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Clinical and prognostic value of tumor volumetric parameters in melanoma patients undergoing 18F-FDG-PET/CT: a comparison with serologic markers of tumor burden and inflammation

Cancer Imaging volume 20, Article number: 44 (2020) Cite this article

Abstract

Background

To investigate the association of tumor volumetric parameters in melanoma patients undergoing 18F-FDG-PET/CT with serologic tumor markers and inflammatory markers and the role as imaging predictors for overall survival.

Methods

A patient cohort with advanced melanoma undergoing 18F-FDG-PET/CT for planning metastasectomy between 04/2013 and 01/2015 was retrospectively included. The volumetric PET parameters whole-body MTV and whole-body TLG as well as the standard uptake value (SUV) peak were quantified using 50%-isocontour volumes of interests (VOIs) and then correlated with the serologic parameters lactate dehydrogenase (LDH), S-100 protein, c-reactive protein (CRP) and alkaline phosphatase (AP). PET parameters were dichotomized by their respective medians and correlated with overall survival (OS) after PET/CT. OS was compared between patients with or without metastases and increased or not-increased serologic parameters.

Results

One hundred seven patients (52 female; 65 ± 13.1yr.) were included. LDH was strongly associated with MTV (rP = 0.73, p <  0.001) and TLG (rP = 0.62, p <  0.001), and moderately associated with SUVpeak (rP = 0.55, p <  0.001). S-100 protein showed a moderate association with MTV (rP = 0.54, p <  0.001) and TLG (rP = 0.48, p <  0.001) and a weak association with SUVpeak (rP = 0.42, p <  0.001). A strong association was observed between CRP and MTV (rP = 0.66, p <  0.001) and a moderate to weak association between CRP and TLG (rP = 0.53, p <  0.001) and CRP and SUVpeak (rP = 0.45, p <  0.001). For differentiation between patients with or without metastases, receiver operating characteristic (ROC) analysis revealed a cut-off value of 198 U/l for serum LDH (AUC 0.81, sensitivity 0.80, specificity 0.72).

Multivariate analysis for OS revealed that both MTV and TLG were strong independent prognostic factors. TLG, MTV and SUVpeak above patient median were accompanied with significantly reduced estimated OS compared to the PET parameters below patient median (e.g. TLG: 37.1 ± 3.2 months vs. 55.9 ± 2.5 months, p <  0.001). Correspondingly, both elevated serum LDH and S-100 protein were accompanied with significantly reduced OS (36.5 ± 4.9 months and 37.9 ± 4.4 months) compared to normal serum LDH (49.2 ± 2.4 months, p = 0.01) and normal S-100 protein (49.0 ± 2.5 months, p = 0.01).

Conclusions

Tumor volumetric parameters in 18F-FDG-PET/CT serve as prognostic imaging biomarkers in patients with advanced melanoma which are associated with established serologic tumor markers and inflammatory markers.

Background

Malignant melanoma incidence is increasing worldwide. At time of diagnosis, most patients have localized disease that can be successfully treated by complete surgical resection, however, 28% of stage IV melanoma patients develop visceral metastases [1]. Recently, new treatment approaches such as antibodies targeting the immune checkpoints T-lymphocyte-associated protein 4 (CTLA-4) or the programmed cell death protein 1 (PD-1) either used alone or as combined immunotherapy remarkably improved prognosis of advanced melanoma. However, about 40–50% of patients fail to respond to therapy [2,3,4,5].

Serum lactate dehydrogenase (LDH) is released through cell damage and has been established as a biochemical marker of tumor load in various tumor entities including malignant melanoma [6]. Serum LDH is part of the AJCC melanoma staging guideline for metastatic melanoma patients [6]. Elevated serum LDH level is associated with poor survival and poor therapy response rates [5, 7, 8].

The calcium-binding, acidic cytoplasmic S-100 protein has been shown to be a specific and reliable immunohistochemical marker in malignant melanoma which correlates with clinical melanoma stage and poor survival [9,10,11,12,13]. Besides, several studies have found that the inflammatory markers c-reactive protein (CRP) and alkaline phosphatase (AP) are independent prognostic biomarkers in patients with both early-stage and advanced-stage melanoma [14,15,16].

Whole-body 18F-FDG-PET/CT is the imaging modality of choice for staging of advanced (stage III and IV) melanoma to provide information on the presence and location of metastases [17]. For assessing the degree of 18F-FDG accumulation in diverse cancer types, the volumetric parameters MTV and TLG have been proposed, as they reflect the whole volume of the tumor rather than the maximum standardized uptake value (SUVmax) which represents only the most active part of the tumor [18,19,20]. The point spread function (PSF) reconstruction as used in modern PET scanners not only improves sensitivity but it overestimates SUVmax [21]. The SUVpeak has been shown to provide a slightly more robust alternative for assessing the most metabolically active region of a tumor [22,23,24,25].

In a recent study of Ito et al., whole-body MTV obtained from baseline PET/CT scans has been shown to be a strong independent prognostic factor among other clinical prognostic factors in melanoma patients treated with ipilimumab [26]. Son et al. observed that among patients with primary cutaneous melanoma, both MTV and TLG are strong prognosticators of survival [27].

Melanoma patients with an elevated serum LDH level have a higher tumor 18F-FDG uptake, however, without full coincidence [8]. The prediction of patient prognosis and the assessment of early response to immunotherapy have become areas of intensive investigation, because unnecessary toxicities or aggressive treatments should be avoided [28].

In this study we investigated the association of tumor volumetric parameters in melanoma patients undergoing 18F-FDG-PET/CT with serologic tumor markers and inflammatory markers and the role as independent imaging predictors for overall survival.

Methods

Ethics approval was obtained from the local ethics committee (Project number: 064/2013B01). Informed consent was obtained from all patients included in the study.

Patient cohort

The underlying study population consisted of patients with advanced melanoma, who were enrolled in a local PET/CT registry between April 2013 and January 2015 [29, 30]. All patients were initially intended for radical metastasectomy based on conventional imaging prior to the PET/CT examination. According to the melanoma guideline, PET/CT imaging is routinely recommended for patients with stage III and IV melanoma and in case of high risk melanoma (ulceration or tumor thickness above 4 mm) or suspect findings in the follow-up (i.e. US or serologic tumor markers) in patients with stage I and II [31].

After having performed the PET/CT scan, patients were re-evaluated regarding the intended management plan (surgery, systemic therapy, watchful watching). The final treatment was a consensus decision of a tumor-board on the basis of the PET/CT result in agreement with the patients. In case of 18F-FDG avid metastases, a corresponding surgical or systemic therapy was initiated. If no vital metastases were confirmed, patients underwent watchful waiting.

18F-FDG-PET/CT imaging

All PET/CT examinations were performed on a state-of-the art clinical scanner (Biograph mCT®, Siemens Healthineers). All patients fasted overnight before examination. Approximately 300 MBq 18F-FDG were injected intravenously 60 min prior to image acquisition. Standardized CT examination protocols included weight-adapted 90–120 ml intravenous CT contrast agent (Ultravist 370®, Schering AG). Portal-venous phase acquisitions were obtained with 70s delay time using a tube voltage of 120 kV and a reference dose of 200mAs. Image reconstruction was performed using iterative CT reconstruction (Siemens SAFIRE®, Forchheim).

PET was acquired from the skull to the mid thigh level over six to eight bed positions and reconstructed using a 3D ordered subset expectation maximization algorithm (two iterations, 21 subsets, Gaussian filter 2.0 mm, matrix size 400 × 400, and slice thickness 2.0 mm). In case of known metastases at the extremities, PET acquisition was expanded accordingly. PET acquisition time was 2–3 min per bed position.

Quantification of tumor lesion 18F-FDG uptake and serologic markers

Segmentation of metastatic tumor lesions was performed by two readers in consensus using approved software for quantification of PET parameters on Syngo.via VB 30A (Siemens Healthineers). Metastatic lesions included all lesions which were characterized by substantially increased 18F-FDG uptake. Segmentation of each lesion was manually performed using 50%-isocontour VOIs for quantification. Whole-body MTV and whole-body TLG were calculated as the sum of all quantified metastatic lesions per patient. The SUVpeak of the metastatic lesion with the highest 18F-FDG uptake in a patient was calculated using an automated computed maximal mean SUV in a 1.0-cm3 spherical volume within the tumor [24]. The documented patient’s SUVpeak is defined as the highest value derived from all lesions within a patient.

As part of the staging procedures in melanoma patients, serum LDH, serum S-100 protein and the acute-phase proteins CRP and AP were routinely determined by the in-house laboratory. Serologic tumor markers were extracted from the clinical data base within 45 days before up to 7 days after PET/CT and acute-phase-proteins 20 days before up to 7 days after PET/CT. The upper limits of the reference ranges were: 250 U/l for serum LDH, 0.1 μg/l for serum S-100 protein, 0.5 μg/dl for CRP and 130 U/l for AP.

Data analysis

Statistical analyses and graphical illustrations were performed using SPSS Version 22 (IBM Corporation).

Association of PET parameters and serologic markers

First, we checked for potential associations between the serum parameters and the whole-body MTV, whole-body TLG and SUVpeak by direct correlation of the absolute values. Second, we analyzed these associations separately in patients undergoing surgical or systemic treatment after PET/CT. Interactions between PET parameters and serologic markers were analyzed by bivariate correlation. Further, a multiple linear regression was calculated to predict the whole-body MTV, whole-body TLG and SUVpeak based on serologic markers. The strength of the linear relationships between the variables was measured by calculating the Pearson correlation coefficient which was denoted by rP. The predictive value of serum LDH for differentiation between patients with and without metastases and between patients with whole-body MTV, whole-body TLG and SUVpeak above or below the cohort’s median was assessed by computing a receiver operating characteristic (ROC) curve and by calculation of the area under the curve (AUC).

Survival analysis

Overall patient survival recorded between date of PET/CT and death was assessed for all patients based on patient records. In a first step, we performed an univariate analysis to identify PET markers including whole-body MTV, whole-body TLG and SUVpeak as well as serologic parameters including LDH, serum-100, AP and CRP associated with OS. In a second step, the factors that were identified as being significant by univariate analysis (p <  0.05) were entered into a Cox multivariate regression analysis model. A forward stepwise multivariate regression analysis was carried out to identify the factors that remained significant after multivariate analysis. The variables with p <  0.05 were entered and those with p > 0.10 were removed.

Third, we compared the OS between patients with and without metastases on PET/CT, between patients with whole-body MTV, whole-body TLG and SUVpeak above or below the cohort’s median and between patients with normal or elevated serologic parameters (serum LDH, serum S-100 protein, CRP, AP). Fourth, OS was analyzed in the patient subgroups undergoing surgical or systemic treatment after PET/CT for normal and elevated PET parameters and serologic markers.

To analyze differences of overall survival between the groups, we performed Kaplan-Meier analyses. The differences between the Kaplan Meier survival curves were evaluated by non-parametric log-rank tests. Optimal thresholds were identified for each marker, which best separated the subgroups (lowest p-value from log-rank test). The significance level was set at a p-value of < 0.05. Estimated mean survival times were derived from Kaplan-Meier analyses.

Results

Study population

107 consecutive patients (52 female; mean age 65 ± 13.1 years) with malignant melanoma who were selected for potential surgical metastasectomy prior PET/CT were evaluated. Tumors were staged according to the eighth edition AJCC Cancer Staging Manual [32].

Five patients had stage I, three patients stage II, 42 patients stage III and 57 patients stage IV melanoma according to PET/CT. The eight early stage (I and II) patients had been scheduled for surgery for suspicious findings in CT or US. On the basis of clinical findings and PET/CT results, 52 patients (48.6%) were selected for surgical treatment whereas 32 patients (29.9%) were selected for systemic therapy. Two patients (1.9%) underwent palliative radiotherapy and one patient (0.9%) underwent isolated extremity perfusion. 20 patients (18.7%) underwent watchful waiting. Detailed patient characteristics are listed in Table 1.

Table 1 Patient characteristics
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Association of PET parameters and serologic markers

PET/CT findings and results of the laboratory are shown in Table 2. A total of 87/107 patients (81.3%) had histologically confirmed metastases. 18F-FDG avid lesions have been identified in 76/107 patients (71.0%) by PET/CT allowing for manual segmentation. 11/107 patients (10.3%) had small cutaneous in-transit metastases which were either not completely recorded by PET/CT scan or not clearly quantifiable (Fig. 1).

Table 2 PET/CT and laboratory findings
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Fig. 1
figure 1

Patient flowchart

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Current serologic parameters were available as follows:

  • Serum LDH was available in 84/107 patients (78.5%), which could be correlated with PET parameters in 67 patients.

  • Serum S-100 protein was available in 82/107 patients (76.6%), which could be correlated with PET parameters in 68 patients.

  • CRP was available in 72/107 patients (67.3%), which could be correlated with PET parameters in 59 patients.

  • AP was available in 68/107 patients (63.6%), which could be correlated with PET parameters in 60 patients.

Serum LDH and serum S-100 protein showed a significantly positive correlation (rP = 0.82, p <  0.001). In the whole patient cohort, serum LDH was strongly associated with whole-body MTV (rP = 0.73, p <  0.001) and moderately associated with whole-body TLG (rP = 0.62, p <  0.001) and SUVpeak (rP = 0.55, p <  0.001) (Fig. 2). S-100 protein showed a moderate association with MTV (rP = 0.54, p <  0.001) and TLG (rP = 0.48, p <  0.001) and a weak association with SUVpeak (rP = 0.42, p <  0.001). A strong association was observed between CRP and MTV (rP = 0.66, p <  0.001) and a moderate to weak association between CRP and TLG (rP = 0.53, p <  0.001) and CRP and SUVpeak (rP = 0.45, p <  0.001). A weak association was also observed between AP and MTV (rP = 0.39, p <  0.001) and AP and TLG (rP = 0.29, p <  0.01). AP and SUVpeak were not associated (rP = 0.16, p = 0.2).

Fig. 2
figure 2

a Bivariate correlation curves between serum LDH and whole-body TLG (rP = 0.62, p <  0.001), between serum LDH and whole-body MTV (rP = 0.73, p <  0.001) (b) and between serum LDH and SUVpeak (rP = 0.55, p <  0.001) (c)

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The separate analysis for patients receiving surgical treatment after PET/CT revealed strong associations between serum LDH and MTV (rP = 0.82, p <  0.001) and serum LDH and TLG (rP = 0.74, p <  0.001) and between serum S-100 protein and MTV (rP = 0.66, p <  0.001). Moderate associations were observed between S-100 protein and TLG (rP = 0.60, p <  0.001), SUVpeak and serum LDH (rP = 0.60, p <  0.001) and SUVpeak and S-100 protein (rP = 0.48, p <  0.001).

In the subgroup of patients undergoing systemic treatment after PET/CT, MTV and serum LDH (rP = 0.61, p <  0.001) and MTV and S-100 protein were moderately associated (rP = 0.56, p <  0.001). Moderate associations were also observed between TLG and serum LDH (rP = 0.47, p <  0.001), TLG and S-100 protein (rP = 0.42, p <  0.001), SUVpeak and serum LDH (rP = 0.51, p <  0.001) and SUVpeak and S-100 protein (rP = 0.43, p <  0.001). The results of the bivariate correlation analyses are listed in Table 3.

Table 3 Bivariate correlation analysis between PET and serologic parameters
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The ROC analysis for differentiation between patients with and without metastases revealed a cut-off value of 198 U/l for serum LDH with an AUC of 0.81 (sensitivity 0.80; specificity 0.72). A significant regression equation was found: F (4,52) = 26.9, p <  0.0001, with R2 of 0.67. Both serum LDH and CRP were significant predictors of whole-body MTV. Whole-body MTV increased 0.84 cm3 for each U/l serum LDH and 1.83 cm3 for each mg/dl CRP.

Overall survival

At the time of analysis in February 2020, 55/107 patients (51.4%) had died, whereas 47/107 patients (43.9%) were still alive. In 5/107 patients (4.7%) survival data were not available.

Univariate analysis revealed that whole-body MTV (< 2.74cm3 vs. > 2.74cm3), whole-body TLG (< 13.0 vs. > 13.0), SUVpeak (< 6.7 vs. > 6.7), as well as the serologic parameters LDH (normal vs. increased), and S-100 protein (normal vs. increased) were significant predictors of overall survival. Multivariate analysis for overall survival including the significant parameters revealed that whole-body TLG greater than 13 (hazard ratio [HR], 3.30; 95% CI, 1.6–6.80, p = 0.001), and whole-body MTV greater than 2.74 cm3 (HR, 2.29; 95% CI, 1.12–4.70, p = 0.02) remained independent prognostic factors (Table 4).

Table 4 Univariate and multivariate survival analyses of PET and serologic parameters
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Patients with 18F-FDG avid metastases had a significantly reduced estimated OS (43.1 ± 2.7 months) compared to patients without 18F-FDG avid metastases (55.7 ± 2.8 months, p <  0.01) (Fig. 3a). Patients with whole-body MTV, whole-body TLG or SUVpeak above the cohort’s median had a significantly (p <  0.001) reduced estimated OS compared to patients with corresponding PET parameters below cohort’s median (MTV: 42.8 ± 3.3 months vs. 51.2 ± 2.7 months; TLG: 37.1 ± 3.2 months vs. 55.9 ± 2.5 months; SUVpeak: 39.9 ± 3.2 months vs. 54.1 ± 2.7 months) (Fig. 3).

Fig. 3
figure 3

a Estimated overall survival in patients with MTV above the cohort’s median (42.8 ± 3.3 months) compared to patients with MTV below the cohort’s median (51.2 ± 2.7 months, p <  0.001), (b) in patients with TLG above the cohort’s median (37.1 ± 3.2 months) compared to patients with TLG below the cohort’s median (55.9 ± 2.5 months, p <  0.001) and (c) in patients with SUVpeak above the cohort’s median (39.9 ± 3.2 months) compared to patients with SUVpeak below the cohort’s median (54.1 ± 2.7 months, p <  0.001)

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Correspondingly, an elevated serum LDH was accompanied with a significantly lower OS (36.5 ± 4.9 months) compared to patients with normal serum LDH (49.2 ± 2.4 months, p = 0.01), which was also observed in patients with an elevated serum S-100 protein (37.9 ± 4.4 months) compared to patients with a normal serum S-100 protein (49.0 ± 2.5 months, p = 0.01) (Fig. 4). No differences in OS could be observed between patients with an elevated (43.3 ± 4.4 months) or normal AP (45.3 ± 3.2 months, p = 0.48) and an elevated (47.8 ± 3.5 months) or normal CRP (41.9 ± 4.4 months, p = 0.41).

Fig. 4
figure 4

(a) Estimated overall survival in patients with elevated serum LDH (36.5 ± 4.9 months) compared to patients with normal serum LDH (49.2 ± 2.4 months, p = 0.01) and (b) in patients with elevated serum S-100 protein (37.9 ± 4.4 months) compared to patients with normal serum S-100 protein (49.0 ± 2.5 months, p = 0.01)

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In the subgroup of patients undergoing surgical treatment after PET/CT, OS was significantly reduced in case of 18F-FDG avid metastases (n = 41, 51.7 ± 4.0 months) compared to patients without 18F-FDG avid metastases (n = 11, 60.9 ± 7.2 months, p <  0.05). This observation was similar to patients with elevated serum S-100 protein (41.8 ± 5.9 months) compared to patients with normal serum S-100 protein (50.2 ± 3.4 months), however, without statistical significance (p = 0.07).

Discussion

In this study, we investigated the clinical and prognostic value of volumetric PET parameters in a patient cohort with advanced melanoma undergoing 18F-FDG-PET/CT by direct correlation with the established serologic tumor markers LDH and S-100 protein and the inflammatory markers AP and CRP.

A strong association was observed between the whole-body MTV and LDH, whereas whole-body TLG was moderately associated with LDH. Moderate associations were also detected between LDH and SUVpeak and between S-100 protein and both MTV and TLG. Similar associations were observed in the patient subgroups who underwent surgical or systemic treatment after PET/CT.

To the best of our knowledge, there are no reports so far reporting on direct correlations of volumetric imaging markers and the established serologic tumor markers LDH and S-100 protein in melanoma. A possible explanation for the strong association between MTV, TLG and LDH is that the conversion of pyruvate to lactate by LDH produces NAD+, and the NADH/NAD+ ratio is thought to be important in various oxidoreductase-based metabolic reactions which are upregulated in melanoma cells [33]. Increasing serum values of LDH are correlated with tumor progression and are therefore found in higher tumor stages [13]. However, it has been shown that LDH is less sensitive in early disease stages and as a predictor of metastatic relapse [34, 35].

The volumetric parameters MTV/TLG and the SUVpeak showed a stronger association with serum LDH as with CRP. This is in concordance with the study results of de Heer et al. showing significantly higher MTV, TLG and SUVpeak in melanoma patients with elevated LDH [8]. CRP is synthetized in response to cytokines such as interleukin-6 (IL-6), which is produced by melanoma cells [36]. However, CRP might also be synthetized by activated T cells, macrophages or monocytes which are also responsible for elevated IL-6 levels in response to inflammation [37]. Therefore, CRP is not a marker which is exclusively increased in melanoma.

We observed a strong association between the serum markers LDH and S-100 protein. Therefore, it is not surprising, that S-100 protein was also associated with MTV, TLG and SUVpeak. It has been observed that S-100 protein has a prometastatic attribute in melanoma by influencing cell growth and differentiation and interaction with coexpressed receptor for advanced glycation endproducts (RAGE) [38, 39]. It has been shown that serum concentrations of S-100 correlate with clinical melanoma stage [13]. In asymptomatic melanoma patients, S-100 protein has been proven to be a useful tool for discovering tumor progression which could be confirmed by PET/CT [40]. However, abnormal elevated S-100 levels may attributed to other causes such as inflammatory and infectious diseases [41,42,43].

Our survival analysis revealed that both whole-body MTV and whole-body TLG are independent prognostic factors. Patients with 18F-FDG avid metastases or MTV/TLG and SUVpeak above the cohort’s median had a significantly reduced survival, which was similarly observed in patients with an elevated serum LDH or elevated serum S-100 protein. Patients undergoing surgical treatment after PET/CT had a reduced OS in case of 18F-FDG avid metastases, which was similarly observed in patients with elevated serum S-100 protein above the cohort’ median.

This is in concordance with other studies demonstrating that both whole-body MTV, whole-body TLG, SUVpeak and serum LDH are independent prognostic factors in patients with malignant melanoma [8, 26, 27]. Ito et al. combined information about PET parameters and clinical factors showing that melanoma patients with high serum LDH in combination with elevated whole-body MTV had a worse prognosis than patients with a high serum LDH or an elevated MTV alone [25]. Differences in survival between patients with a sum of SUVpeak above and below the cohort’s median were not significant, however, a trend was noted. In their study, patients were divided into subgroups with increased or not increased LDH referring to the upper limit of the normal range. We could additionally show that serum LDH and PET parameters are directly associated which may help clinicians to early identify melanoma patients who would particularly benefit from PET/CT imaging for staging. Further, Ito et al. included only patients with unresectable melanoma who were planned for ipilimumab immunotherapy and a part of their patient cohort had already undergone previous systemic therapy [44]. Son et al. evaluated the prognostic relevance of MTV, TLG and SUVmax in patients with primary cutaneous malignant melanoma [27]. The volumetric parameters MTV and TLG were significant prognostic factors for melanoma-specific survival, whereas SUVmax was not a significant factor [27]. Tumor volumetric parameters assessed on baseline PET/CT have been proven to be of prognostic value in various malignancies, including non-small cell lung cancer [45], lymphoma [46], breast cancer [47], head and neck cancer [48], and pancreatic cancer [20]. The clinical applicability of standard uptake value (SUV) for prognostic purposes in melanoma patients is still under discussion [49, 50]. The intra- and inter-patient heterogeneity in tumor lesion 18F-FDG uptake (SUV) among metastatic melanoma patients are major limitations [8].

S-100 protein and LDH have been reported as early prognostic markers for response and overall survival in melanoma patients treated with anti-PD-1 or combined anti-PD-1 plus anti-CTLA-4 antibodies [51]. Our observation that increased MTV is directly associated with serum LDH and S-100 in patients being accompanied with worse prognosis might therefore be a help for clinicians to early identify patients with an increased risk of relapse and which deserve particularly close monitoring. Perhaps, these would also be the patients who particularly benefited from a neoadjuvant therapy approach. No differences in OS could be observed between patients with elevated or normal acute-phase proteins (AP and CRP). An explanation is the low specificity of CRP and AP which may be elevated due to other reasons, for instance, inflammatory disorders [52, 53].

In our study, a cut-off value of 198 U/l for serum LDH could be defined which best differentiates between patients with or without 18F-FDG avid metastases (sensitivity 0.80; specificity 0.72). If the serum LDH rises above this cut-off value, vital tumor burden can be reasonably assumed. This finding is of great diagnostic relevance as an increasing serum LDH above this cut-off value may influence patient prognosis and it is below the generally accepted cut-off of 250 U/l. The early decision to perform a PET/CT in a clinical diagnostic setting should be considered.

Our study has limitations. Due to the retrospective design, a selection bias cannot be excluded. In addition, serologic parameters and survival data were not available for all patients. Volumetric parameters such as the MTV require an accurate lesion segmentation using a standardized segmentation method which has still not been established across clinical institutions. As all patients of our study cohort were examined at our institution, MTV was measured using the same segmentation method which includes a fixed relative threshold for all lesions. Further, we used a 50% threshold for the isocontour VOIs instead of the EANM recommended 41% threshold, which may underestimate volumetric PET parameters [54]. The rationale for this choice was that our mCT system uses PSF modeling, producing higher values of SUV compared to standard OSEM. Our data are the first to demonstrate that there is an association between the absolute values of PET parameters and established serologic tumor markers in melanoma patients. Further prospective studies with more patients and consideration of neoadjuvant therapy approaches should be conducted.

Conclusions

Tumor volumetric parameters in 18F-FDG-PET/CT serve as prognostic imaging biomarkers in patients with advanced melanoma which are associated with established serologic tumor markers and inflammatory markers.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available due to sensitive information but are available in anonymous form from the corresponding author on reasonable request.

Abbreviations

AP:

Alkaline phosphatase

AUC:

Area under the curve

AJCC:

American Joint Committee on Cancer

CTLA-4:

T-lymphocyte-associated protein 4

CRP:

C-reactive protein

EANM:

European Association of Nuclear Medicine

18F-FDG:

[18F]fluoro-2-deoxy-D-glucose

LDH:

Serum lactate dehydrogenase

MBq:

Megabecquerel

MTV:

Metabolic tumor volume

OS:

Overall survival

PET/CT:

Positron emission tomography/computed tomography

PD-1:

Programmed cell death protein 1

PSF:

point spread function

RAGE:

Receptor for advanced glycation endproducts

ROC:

Receiver operating characteristic

SUV:

Standardized uptake value

VOI:

Volume of Interest

References

  1. Duncan LM. The classification of cutaneous melanoma. Hematol Oncol Clin North Am. 2009;23:501–13. https://doi.org/10.1016/j.hoc.2009.03.013.

    Article  PubMed  Google Scholar 

  2. Ribas A, Hamid O, Daud A, Hodi FS, Wolchok JD, Kefford R, et al. Association of Pembrolizumab With Tumor Response and Survival Among Patients With Advanced Melanoma. JAMA. 2016;315:1600–9. https://doi.org/10.1001/jama.2016.4059.

    Article  CAS  PubMed  Google Scholar 

  3. Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L, et al. Nivolumab in Previously Untreated Melanoma without BRAF Mutation. N Engl J Med. 2014;372:320–30. https://doi.org/10.1056/NEJMoa1412082.

  4. Hodi FS, Chiarion-Sileni V, Gonzalez R, Grob J-J, Rutkowski P, Cowey CL, et al. Nivolumab plus ipilimumab or nivolumab alone versus ipilimumab alone in advanced melanoma (CheckMate 067): 4-year outcomes of a multicentre, randomised, phase 3 trial. Lancet Oncol. 2018;19:1480–92. https://doi.org/10.1016/S1470-2045(18)30700-9.

    Article  CAS  PubMed  Google Scholar 

  5. Wolchok JD, Chiarion-Sileni V, Gonzalez R, Rutkowski P, Grob J-J, Cowey CL, et al. Overall survival with combined Nivolumab and Ipilimumab in advanced melanoma. N Engl J Med. 2017;377:1345–56. https://doi.org/10.1056/NEJMoa1709684.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Balch CM, Gershenwald JE, Soong SJ, Thompson JF, Atkins MB, Byrd DR, et al. Final version of 2009 AJCC melanoma staging and classification. J Clin Oncol. 2009;27:6199–206. https://doi.org/10.1200/jco.2009.23.4799.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Nosrati A, Tsai KK, Goldinger SM, Tumeh P, Grimes B, Loo K, et al. Evaluation of clinicopathological factors in PD-1 response: derivation and validation of a prediction scale for response to PD-1 monotherapy. Br J Cancer. 2017;116:1141–7. https://doi.org/10.1038/bjc.2017.70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. de Heer EC, Brouwers AH, Boellaard R, Sluiter WJ, Diercks GFH, Hospers GAP, et al. Mapping heterogeneity in glucose uptake in metastatic melanoma using quantitative (18) F-FDG PET/CT analysis. EJNMMI Res. 2018;8:101. https://doi.org/10.1186/s13550-018-0453-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Weide B, Elsässer M, Büttner P, Pflugfelder A, Leiter U, Eigentler TK, et al. Serum markers lactate dehydrogenase and S100B predict independently disease outcome in melanoma patients with distant metastasis. Br J Cancer. 2012;107:422. https://doi.org/10.1038/bjc.2012.306 https://www.nature.com/articles/bjc2012306#supplementary-information.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. von Schoultz E, Hansson LO, Djureen E, Hansson J, Kärnell R, Nilsson B, et al. Prognostic value of serum analyses of S-100β protein in malignant melanoma. Melanoma Res. 1996;6:133–8. https://doi.org/10.1097/00008390-199604000-00008.

  11. Mårtenson ED, Hansson LO, Nilsson B, Ev S, Brahme EM, Ringborg U, et al. Serum S-100B Protein as a Prognostic Marker in Malignant Cutaneous Melanoma.. J Clin Oncol. 2001;19:824–31. https://doi.org/10.1200/jco.2001.19.3.824.

  12. Mocellin S, Zavagno G, Nitti D. The prognostic value of serum S100B in patients with cutaneous melanoma: A meta-analysis. Int J Cancer 2008;123:2370–6. https://doi.org/10.1002/ijc.23794.

  13. Egberts F, Kotthoff EM, Gerdes S, Egberts JH, Weichenthal M, Hauschild A. Comparative study of YKL-40, S-100B and LDH as monitoring tools for stage IV melanoma. Eur J Cancer. 2012;48:695–702. https://doi.org/10.1016/j.ejca.2011.08.007.

    Article  CAS  PubMed  Google Scholar 

  14. Fang S, Wang Y, Sui D, Liu H, Ross MI, Gershenwald JE, et al. C-Reactive Protein As a Marker of Melanoma Progression. J Clin Oncol 2015;33:1389–96. https://doi.org/10.1200/jco.2014.58.0209.

  15. Deichmann M, Kahle B, Moser K, Wacker J, Wüst K. Diagnosing melanoma patients entering American joint committee on Cancer stage IV, C-reactive protein in serum is superior to lactate dehydrogenase. Br J Cancer. 2004;91:699–702. https://doi.org/10.1038/sj.bjc.6602043.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wang Y, Zhang H, Yang Y, Zhang T, Ma X. Prognostic Value of Peripheral Inflammatory Markers in Preoperative Mucosal Melanoma: A Multicenter Retrospective Study. Front Oncol. 2019;9:995. https://doi.org/10.3389/fonc.2019.00995.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Reinhardt MJ, Joe AY, Jaeger U, Huber A, Matthies A, Bucerius J, et al. Diagnostic Performance of Whole Body Dual Modality 18F-FDG PET/CT Imaging for N- and M-Staging of Malignant Melanoma: Experience With 250 Consecutive Patients. J Clin Oncol 2006;24:1178–87. https://doi.org/10.1200/jco.2005.03.5634.

  18. Larson SM, Erdi Y, Akhurst T, Mazumdar M, Macapinlac HA, Finn RD, et al. Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging. The visual response score and the change in Total lesion glycolysis. Clin Positron Imaging. 1999;2:159–71.

    Article  PubMed  Google Scholar 

  19. Biehl KJ, Kong FM, Dehdashti F, Jin JY, Mutic S, El Naqa I, et al. 18F-FDG PET definition of gross tumor volume for radiotherapy of non-small cell lung cancer: is a single standardized uptake value threshold approach appropriate? J Nucl Med. 2006;47:1808–12.

    PubMed  Google Scholar 

  20. Lee JW, Kang CM, Choi HJ, Lee WJ, Song SY, Lee JH, et al. Prognostic value of metabolic tumor volume and Total lesion glycolysis on preoperative (1)(8) F-FDG PET/CT in patients with pancreatic Cancer. J Nucl Med. 2014;55:898–904. https://doi.org/10.2967/jnumed.113.131847.

    Article  CAS  PubMed  Google Scholar 

  21. Armstrong IS, Kelly MD, Williams HA, Matthews JC. Impact of point spread function modelling and time of flight on FDG uptake measurements in lung lesions using alternative filtering strategies. EJNMMI Physics. 2014;1:99. https://doi.org/10.1186/s40658-014-0099-3.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Vanderhoek M, Perlman SB, Jeraj R. Impact of the definition of peak standardized uptake value on quantification of treatment response. J Nuclear Med. 2012;53:4–11. https://doi.org/10.2967/jnumed.111.093443.

    Article  CAS  Google Scholar 

  23. Lodge MA, Chaudhry MA, Wahl RL. Noise considerations for PET quantification using maximum and peak standardized uptake value. J Nucl Med. 2012;53:1041–7. https://doi.org/10.2967/jnumed.111.101733.

    Article  CAS  PubMed  Google Scholar 

  24. Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nuclear Med. 2009;50(Suppl 1):122S–50S. https://doi.org/10.2967/jnumed.108.057307.

    Article  CAS  Google Scholar 

  25. Sher A, Lacoeuille F, Fosse P, Vervueren L, Cahouet-Vannier A, Dabli D, et al. For avid glucose tumors, the SUV peak is the most reliable parameter for [(18) F]FDG-PET/CT quantification, regardless of acquisition time. EJNMMI Res. 2016;6:21. https://doi.org/10.1186/s13550-016-0177-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ito K, Schöder H, Teng R, Humm JL, Ni A, Wolchok JD, et al. Prognostic value of baseline metabolic tumor volume measured on 18F-fluorodeoxyglucose positron emission tomography/computed tomography in melanoma patients treated with ipilimumab therapy. Eur J Nucl Med Mol Imaging. 2019;46:930–9. https://doi.org/10.1007/s00259-018-4211-0.

    Article  CAS  PubMed  Google Scholar 

  27. Son SH, Kang SM, Jeong SY, Lee S-W, Lee S-J, Lee J, et al. Prognostic Value of Volumetric Parameters Measured by Pretreatment 18F FDG PET/CT in Patients With Cutaneous Malignant Melanoma. Clin Nucl Med 2016;41:e266–e73. https://doi.org/10.1097/rlu.0000000000001205.

  28. Ribas A, Puzanov I, Dummer R, Schadendorf D, Hamid O, Robert C, et al. Pembrolizumab versus investigator-choice chemotherapy for ipilimumab-refractory melanoma (KEYNOTE-002): a randomised, controlled, phase 2 trial. Lancet Oncol. 2015;16:908–18. https://doi.org/10.1016/S1470-2045(15)00083-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Pfannenberg C, Gueckel B, Wang L, Gatidis S, Olthof SC, Vach W, et al. Practice-based evidence for the clinical benefit of PET/CT-results of the first oncologic PET/CT registry in Germany. Eur J Nucl Med Mol Imaging. 2019;46:54–64. https://doi.org/10.1007/s00259-018-4156-3.

    Article  PubMed  Google Scholar 

  30. Forschner A, Olthof SC, Guckel B, Martus P, Vach W, la Fougere C, et al. Impact of (18) F-FDG-PET/CT on surgical management in patients with advanced melanoma: an outcome based analysis. Eur J Nucl Med Mol Imaging. 2017;44:1312–8. https://doi.org/10.1007/s00259-017-3674-8.

    Article  PubMed  Google Scholar 

  31. Pflugfelder A, Kochs C, Blum A, Capellaro M, Czeschik C, Dettenborn T, et al. Malignant melanoma S3-guideline "diagnosis, therapy and follow-up of melanoma". J Dtsch Dermatol Ges. 2013;11 Suppl 6:1–116, 1–26. https://doi.org/10.1111/ddg.12113_suppl.

    Article  PubMed  Google Scholar 

  32. Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, et al. The eighth edition AJCC Cancer staging manual: continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging. CA Cancer J Clin. 2017;67:93–9. https://doi.org/10.3322/caac.21388.

    Article  PubMed  Google Scholar 

  33. San-Millan I, Brooks GA. Reexamining cancer metabolism: lactate production for carcinogenesis could be the purpose and explanation of the Warburg effect. Carcinogenesis. 2017;38:119–33. https://doi.org/10.1093/carcin/bgw127.

    Article  CAS  PubMed  Google Scholar 

  34. Hofmann MA, Schicke B, Fritsch A, Biesold S, Gussmann F, Kuchler I, et al. Impact of lymph node metastases on serum level of melanoma inhibitory activity in stage III melanoma patients. J Dermatol. 2011;38:880–6. https://doi.org/10.1111/j.1346-8138.2011.01219.x.

    Article  CAS  PubMed  Google Scholar 

  35. Kluger HM, Hoyt K, Bacchiocchi A, Mayer T, Kirsch J, Kluger Y, et al. Plasma markers for identifying patients with metastatic melanoma. Clin Cancer Res. 2011;17:2417–25. https://doi.org/10.1158/1078-0432.Ccr-10-2402.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Colombo MP, Maccalli C, Mattei S, Melani C, Radrizzani M, Parmiani G. Expression of cytokine genes, including IL-6, in human malignant melanoma cell lines. Melanoma Res. 1992;2:181–9. https://doi.org/10.1097/00008390-199209000-00006.

    Article  CAS  PubMed  Google Scholar 

  37. Kishimoto T. The biology of interleukin-6. Blood. 1989;74:1–10.

    Article  CAS  PubMed  Google Scholar 

  38. Wolf S, Haase-Kohn C, Lenk J, Hoppmann S, Bergmann R, Steinbach J, et al. Expression, purification and fluorine-18 radiolabeling of recombinant S100A4: a potential probe for molecular imaging of receptor for advanced glycation endproducts in vivo? Amino Acids. 2011;41:809–20. https://doi.org/10.1007/s00726-010-0822-x.

    Article  CAS  PubMed  Google Scholar 

  39. Herwig N, Belter B, Wolf S, Haase-Kohn C, Pietzsch J. Interaction of extracellular S100A4 with RAGE prompts prometastatic activation of A375 melanoma cells. J Cell Mol Med. 2016;20:825–35. https://doi.org/10.1111/jcmm.12808.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Peric B, Zagar I, Novakovic S, Zgajnar J, Hocevar M. Role of serum S100B and PET-CT in follow-up of patients with cutaneous melanoma. BMC Cancer. 2011;11:328. https://doi.org/10.1186/1471-2407-11-328.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Harpio R, Einarsson R. S100 proteins as cancer biomarkers with focus on S100B in malignant melanoma. Clin Biochem. 2004;37:512–8. https://doi.org/10.1016/j.clinbiochem.2004.05.012.

    Article  CAS  PubMed  Google Scholar 

  42. Molina R, Navarro J, Filella X, Castel T, Ballesta AM. S-100 protein serum levels in patients with benign and malignant diseases: false-positive results related to liver and renal function. Tumour Biol. 2002;23:39–44. https://doi.org/10.1159/000048687.

    Article  CAS  PubMed  Google Scholar 

  43. Tsoporis JN, Mohammadzadeh F, Parker TG. S100B: a multifunctional role in cardiovascular pathophysiology. Amino Acids. 2011;41:843–7. https://doi.org/10.1007/s00726-010-0527-1.

    Article  CAS  PubMed  Google Scholar 

  44. Warner AB, Postow MA. Combination controversies: checkpoint inhibition alone or in combination for the treatment of melanoma? Oncology (Williston Park). 2018;32:228–34.

    Google Scholar 

  45. Salavati A, Duan F, Snyder BS, Wei B, Houshmand S, Khiewvan B, et al. Optimal FDG PET/CT volumetric parameters for risk stratification in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial. Eur J Nucl Med Mol Imaging. 2017;44:1969–83. https://doi.org/10.1007/s00259-017-3753-x.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Schöder H, Moskowitz C. Metabolic tumor volume in lymphoma: hype or Hope? J Clin Oncol. 2016;34:3591–4. https://doi.org/10.1200/jco.2016.69.3747.

    Article  PubMed  Google Scholar 

  47. Marinelli B, Espinet-Col C, Ulaner GA, McArthur HL, Gonen M, Jochelson M, et al. Prognostic value of FDG PET/CT-based metabolic tumor volumes in metastatic triple negative breast cancer patients. Am J Nucl Med Mol Imaging. 2016;6:120–7.

    PubMed  PubMed Central  Google Scholar 

  48. Pak K, Cheon GJ, Nam HY, Kim SJ, Kang KW, Chung JK, et al. Prognostic value of metabolic tumor volume and total lesion glycolysis in head and neck cancer: a systematic review and meta-analysis. J Nucl Med. 2014;55:884–90. https://doi.org/10.2967/jnumed.113.133801.

    Article  CAS  PubMed  Google Scholar 

  49. Kruijff S, Bastiaannet E, Speijers MJ, Kobold ACM, Brouwers AH, Hoekstra HJ. The value of pre operative S-100B and SUV in clinically stage III melanoma patients undergoing therapeutic lymph node dissection. Eur J Surg Oncol. 2011;37:225–32. https://doi.org/10.1016/j.ejso.2010.12.013.

    Article  CAS  PubMed  Google Scholar 

  50. Bastiaannet E, Hoekstra OS, de Jong JR, Brouwers AH, Suurmeijer AJH, Hoekstra HJ. Prognostic value of the standardized uptake value for 18F-fluorodeoxyglucose in patients with stage IIIB melanoma. Eur J Nucl Med Mol Imaging. 2012;39:1592–8. https://doi.org/10.1007/s00259-012-2182-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wagner NB, Forschner A, Leiter U, Garbe C, Eigentler TK. S100B and LDH as early prognostic markers for response and overall survival in melanoma patients treated with anti-PD-1 or combined anti-PD-1 plus anti-CTLA-4 antibodies. Br J Cancer. 2018;119:339–46. https://doi.org/10.1038/s41416-018-0167-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Bilski J, Mazur-Bialy A, Wojcik D, Zahradnik-Bilska J, Brzozowski B, Magierowski M, et al. The role of intestinal alkaline phosphatase in inflammatory disorders of gastrointestinal tract. Mediat Inflamm. 2017;2017:9. https://doi.org/10.1155/2017/9074601.

    Article  CAS  Google Scholar 

  53. Gabay C, Kushner I. Acute-phase proteins and other systemic responses to inflammation. N Engl J Med. 1999;340:448–54. https://doi.org/10.1056/nejm199902113400607.

    Article  CAS  PubMed  Google Scholar 

  54. Boellaard R, Delgado-Bolton R, Oyen WJG, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54. https://doi.org/10.1007/s00259-014-2961-x.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

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Funding

This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2180 - 390900677. The study was supported by the Junior Clinician Scientist Program of the Medical Faculty, University of Tübingen.

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Authors and Affiliations

  1. Department of Radiology, Diagnostic and Interventional Radiology, University Hospital TĂĽbingen, Hoppe-Seyler-Str.3, 72076, TĂĽbingen, Germany

    Christian Philipp Reinert, Sergios Gatidis, Julia Sekler, Christina Pfannenberg & Konstantin Nikolaou

  2. Department of Radiology, Nuclear Medicine and Clinical Molecular Imaging, University Hospital, TĂĽbingen, Hoppe-Seyler-Str.3, 72076, TĂĽbingen, Germany

    Helmut Dittmann & Christian la Fougère

  3. Cluster of Excellence iFIT (EXC 2180) “Image Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany

    Christian la Fougère & Konstantin Nikolaou

  4. German Cancer Consortium (DKTK). Partner Site TĂĽbingen, TĂĽbingen, Germany

    Christian la Fougère & Konstantin Nikolaou

  5. Department of Dermatology, University Hospital TĂĽbingen, Liebermeisterstrasse 25, 72076, TĂĽbingen, Germany

    Andrea Forschner

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  1. Christian Philipp Reinert
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Contributions

CR and AF conceived of the presented idea. CR and JS performed the patient data research. CR performed the segmentations and measurements. SG, HD, CP and AF verified the analytical methods. CR performed the analysis, drafted the manuscript and designed the figures. AF, SG and CP supervised the findings of this work. Both KN and CF contributed to the conceptual idea and final version of the manuscript. All authors provided critical feedback and helped shape the research, analysis and manuscript. The authors read and approved the final manuscript.

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Correspondence to Christian Philipp Reinert.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.

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Competing interests

Konstantin Nikolaou received institutional research funds and speaker’s honorarium from Siemens Healthineers and is a scientific advisor of Siemens Healthcare Germany. The other authors have declared that no competing interests exist.

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Reinert, C.P., Gatidis, S., Sekler, J. et al. Clinical and prognostic value of tumor volumetric parameters in melanoma patients undergoing 18F-FDG-PET/CT: a comparison with serologic markers of tumor burden and inflammation. Cancer Imaging 20, 44 (2020). https://doi.org/10.1186/s40644-020-00322-1

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Keywords

Cancer Imaging

ISSN: 1470-7330

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