007/s13139-016-0456-3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28878849]
15. Pecorelli, S. Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium. Int. J. Gynaecol. Obstet.; 2009; 105, pp. 103-104. [DOI: https://dx.doi.org/10.1016/j.ijgo.2009.02.012]
16. Lee, W.K.; Chong, G.O.; Jeong, S.Y.; Lee, H.J.; Park, S.; Ryu, J.M.; Choi, Y.S.; Kang, S.; Koo, Y.; Lee, D.H. et al. Prognosis-Predicting Model Based on [18F]fluorodeoxyglucose PET Metabolic Parameters in Locally Advanced Cervical Cancer Patients Treated with Concurrent Chemoradiotherapy: Multi-Center Retrospective Study. J. Clin. Med.; 2020; 9, 427. [DOI: https://dx.doi.org/10.3390/jcm9020427]
17. Han, S.; Kim, H.; Kim, Y.J.; Suh, C.H.; Woo, S. Prognostic Value of Volume-Based Metabolic Parameters of 18F-FDG PET/CT in Uterine Cervical Cancer: A Systematic Review and Meta-Analysis. AJR Am. J. Roentgenol.; 2018; 211, pp. 1112-1121. [DOI: https://dx.doi.org/10.2214/AJR.18.19734]
18. Chu, K.P.; Murphy, J.D.; La, T.H.; Krakow, T.E.; Iagaru, A.; Graves, E.E.; Hsu, A.; Maxim, P.G.; Loo, B.; Chang, D.T. et al. Prognostic value of metabolic tumor volume and velocity in predicting head-and-neck cancer outcomes. Int. J. Radiat. Oncol. Biol. Phys.; 2012; 83, pp. 1521-1527. [DOI: https://dx.doi.org/10.1016/j.ijrobp.2011.10.022]
19. Lee, J.W.; Kim, S.K.; Lee, S.M.; Moon, S.H.; Kim, T. Detection of hepatic metastases using dual-time-point FDG PET/CT scans in patients with colorectal cancer. Mol. Imaging Biol.; 2011; 13, pp. 565-572. [DOI: https://dx.doi.org/10.1007/s11307-010-0394-x]
20. Kidd, E.A.; Siegel, B.A.; Dehdashti, F.; Rader, J.S.; Mutch, D.S.; Powell, M.A.; Grigsby, P.W. Lymph node staging by positron emission tomography in cervical cancer: Relationship to prognosis. J. Clin. Oncol.; 2010; 28, pp. 2108-2113. [DOI: https://dx.doi.org/10.1200/JCO.2009.25.4151]
21. Ramlov, A.; Kroon, P.S.; Jürgenliemk-Schulz, I.M.; De Leeuw, A.C.; Gormsen, L.C.; Fokdal, L.U.; Tanderup, K.; Lindegaard, J.C. Impact of radiation dose and standardized uptake value of (18) FDG PET on nodal control in locally advanced cervical cancer. Acta Oncol.; 2015; 54, pp. 1567-1573. [DOI: https://dx.doi.org/10.3109/0284186X.2015.1061693] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26271799]
22. Cho, H.; Kim, S.H.; Kim, H.; Koh, Y.W.; Kim, S.; Choi, E.C.; Yun, M. Lymph Node With the Highest FDG Uptake Predicts Distant Metastasis-Free Survival in Patients With Locally Advanced Nasopharyngeal Carcinoma. Clin. Nucl. Med.; 2018; 43, pp. e220-e225. [DOI: https://dx.doi.org/10.1097/RLU.0000000000002145] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29762247]
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; Shin Young Jeong 2 ; Lee, Yoon Hee 1 ; Shin-Hyung, Park 3 ; Hyun Jung Lee 4
; Sang-Woo, Lee 2 ; Hong, Dae Gy 1 ; Yoon Soon Lee 1 1 Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu 41944, Korea;
2 Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Korea;
3 Department of Radiation Oncology, School of Medicine, Kyungpook National University, Daegu 41944, Korea;
4 Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu 41944, Korea;