Metabolic Parameters on Baseline and Early [18F]FDG PET/CT as a Predictive Biomarker for Resistance to BRAF/MEK Inhibition in Advanced Cutaneous BRAFV600-mutated Melanoma

Metabolic Parameters on Baseline and Early [18F]FDG PET/CT as a Predictive Biomarker for Resistance to BRAF/MEK Inhibition in Advanced Cutaneous BRAFV600-mutated Melanoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metabolic Parameters on Baseline and Early [18F]FDG PET/CT as a Predictive Biomarker for Resistance to BRAF/MEK Inhibition in Advanced Cutaneous BRAFV600-mutated Melanoma Bernies van der Hiel, Berlinda J. de Wit - van der Veen, Alfons J.M. van den Eertwegh, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5941915/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 May, 2025 Read the published version in EJNMMI Research → Version 1 posted 5 You are reading this latest preprint version Abstract Background: [ 18 F]FDG PET/CT plays a crucial role in evaluating cancer patients and assessing treatment response, including in BRAF-mutated melanoma. Metabolic tumor volume (MTV) and total lesion glycolysis (TLG) have emerged as promising alternatives to standardized uptake value (SUV)-based measures for tumor assessment. This study evaluates the predictive value of SUVpeak, MTV, and TLG in predicting progression-free survival (PFS) in advanced BRAF-mutated melanoma treated with BRAF/MEK inhibitors. Results: Seventy-five patients with metastatic melanoma were enrolled in a multi-center trial and treated with vemurafenib/cobimetinib. [ 18 F]FDG-PET/CT scans were performed at baseline, week-2, and week-7. Imaging analysis included SUVpeak, MTV, and TLG of summed metastases, as well as percentage changes over time (∆). Baseline median PET-parameters were SUVpeak 12.59 (range 3.13-50.59), MTV 159mL (range 0-1897 mL), and TLG 1013 (range 1-13162). MTV had the highest predictive performance for risk of progression (AUC T=6 months =0.714). Patients with TLG below the median had significantly prolonged PFS (15.4 vs. 8.5 months, P=0.024). MTV above optimal cutoff (45.3 mL) was associated with an increased risk of progression/death, even after adjusting for LDH, ECOG status, and metastatic sites (HR=2.97, 95% CI 1.17-7.52, P=0.022). At week-7, ∆SUVpeak% was predictive (median ∆SUVpeak%: 64); PFS was 5.0 months (95% CI: 4.3-NA) for patients below the median versus 14.7 months (95% CI: 9.2-20.2) for those above or with non-quantifiable scans (P=0.0002). Median ∆MTV was 95.5% at week-2 and 97.6% at week-7, with significant PFS differences at both time points (week-2: P=0.020, week-7: P<0.001). TLG mirrored MTV. Patients with MTV increases at week-7 after an initial response at week-2 had a median PFS of 5.3 vs. 12.6 months for those with stable or declining MTV (P=0.0023). Conclusion: This study supports the use of MTV and TLG as robust predictive markers for PFS in advanced melanoma treated with BRAF/MEK-inhibitors. Monitoring early PET parameters changes can provide valuable insights into therapeutic response and disease progression. Trial Registration Clinicaltrials.gov identifier: NCT02414750. Registered 10 April 2015, retrospectively registered. Melanoma BRAF mutation progression-free survival Positron Emission Tomography metabolic tumor volume total lesion glycolysis standardized uptake value targeted therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 BACKGROUND Melanoma is a major health concern globally due to its aggressive nature and high mortality rate [ 1 ]. Fortunately, with the introduction of immune checkpoint inhibitors (ICI) and targeted therapy, the treatment landscape for melanoma patients has revolutionized dramatically over the past decade [ 2 ]. Targeted therapy with combined BRAF/MEK inhibitors (BRAF/MEKi) have emerged as effective treatment options for patients harboring a V600E/K mutation in the BRAF gene. This mutation, found in 40–60% of melanomas, activates the Mitogen-activated protein kinase (MAPK) signaling pathway, driving uncontrolled cell growth and survival [ 3 – 5 ]. Clinical trials have shown that BRAF/MEKi offers substantial clinical benefit for patients with V600E/K BRAF-mutated melanoma, reinforcing its role in treatment of melanoma [ 6 – 8 ]. Nevertheless, though most patients initially respond to BRAF/MEKi treatment, resistance often develops over time. Prediction or early detection of this acquired resistance would provide valuable insights for clinicians to make informed decisions regarding treatment modifications – such as switching to immune checkpoint inhibitors – to optimize patient outcomes. 18F-Fluorodeoxyglucose Positron Emission Tomography / Computed Tomography ([ 18 F]FDG PET/CT) plays a crucial role in the evaluation of cancer patients and has gained considerable attention as a valuable tool for assessing treatment response in various malignancies, including BRAF-mutated melanoma [ 9 – 11 ]. The Standardized Uptake Value (SUV) is the primary quantitative measure to assess relative uptake of [ 18 F]FDG in tumors, reflecting their metabolic activity. However, SUV measurements can be influenced by various factors, including patient body weight, blood glucose levels, and the timing between FDG injection and image acquisition [ 12 ]. The most common implementations involve SUVmax and SUVpeak, where only the most active voxels of tumor masses are evaluated. These variations and limitations in SUV measurements can undermine their reliability in accurately evaluating treatment response and predicting patient outcomes. To overcome the limitations of SUV-based measures, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) analysis have emerged as promising alternatives for a more comprehensive tumor assessment of several cancers [ 13 – 15 ]. MTV encompasses the total volume of metabolically active tumor tissue, typically defined by a SUV-threshold. By considering the entire tumor burden rather than focusing on specific tumor areas, MTV provides a holistic perspective on the malignancy’s extent and biological behavior. In addition, MTV is less influenced by patient-related and technical factors, making it a more robust parameter for quantitative analysis in clinical practice. Emerging evidence suggests that MTV may serve as a predictive marker in various malignancies, including patients treated with BRAF/MEKi [ 16 , 17 ]. This predictive capability could enhance risk stratification, treatment planning, and monitoring therapeutic responses. While MTV only represents the volume of metabolically active tumor tissue, TLG integrates the metabolic tumor volume with its actual metabolic activity (as represented by SUV). It is calculated by multiplying the MTV by the mean SUV within the tumor volume, offering a metric that considers both the volume and the metabolic intensity of the tumors. However, the value of TLG in addition to MTV for predicting treatment response in metastatic melanoma is unknown. The aim of this study is to assess the predictive value of different metabolic PET parameters on [ 18 F]FDG-PET/CT and determine whether these parameters can serve as indicators for progression-free survival in patients with advanced melanoma undergoing BRAF/MEK-targeted therapy. METHODS Patients Seventy-five patients diagnosed with histologically proven advanced or metastatic BRAF-mutated melanoma were enrolled in the REPOSIT-trial (NCT02414750) from March 2015 to February 2019. The study design has been published previously [ 18 ]. Briefly, this phase II, multi-center, single arm prospective study included BRAF-mutated unresectable stage IIIC or stage IV American Joint Committee on Cancer Classification (AJCC) 7th edition [ 19 ] melanoma patients with measurable lesions according to Response Evaluation Criteria In Solid Tumours version 1.1 (RECIST1.1) [ 20 ]. Patients were treated with combined BRAF/MEK inhibitor vemurafenib plus cobimetinib until progression or uncontrollable toxicity. Patients were recruited from nine hospitals, which are part of the Dutch Melanoma and Skin Cancer group (DMSCG). The study was approved by the local Medical Ethical Committees. Written informed consent was obtained before inclusion. The study closed enrollment before reaching the anticipated sample size of 90 patients as outlined in the study protocol, due to slow patient accrual. Ultimately, a total of 75 patients were included. Comprehensive data such as patient demographics, clinical, histopathological, imaging, and laboratory data were collected. Imaging protocol Patients underwent baseline [ 18 F]FDG PET/CT within one month prior to the initiation of therapy and follow-up [ 18 F]FDG PET/CT on day 15 of Cycle 1 (week-2) and day 21 of Cycle 2 (week-7). Contrast-enhanced CT scans were also performed in accordance with protocol, every eight weeks, and whenever progressive disease was suspected. PET/CT scans were performed in accordance with the European Association of Nuclear Medicine (EANM) guideline for oncology [18F]FDG PET/CT imaging [ 12 , 21 ]. Prior to PET/CT scan, patients fasted for at least 4 hours and had more than 400ml fluid intake. Serum glucose levels were below 11.0 mmol/L. [ 18 F]FDG was administered intravenously, with an activity dosage in agreement to the local institutional protocol, ensuring compliance with the EANM Research Ltd. (EARL) standard 1 [ 22 ]. Approximately 60 minutes (range 55–65 min) after administration, PET/CT images were acquired from at least base of the skull to thighs at 2–4 min per bed position in a supine position. A whole-body low-dose CT scan was also conducted for attenuation correction and anatomic localization. PET/CT scans were performed on a Gemini TF PET/CT, TF Big Bore PET/CT, Ingenuity TF PET/CT (all Philips Medical Systems, Best, the Netherlands), or Siemens Biograph mCT PET/CT (Siemens Healtineers, Erlangen, Germany). For consistency, all PET/CT scans for an individual patient were performed on the same scanner, with a maximum variation of 10% in activity compared to baseline. All participating PET imaging centers were accredited for EARL standard 1 and performed image reconstruction accordingly (EANM resEARch4Life, https://earl.eanm.org/ ) [ 22 ]. Image analysis and response evaluation The PET/CT scans were sent for central review, where objective evaluation was performed by an experienced nuclear medicine physician (BvdH). Areas of increased uptake were identified for further quantification. In cases of uncertainty, a second experienced nuclear medicine physician (MS) reviewed the data to reach a consensus. Quality control review of all PET/CT scans was conducted to assess their suitability for analysis [ 23 ]. Scans were included when SUVmean of the liver fell within the range of 1.3-3.0, measured by placing a spherical VOI with a 3cm diameter in the right upper lobe of the liver, avoiding malignancies and organ boundaries [ 24 ]. In case of extensive liver metastases, the SUVmean blood pool was required to fall within 0.79–2.32, measured by placing several VOIs in the thoracic aorta, ensuring the vessel wall was excluded [ 24 – 26 ]. Scans with SUVmean outside these normal ranges underwent further evaluation of protocol adherence to identify potential errors, and each scan was individually assessed for in- or exclusion. Since at the initiation of the REPOSIT trial there was limited evidence-based data on assessing therapy response and resistance using [ 18 F]FDG PET/CT in patients with unresectable stage IIIc or metastatic melanoma treated with BRAF/MEK inhibitors, the PET imaging analyses were intentionally not pre-specified. Instead, we conducted our analyses in alignment with the most current literature available at the time of the study. For quantification of the PET images, a validated in-house developed software package (ACCURATE) was used [ 27 ]. With the total tumor burden tool (TTB) in ACCURATE, PET-images were automatically delineated using the PET image-based segmentation method SUV40, resulting in a region of interest (ROI) of the summed lesions with a fixed SUV threshold of 4.0 and a volume of > 1mL, see Fig. 1 [ 28 ]. The resulting ROI delineation was inspected visually and manually corrected if necessary. SUVpeak, defined as a 1-mL spherical volume of interest with the highest uptake, MTV and TLG were calculated. On follow-up scans, patients with tumor responses resulting in no measurable metastases (i.e. a complete metabolic response, or with remaining metastatic lesions with a SUV below the threshold of 4.0 and a volume of less than 1mL) were categorized as ‘not quantifiable’. For the remaining quantifiable [ 18 F]FDG PET/CT scans, parameters were calculated as the percentage difference compared to baseline using the formula: ∆%=100*(baseline value – week 2 value)/baseline value. Similarly to baseline values, cutoffs were established, and percentage differences were categorized into two groups (above and below cutoff), with the ‘not quantifiable’ category kept separate. In a separate analysis, patients with a ‘not quantifiable’ response PET were combined with those showing a ∆% above median (indicating good responders). This combined group (labelled; GroupedSUVpeak, GroupedMTV or GroupedTLG) was compared to the patient group with a ∆% below the median (indicating lesser responders). Statistical analysis For summarizing patient characteristics, median and range for continuous variables, and frequency and percentage for categorical variables were displayed. PFS was defined as the time from commencement of BRAF/MEKi to disease progression (based on clinical findings and/or RECIST1.1) or death from any cause in the absence of progression, whichever occurred first. Patients without any of these events before the end of follow-up were censored at the date last known to be alive and progression/recurrence-free. Patients starting non-protocol treatment were censored at the date of start of this new treatment. Survival curves were generated using the Kaplan-Meier method, and compared using the log-rank test. Univariable and multivariable Cox regression analyses were performed, hazards ratio (HR) and the corresponding 95% confidence interval (CI) were reported. The likelihood of pairs of nested models was compared using a likelihood ratio test. Multivariable analyses were adjusted for baseline lactate dehydrogenase (LDH) level, Eastern Cooperative Oncology Group (ECOG) performance status and number of metastatic organs at baseline, since these are the most common prognostic biomarkers for progression-free survival (PFS) and overall survival (OS) in melanoma patients treated with BRAF/MEKi [ 7 , 29 , 30 ]. To evaluate the discriminating potential of MTV, TLG and SUV, time-dependent receiver operating characteristic (ROC) curve analyses were performed for survival data. The area under the curve at 6 months was computed. The survivalROC package in R was used for this purpose, using the nearest neighbor estimator method of the bivariate distribution of the censoring time and the failure time with moderate smoothing (span = 5%) [ 31 ]. Restricted cubic splines were used to evaluate the association between each PET parameter and PFS and the suitability of a threshold to dichotomize the PET parameter for further analysis. Since no clear threshold was observed, maximally selected rank statistics were used to determine an optimal cutoff achieving the maximum PFS benefit from each PET parameter. The method by Hothorn and Lausen [ 32 ] in the R package maxstat was used for approximating the p-value for the comparison between groups based on the optimal cutoff. Due to the explorative nature of this study and the limited sample size in relation to the number of tests performed, no adjustments for multiplicity were performed except for the adjustment of the P -value in the maximally selected rank statistics analysis. All P -values were 2-sided. Statistical analyses were performed using R statistical software (version 4.2.0; The R Foundation for Statistical Computing, Vienna, Austria) and SAS statistical software package (version 9.4; SAS Institute Inc. Cary, NC). RESULTS Patient characteristics Sixty-nine out of 75 patients were included for [ 18 F]FDG PET/CT-analysis, including 36 males and 33 females, with a median age of 63 y (range, 30–88 y). A flow chart of in- and exclusion is presented in Fig. 2 , detailed patient demographics are displayed in Table 1 . All but 3 (4.3%) patients were diagnosed with stage IV disease, 49 (71.0%) patients had metastases in at least three different tissue types. The median follow-up time among all patients regardless of censoring status was 15.0 months (IQR 9.1 to 24.9 months). In 36 patients (53.7%) treatment ended due to progression and in 17 patients (25.4%) due to adverse events. All 69 patients received baseline [ 18 F]FDG PET/CT. In 62 (89.9%) patients [ 18 F]FDG PET/CT was performed at week-2 and in 61 (88.4%) at week-7. In 58 (84.1%) patients [ 18 F]FDG PET/CT was performed at all three time points (baseline, week-2 and week-7). The median PFS of all included patients was 9.6 months (IQR 8 to 14.9 months). Table 1 Patient demographics N = 69 patients Characteristic Frequency (%) Sex Male 36 (52.2%) Female 33 (47.8%) Age in years (median (range)) 63 (30–88) ECOG performance status 0 39 (56.5%) 1 30 (43.5%) AJCC 7th edition Locally advanced (Stage IIIc) 3 (4.3%) Metastatic (Stage IV) 66 (95.7%) Number of metastasis Median (Q1-Q3) 12 (6–34) Min-max 1-128 Number of metastatic sites ULN 33 (49.3%) [ 18 F]FDG PET/CT scans Baseline 69 (100%) Day 15 Cycle 1 62 (89.9%) Day 21 Cycle 2 61 (88.4%) ECOG = Eastern Cooperative Oncology Group; AJCC = American Joint Committee on Cancer; LDH = Lactate dehydrogenase; ULN = Upper limit of normal. The predictive power of baseline [F]FDG PET/CT for progression At baseline, all patients presented with metastases that met the criteria of a SUV threshold > 4.0 and a volume > 1 mL, were included for automated delineation of ROIs to determine SUVpeak, MTV and TLG. Median SUVpeak was 12.59 (range 3.13–50.59); median MTV was 159mL (range 0-1897 mL) and median TLG was 1013 (range 1-13162). Time-dependent ROC curve analyses demonstrated that MTV had the best predictive performance for identifying patients at risk of progression at 6 months, as evidenced by its highest AUC T=6 months =0.714 among the evaluated metrics. For TLG, AUC T=6 months =0.685, while for SUVpeak AUC T=6months =0.598. Baseline [F]FDG PET parameters using median and optimal cutoff values Using median SUVpeak (12.6) as a cutoff, median PFS for patients with a SUVpeak below the median, was 14.7 months, versus 9.2 months for patients with a SUVpeak above the median (P = 0.064), see Fig. 3 A. For patients with MTV > 159.2mL (median cutoff), the median PFS was 8.0 months, compared to 14.9 months for those with MTV ≤ 159mL, P = 0.094 (Fig. 3 B). For TLG, the median PFS for patients with a TLG above the median of 1013.2 was significantly higher than for those with TLG below the median (8.5 vs. 15.4 months, P = 0.024), Fig. 3 C, but only in univariable analyses. Results from univariable and multivariable Cox regression analysis for these baseline PET parameters are displayed in Table 2 . When using maximally selected rank statistics, no significant PFS differences were found between groups for SUVpeak. With the estimated best cutoff of 10.9, the median PFS for patients with SUVpeak ≤ 10.9 was 16.8 months vs. 8.8 months SUVpeak > 10.9, P = 0.32, as shown in Fig. 3 D. For MTV, a significant difference in PFS was found with a cutoff of 45.3 mL: the median PFS for patients with MTV > 45.3mL was 8.5 months vs. 21.6 months for those with MTV ≤ 45.3mL, P = 0.021, as shown in Fig. 3 E. At this cutoff, the sensitivity and positive predictive value from the time-dependent ROC curve at 6 months were 1.00 and 0.81, respectively, whereas using the median cutoff of 159.2mL, sensitivity decreased to 0.64. Patients with MTV > 45.3 mL had a hazard of progression more than three times higher (HR = 3.53, 95% CI 1.50–8.36, P = 0.021), as detailed in Table 2 . The prolonged PFS remained significant in the multivariable analysis adjusted for baseline LDH level, ECOG performance status and number of metastatic sites at baseline (HR = 2.97, 95% CI 1.17–7.52, P = 0.022). Best cutoff for TLG was estimated to be 268, but since the patient distribution above and below this threshold was the same to the MTV cutoff (14 patients below and 55 patients above), the results for TLG were consistent with those for MTV, Fig. 3 F. Table 2 Univariable and multivariable Cox regression results for progression-free survival SUVpeak, MTV and TLG at baseline. Univariable Multivariable No. patients HR 95% CI P # HR 95% CI P ## SUVpeak using median ≤ 12.6 35 1.0 (ref.) 1.0 (ref.) > 12.6 34 1.67 0.96–2.91 0.064 1.58 0.88–2.85 0.13 SUVpeak using optimal cutoff ≤ 10.9 24 1.0 (ref.) 1.0 (ref.) > 10.9 45 2.20 1.19–4.05 0.32 1.89 0.97–3.66 0.0602 MTV using median ≤ 159.2 34 1.0 (ref.) 1.0 (ref.) > 159.2 35 1.60 0.92–2.77 0.094 1.21 0.61–2.40 0.58 MTV using optimal cutoff ≤ 45.3 14 1.0 (ref.) 1.0 (ref.) > 45.3 55 3.53 1.50–8.36 0.021* 2.97 1.17–7.52 0.022* TLG using median ≤ 1013.2 35 1.0 (ref.) 1.0 (ref.) > 1013.2 34 1.88 1.08–3.28 0.024* 1.42 0.72–2.80 0.31 TLG using optimal cutoff ≤ 268.2 14 1.0 (ref.) 1.0 (ref.) > 268.2 55 3.53 1.50–8.36 0.021* 2.97 1.17–7.52 0.022* All patients presented with metastases that met the criteria of an SUV threshold > 4.0 and a volume > 1 mL, which were included for the automated delineation of regions of interest (ROIs) to determine SUVpeak, MTV and TLG. # Log-rank test p-value. When maximally selected log-rank statistics are used to determine the optimal cutoff, the p-value is approximated using the method by Hothorn and Lausen. ## From multivariable Cox regression model adjusted for LDH, ECOG performance status and the number of metastatic organs at baseline. Of note: Due to the different tests used for obtaining p-values (log-rank with or without approximated p-value using the method by Hothorn and Lausen for the univariable analysis, Wald test for multivariable analysis) comparison between univariable and multivariable analyses should focus on HR estimates. * p-value < 0.05 (for median cutoff only). SUV = standard uptake value; MTV = metabolic tumor volume; TLG-total lesion glycolysis. Changes in early and late [ 18 F]FDG PET parameters on treatment During BRAF/MEKi treatment, [ 18 F]FDG PET/CT performed at week-2 revealed in 23 (37.1%) patients a SUV < 4.0 in all remaining metastases, preventing automated delineation of ROI. These scans were classified as ‘not quantifiable' and were considered good responders. At week-7, the number of not quantifiable scans increased to 32 (52.5%). Figure 1 provides an example of a patient with a not quantifiable scan at week-7. For the optimal cutoff percentage difference determined with maximally selected rank statistics the outcome was similar compared to the median percentage difference for both MTV and TLG. Therefore, for on treatment results, we focused on the median percentage difference (median ∆%) PET parameters. Kaplan-Meier curves for PFS with MTV and TLG grouped according to the optimal cutoff are summarized in Supplemental 1. Percentage change from baseline SUVpeak Kaplan-Meier curves for PFS, stratified by the percentage change in SUVpeak from baseline and a separate not quantifiable group, are presented in Fig. 4 . The median percentage difference of SUVpeak at week-2 (median ∆SUVpeak% week−2 ) was 61% (range: -5–100%), and at week-7 (median ∆SUVpeak% week−7 ) it was 64% (range − 53–100%). The not quantifiable group had the longest median PFS at both time-points (Fig. 4 A and 4 C). When combining the not quantifiable group with patients who had a ∆SUVpeak% above median, the PFS for this GroupedSUVpeak was significantly longer compared to ∆SUVpeak% below the median at week-7, but not at week-2 (P = 0.0002 versus P = 0.056, respectively), see Fig. 4 B and 4 D. These results were corroborated in multivariable analyses (see Table 3 ). Table 3 Univariable and multivariable Cox regression results for progression free survival SUVpeak, MTV and TLG on treatment. Univariable Multivariable No. patients HR 95% CI P # HR 95% CI P ## On treatment Baseline − 2 weeks SUVpeak % difference using median Not quantifiable 23 1.0 (ref.) 1.0 (ref.) > 60.6 19 2.08 1.01–4.28 0.013* 1.61 0.73–3.55 0.24 ≤ 60.6 20 2.51 1.21–5.18 0.048* 1.74 0.77–3.90 0.18 SUVpeak Grouped Not quantifiable + SUVpeak > 60.6 42 1.0 (ref.) 1.0 (ref.) ≤ 60.6 20 1.79 0.98–3.30 0.056 1.33 0.69–2.59 0.40 MTV % difference using median Not quantifiable 20 1.0 (ref.) 1.0 (ref.) > 95.6 21 1.65 0.79–3.43 0.18 0.82 0.36–1.84 0.63 ≤ 95.6 21 2.83 1.33–6.03 0.0070* 2.09 0.92–4.74 0.077 MTV Grouped Not quantifiable + MTV > 95.6 41 1.0 (ref.) 1.0 (ref.) ≤ 95.6 21 2.15 1.17–3.96 0.012* 2.36 1.21–4.60 0.012* TLG % difference using median Not quantifiable 20 1.0 (ref.) 1.0 (ref.) > 97.3 21 1.68 0.81–3.49 0.17 0.98 0.44–2.16 0.96 ≤ 97.3 21 2.75 1.29–5.86 0.0089* 2.02 0.87–4.71 0.104 TLG Grouped Not quantifiable + TLG > 97.3 41 1.0 (ref.) 1.0 (ref.) ≤ 97.3 21 2.07 1.12–3.82 0.017* 2.05 1.04–4.05 0.039* On treatment Baseline − 7 weeks SUVpeak % difference using median Not quantifiable 32 1.0 (ref.) 1.0 (ref.) > 64 14 1.43 0.69–2.98 0.33 1.77 0.83–3.77 0.14 ≤ 64 15 4.0 1.88–8.48 0.0003* 4.58 1.92–10.91 0.0006* SUVpeak Grouped Not quantifiable + SUVpeak > 64 46 1.0 (ref.) 1.0 (ref.) ≤ 64 15 3.56 1.76–7.19 0.0002* 3.75 1.66–8.46 0.0014* MTV % difference using median Not quantifiable 31 1.0 (ref.) 1.0 (ref.) > 97.6 15 1.36 0.66–2.80 0.3988 1.28 0.61–2.69 0.52 ≤ 97.6 15 4.70 2.19–10.07 0.0001* 3.68 1.46–9.29 0.0057* MTV Grouped Not quantifiable + MTV > 97.6 46 1.0 (ref.) 1.0 (ref.) ≤ 97.6 15 4.19 2.07–8.48 98.6 15 1.36 0.66–2.80 0.40 1.39 0.66–2.95 0.38 ≤ 98.6 15 4.70 2.19–10.07 0.0001* 4.41 1.68–11.62 0.0027* TLG Grouped Not quantifiable + TLG > 98.6 46 1.0 (ref.) 1.0 (ref.) ≤ 98.6 15 4.19 2.07–8.48 4.0 and a volume > 1 mL were classified as not quantifiable. SUV = standard uptake value; MTV = metabolic tumor volume; TLG-total lesion glycolysis. # Log-rank test p-value. ## From multivariable Cox regression model adjusted for baseline SUVpeak/MTV/TLG, LDH, ECOG performance status and the number of metastatic organs at baseline. * p-value < 0.05. Using the not quantifiable group as reference for good response, patients with ∆SUVpeak% below the median had a worse PFS at both time-points (∆SUVpeak% week−2 : HR = 2.51, 95% CI 1.21–5.18, P = 0.048; ∆SUVpeak% week−7 : HR = 4.00, 95% CI 1.88–8.48, P = 0.0003), though only the latter remained significant in multivariable analyses. This was also true for week-7 when not quantifiable was grouped with patients having a ∆SUVpeak% above the median (GroupedSUVpeak), at detailed in Table 3 . It should be noted that this model had a poorer fit for the data than when no grouping was done, according to likelihood ratio test results. Percentage change from baseline metabolic tumor volume At both week-2 (∆% week−2 ) and week-7 (∆% week−7 ), the groups specified by percentage change below and above median MTV were similar as those for TLG, resulting in the same outcomes for both PET-parameters. As a results, we focused on changes in MTV only. Figure 5 displays Kaplan-Meier curves for PFS, stratified by median percentage changes in MTV during treatment. See Supplemental 1 for Kaplan-Meier PFS by optimal cutoff for percentage change in MTV. Among the quantifiable scans, the median percentage change in MTV at week-2 (median ∆MTV% week−2 ) was 96% (range 22–100%), and at week-7 (median ∆MTV% week−7 ) this was 98% (range − 41–100%). Significant differences in PFS were seen between the 3 groups at both week-2 and week-7 (P = 0.020 week − 2 ; P < 0.0001 week − 7 ), see Fig. 5 A and 5 C. When combining not quantifiable with patients having a ∆MTV% above median, the PFS for this combined group (GroupedMTV) was significantly longer compared to those with ∆MTV% below median (median 13.9 vs. 6.9 months at week-2, and 14.7 vs. 4.5 months at week-7), see Fig. 5 B and 5 D. See Supplemental 2 for the results of TLG. With the not quantifiable group used as reference for good response, at both time points the hazard of a PFS event was significantly higher for patients with ∆MTV% below the median, but not for patients with ∆MTV% above the median; HR = 2.83 (95% CI 1.33–6.03, P = 0.0070) vs. HR = 1.65 (95% CI 0.79–3.43, P = 0.1804) at week-2, and HR = 4.7 (95% CI 2.19–10.07, P = 0.0001) vs. HR = 1.36 (95% CI 0.66–2.80, P = 0.3988) at week-7, see Table 3 . Only results for week-7 remained significant in multivariable analyses. The hazard of a PFS event remained significant when not quantifiable was grouped with median ∆MTV% above the median (Grouped MTV) in both univariable and multivariable analyses for week-2 and week-7, see Table 3 . A likelihood ratio test indicated that this model was a worse fit for the data than the ungrouped model though. On treatment increase of metabolic tumor volume At week-2 and week-7, all patients revealed a decrease in MTV compared to baseline. However, when PET/CT of week-7 was compared to week-2, an increase in MTV was measured in 9/58 (15.5%) patients, illustrated in Fig. 6 . Median PFS of these 9 patients was 5.3 months compared to 12.6 months of the other patients with stable or ongoing decrease of MTV (P = 0.0023), see Fig. 7 . A multivariable Cox regression analysis displayed a high HR but power was limited (HR = 2.34, 95% CI 0.96–5.74, P = 0.062). DISCUSSION [ 18 F]FDG PET/CT has emerged as a powerful imaging tool for evaluating treatment response and predicting outcomes in various cancers, including BRAF-mutated melanoma [ 9 – 11 ]. In this study, we present the results of baseline [ 18 F]FDG PET parameters, such as SUVpeak, MTV, and TLG, in predicting PFS in patients with advanced BRAF-mutated melanoma undergoing BRAF/MEKi therapy. Additionally, the potential of these parameters as early indicators of treatment resistance was assessed. Baseline SUVpeak, MTV and TLG At baseline, the ability of median SUVpeak (12.59) to predict PFS was modest, with an AUC of 0.598 at six months. While lower baseline SUVpeak values suggested a trend toward prolonged PFS (median 14.7 vs. 9.2 months), the difference was not statistically significant (P = 0.064). Similarly, a cutoff of 10.9 showed a potential survival benefit, but without statistical significance (P = 0.32). The limited predictive value of SUVpeak may be due to its focus on metabolic activity within a small tumor region, thus overlooking tumor heterogeneity and total disease burden. Additionally, SUVpeak is influenced by technical factors such as image acquisition and reconstruction methods, as well as biological factors like glucose metabolism [ 12 ]. Though moderate, our study findings indicate that MTV had a higher predictive value for PFS (AUC = 0.714 at six months) than SUVpeak. Patients with a baseline MTV below 45.3mL had significantly longer PFS (21.6 vs. 8.5 months, P = 0.021), while those with MTV above this threshold experienced a threefold increased risk of progression (HR = 3.53). The multivariable analysis also indicated prolonged PFS in patients with a baseline MTV below 45.3 mL. However, it is important to note that the corresponding p-value cannot be directly approximated for selecting the optimal cutoff due to the multivariable nature of the analysis, which limits its interpretability. Nonetheless, these findings demonstrate MTV's potential to better capture overall disease burden and predict long-term outcomes. The results for TLG closely mirrored those of MTV, reinforcing the reliability and robustness of volumetric PET parameters. In contrast to SUVpeak, MTV provides a more comprehensive view of the total volume of metabolically active tumor tissue, capturing volumetric changes across all metastatic sites rather than focusing on selected lesions. In literature, two studies investigated baseline MTV on [ 18 F]FDG PET as a predictor of survival following BRAF/MEK inhibition in patients with advanced BRAFV600-mutated melanoma. McArthur et al. prospectively evaluated 35 BRAFi/MEKi-naïve melanoma patients treated with vemurafenib and cobimetinib [ 16 ]. As in our study, they observed significant early and improving metabolic responses during therapy. [ 18 F]FDG PET scans, performed during the first two treatment cycles (day 10–15 and day 35–49), showed marked reductions in tumor burden and metabolism, with patients achieving substantial decreases in MTV and SUVmax. While baseline tumor burden did not correlate with metabolic response, baseline MTV was a predictor of overall survival (OS), with lower baseline values linked to longer survival. In a retrospective cohort of 57 metastatic melanoma patients treated with BRAF/MEK inhibitors, Annovazzi et al. revealed that a total metabolic tumor volume (TMTV), i.e. the sum of metastases with a SUVmax > 2 and with a volume > 0.5mL, of over 56mL at baseline [ 18 F]FDG PET/CT and the presence of more than two metastatic organ sites were significantly correlated with shorter PFS and OS, with TMTV being the only independent predictor in multivariate analysis [ 17 ]. Noteworthy, this cutoff is almost similar to the optimal cutoff of 45mL found in our study, where the minimal differences might be explained by the different threshold for automatic delineation in our study (SUVmax > 4 and volume of > 1mL). These findings underscore that baseline MTV is a valuable predictive indicator for survival in advanced melanoma patients treated with BRAF/MEKi. Early changes of metabolic parameters during BRAF/MEKi treatment At week-2, percentage changes in SUVpeak, when stratified above or below the median, were not predictive of PFS. Patients with not quantifiable lesions on PET (i.e. no metastases above the SUV threshold of 4) had the best PFS, with the hazard for an event significantly higher for patients with a percentage change in SUVpeak above or below the median. These results indicate that early response prediction for determining the best PFS is more accurately associated with an absolute SUV threshold rather than mean percentage differences. In contrast, percentage changes in SUVpeak became predictive at week-7, where a shorter PFS was seen for patients with percentage changes in SUVpeak below the median compared to patients with changes above the median or the not quantifiable patients. Therefore, in our study no incremental predictive benefit was observed using percentage changes of SUVpeak on early [ 18 F]FDG PET/CT at week-2. Only one study investigated the correlation between SUV on early [ 18 F]FDG PET/CT response and survival during BRAF/MEKi treatment of melanoma patients [ 11 ]. In this study by Schmitt et al., changes in SUVmax of the hottest lesion and of the least responsive tumor on follow-up [ 18 F]FDG PET/CT were calculated in 24 patients and correlated to PFS and OS. Mean time from baseline to follow-up [ 18 F]FDG PET/CT was 26 days, being approximately double the duration compared to our study. They observed a significant association between the smallest change in SUVmax and progression-free survival (P = 0.01), but not overall survival (P = 0.52). Though our results indicate that percentage change SUVpeak might be predictive at week-7, it might already be at an earlier time-point of four weeks, as was observed by Schmitt et al. When examining the impact of MTV and TLG during treatment, significant reductions in MTV and TLG at both week-2 and week-7 follow-up scans were associated with improved PFS. Patients who demonstrated a ≥ 96% reduction in MTV at week-2 and ≥ 98% reduction at week-7 experienced significantly longer PFS compared to those with lesser reductions. When adjusting for LDH, ECOG performance status and number of metastatic sites, the risk of progression for patients with MTV above this threshold remained significantly increased at both time-points compared to the Grouped MTV (HR = 2.36 week-2 vs HR = 0.0057). However, at both week-2 and week-7, best PFS was observed in the not quantifiable group, being classified based on a SUV threshold of 4. So, during treatment, patients with the best PFS are determined based on SUV rather than MTV, indicating that metabolic activity, as reflected by changes in SUV, may be a more sensitive and reliable predictor of treatment response and long-term outcomes than the overall tumor burden measured by MTV. However, these findings do emphasize the potential of MTV and TLG as early markers of treatment efficacy, with the possibility of identifying patients at risk of early progression even before clinical or radiographic evidence of disease worsening. Interestingly, patients whose MTV increased between week-2 and week-7 had a median PFS of only 5.3 months, compared to 12.6 months for patients with continued MTV reduction. This suggests that any increase in MTV during treatment may be an early indicator of resistance to BRAF/MEKi therapy. Such findings underscore the importance of serial [ 18 F]FDG PET/CT imaging in monitoring treatment response, as changes in MTV could provide critical insights into disease dynamics, allowing for timely modifications to treatment strategies. In this context, [ 18 F]FDG PET/CT might be a valuable tool for patients who are too frail to initiate treatment with first line ICI, but for whom a switch to immunotherapy is being considered later following BRAF/MEKi therapy. A key limitation of our study is the relatively small sample size (n = 69), which may limit the generalizability of our findings. Additionally, due to the application of the SUV threshold for MTV and TLG analysis during treatment to adequately distinguish tumor from physiologic uptake, the sample size of patients eligible for evaluation of MTV and TLG was further reduced. Nevertheless, to our knowledge, this is the only study to prospectively investigate early [ 18 F]FDG PET/CT during treatment of BRAF/MEK inhibition in advanced BRAF-naïve melanoma patients. Furthermore, the uniqueness of this cohort lies in the fact that patients were treated with BRAF/MEK inhibitors until disease progression, a treatment approach that is less feasible in current clinical practice due to changes in the therapeutic landscape. CONCLUSIONS In conclusion, this study highlights the predictive value of [ 18 F]FDG PET/CT in assessing BRAF/MEKi treatment response in advanced BRAF-mutated melanoma. Baseline metabolic tumor volume (MTV) was the best predictive indicator, with lower MTV linked to longer PFS, while SUVpeak had limited predictive power. During treatment, percentage changes in MTV and TLG all correlated with improved PFS already on early imaging and additional SUVpeak at week-7, with treatment response best predicted by an absolute SUV threshold of 4. Increase of MTV on serial [ 18 F]FDG PET/CT from week-2 to week-7 can identify early resistance. Abbreviations [ 18 F]FDG 18F-Fluorodeoxyglucose AUC area under the curve CI confidence interval CT computed tomography EANM European Association of Nuclear Medicine EARL EANM Research Ltd. ECOG Eastern Cooperative Oncology Group HR hazard ratio IQR interquartile range LDH lactate dehydrogenase MAPK mitogen-activated protein kinase MTV metabolic tumor volume NA not available PET positron emission tomography PFS progression-free survival RECIST response evaluation criteria in solid tumors ROC receiver-operating characteristic ROI region of interest SUV standardized uptake value TF time of flight TLG total lesion glycolysis TTB total tumor burden Declarations Ethics approval and consent to participate The Medical Ethical Committee of the Netherlands Cancer Institute approved the study for all participating centers. Informed consent was obtained from all participants before entering the study. Consent for publication The authors affirm that human research participants provided informed consent for publication of the manuscript. Availlability of data and material The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests AvdE: Study grant: Roche, Idera, Travel expenses: Ipsen Advisory Board: Bristol-Myers Squibb, MSD Oncology, Ipsen, Janssen Cilag BV, Pierre Fabre EK: consultancy/advisory relationships with Bristol Myers Squibb, Novartis, Merck, Pierre Fabre, Lilly and Bayer, all paid to institute, received research grants not related to this paper from Bristol Myers Squibb, Delcath, Novartis and Pierre Fabre. GH: consultancy/advisory relationships with Amgen, Bristol-Myers Squibb, Roche, MSD, Pfizer, Novartis, Sanofi, Pierre Fabre, all paid to institute, received research grants from Bristol-Myers Squibb, Seerave, all paid to institute. MA: advisory board / consultancy honoraria from Amgen, Bristol Myers Squibb, Novartis, MSD-Merck, Merck-Pfizer, Pierre Fabre, Sanofi, Astellas, Bayer. Research grants Merck-Pfizer, all paid to institute and not related to current work. FdV: received research grant from Foundation STOPBraintumors.org, BMS, Novartis, Servier, CureVac, EORTC, all paid to institute. AvdV: consultancy roles (all paid to the institute) for BMS, MSD, Roche, Sanofi, Novartis, Pierre Fabre, Merck, Ipsen, Eisai, Pfizer, all paid to the institute. JH: advisory roles for BMS, CureVac, GSK, Ipsen, Iovance Biotherapeutics, Imcyse, Merck Serono, Molecular Partners, MSD, Novartis, Pfizer, Roche, Sanofi, Third Rock Ventures, member of SAB of Achilles Tx, BioNTech, Gadeta, Immunocore, Instil Bio, PokeAcell, Scenic, T-Knife and Neogene Tx, all paid to institute except Neogene Tx and Scenic, received grant support from Amgen, Asher Bio, BioNTech, BMS, MSD, Novartis, and Sastra Cell Therapy, all paid to institute. The other authors declare no conflict of interest. Funding The REPOSIT-study is supported by an unrestricted grant by Roche Medical B.V. The company has approved the design of the study and provided cobimetinib free of charge. The company has no role in collection, analysis, and interpretation of data or in writing the manuscript. Authors’ contributions BvdH, LdW, AvdE, MS, RB and JH contributed to the conception and design of the study. Data collection, including patient-related activities was done by BvdH, AvdE, JH, EK, GH, MA, FdV, MB, AvdV and JWdG. Data analysis, statistics and interpretation of data was performed by BvdH, MS, ML, RB, LdW and WV. Acknowledgements: The authors want to thank Ms. I. Eggink, Dr. R.H.T. Koornstra, Drs. A. Arens, Drs. M.G.G. Hobbelink, Prof. dr. L.F. de Geus-Oei, Dr. W.H.J. Kruit, Prof. dr. J.F. Verzijlbergen, Prof. dr. F.M. Mottaghy, Dr. S. Knollema, Dr. A.H. Brouwers and Prof. dr. O.S. Hoekstra for their contributions. 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Computational Statistics & Data Analysis. 2003;43:121-37. doi:https://doi.org/10.1016/S0167-9473(02)00225-6. Supplementary Files Sup1.KaplanMeierontreatmentoptimalcutoffplain.docx Supplemental 1. Kaplan-Meier progression-free survival by optimal cutoff for percentage change in SUVpeak, MTV and TLG on treatment. Kaplan-Meier progression free survival curves by 2-weeks optimal cutoff % change SUVpeak (A), MTV (B) and TLG (C) and 7-weeks optimal cutoff % change of SUVpeak (D), MTV (E) and TLG (F). SUV=standard uptake value; MTV=metabolic tumor volume; TLG-total lesion glycolysis; NQ=not quantifiable. P-values for log-rank test are displayed. Sup2.KaplanMeierontreatmentTLGplain.docx Supplemental 2. Kaplan-Meier progression-free survival by TLG on treatment. Kaplan-Meier progression free survival curves by 2-weeks median % change TLG (A), 2-weeks grouped TLG (B), 7-weeks median % change TLG (C), and 7-weeks grouped TLG (D). TLG=total lesion glycosysis; NQ=Not quantifiable. 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Kapiteijn","email":"","orcid":"","institution":"Leiden University Medical Center: Leids Universitair Medisch Centrum","correspondingAuthor":false,"prefix":"","firstName":"Ellen","middleName":"W.","lastName":"Kapiteijn","suffix":""},{"id":415779782,"identity":"8b53cdff-8fe2-4bfc-b8f1-b23328918d61","order_by":8,"name":"Geke A.P. Hospers","email":"","orcid":"","institution":"University Medical Centre Groningen: Universitair Medisch Centrum Groningen","correspondingAuthor":false,"prefix":"","firstName":"Geke","middleName":"A.P.","lastName":"Hospers","suffix":""},{"id":415779783,"identity":"be716dfb-4bd8-4fa8-81c5-2a842324ca87","order_by":9,"name":"Maureen J.B. Aarts","email":"","orcid":"","institution":"Maastricht University Hospital: Maastricht Universitair Medisch Centrum+","correspondingAuthor":false,"prefix":"","firstName":"Maureen","middleName":"J.B.","lastName":"Aarts","suffix":""},{"id":415779784,"identity":"e531d0a6-e008-43ca-bbe9-8a1967626762","order_by":10,"name":"Filip Y.F.L. de Vos","email":"","orcid":"","institution":"University Medical Centre Utrecht: Universitair Medisch Centrum Utrecht","correspondingAuthor":false,"prefix":"","firstName":"Filip","middleName":"Y.F.L.","lastName":"de Vos","suffix":""},{"id":415779785,"identity":"71892f03-aeef-40a1-bf6d-e365420c1359","order_by":11,"name":"Marye J. Boers-Sonderen","email":"","orcid":"","institution":"University Medical Center Nijmegen: Radboudumc","correspondingAuthor":false,"prefix":"","firstName":"Marye","middleName":"J.","lastName":"Boers-Sonderen","suffix":""},{"id":415779786,"identity":"2a44ce2b-f359-4ba2-bd88-f01e5b11bf99","order_by":12,"name":"Astrid A.M. van der Veldt","email":"","orcid":"","institution":"Erasmus Medical Centre: Erasmus MC","correspondingAuthor":false,"prefix":"","firstName":"Astrid","middleName":"A.M. van der","lastName":"Veldt","suffix":""},{"id":415779787,"identity":"bd82c0fb-8f34-4824-ad58-4996b1ee7e51","order_by":13,"name":"Jan Willem B. de Groot","email":"","orcid":"","institution":"Isala Hospitals: Isala","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"Willem B.","lastName":"de Groot","suffix":""},{"id":415779788,"identity":"d738a38c-32f1-48ff-a1a0-ad0ac4fecc61","order_by":14,"name":"John B.A.G. Haanen","email":"","orcid":"","institution":"Netherlands Cancer Institute: Antoni van Leeuwenhoek","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"B.A.G.","lastName":"Haanen","suffix":""}],"badges":[],"createdAt":"2025-02-01 13:00:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5941915/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5941915/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13550-025-01259-x","type":"published","date":"2025-05-28T15:57:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76576330,"identity":"abf834c6-d94d-47e8-9b06-5a3dff146537","added_by":"auto","created_at":"2025-02-18 14:16:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":374617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnterior Maximum Intensity Projections with semi-automatic delineation of target lesions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaximum Intensity Projection [\u003csup\u003e18\u003c/sup\u003eF]FDG PET of a patient at baseline, week-2 and week-7. Delineation of melanoma metastases with a SUV\u0026gt;4 and volume \u0026gt;1mL are shown in red. At baseline, some metastases were below the threshold for delineation with Accurate (green arrows; left hilar and left femur). Blue arrows demonstrate metastases that were delineated at baseline, but were below the threshold at week-2, though still visible. At week-7, minimal residual tumor was present (orange arrow), but no metastases could be delineated and the scan was therefore classified as not quantifiable.\u003c/p\u003e","description":"","filename":"Fig1.MIPplain.png","url":"https://assets-eu.researchsquare.com/files/rs-5941915/v1/e55e2079d1ab16259149e95d.png"},{"id":76576336,"identity":"914296c5-7ccd-4353-a422-7fc4cc109116","added_by":"auto","created_at":"2025-02-18 14:16:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart included patients for PET analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFlow chart patient inclusion. BL = baseline; D15C1 = Day 15 of Cycle 1; D21C2 = Day 21 of Cycle 2.\u003c/p\u003e","description":"","filename":"Fig2.Flowchartplain.png","url":"https://assets-eu.researchsquare.com/files/rs-5941915/v1/b3313cf2ecd7768c8b24fa2b.png"},{"id":76576335,"identity":"15622765-67fa-4c0e-a774-d56066ea0687","added_by":"auto","created_at":"2025-02-18 14:16:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":904488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier progression-free survival by SUVpeak, MTV and TLG at baseline.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier progression free survival curves by baseline median of SUVpeak (A), MTV (B) and TLG (C) and optimal cutoff of SUVpeak (D), MTV (E) and TLG (F). SUV=standard uptake value; MTV=metabolic tumor volume; TLG=total lesion glycolysis.\u003c/p\u003e","description":"","filename":"Fig3.KaplanMeierbaselineplain.png","url":"https://assets-eu.researchsquare.com/files/rs-5941915/v1/9a31ef5e3b0b9a42ffbcd63c.png"},{"id":76576344,"identity":"65aae131-b5d4-4463-914a-673451e58981","added_by":"auto","created_at":"2025-02-18 14:16:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":841213,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier progression-free survival by on treatment median percentage difference SUVpeak from baseline.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier progression free survival curves by 2-weeks median % change SUVpeak (A), 2- weeks grouped SUVpeak (B), 7-weeks median % change SUVpeak (C), and 7-weeks grouped SUVpeak (D). SUV= standard uptake value; NQ=Not quantifiable.\u003c/p\u003e","description":"","filename":"Fig4.KaplanMeierontreatmentSUVpeakplain.png","url":"https://assets-eu.researchsquare.com/files/rs-5941915/v1/c3b6e2788d872c82273e066a.png"},{"id":76578060,"identity":"de60a77f-be61-4963-ad17-0e662fc6fa28","added_by":"auto","created_at":"2025-02-18 14:32:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":778875,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier progression-free survival by on treatment median percentage difference MTV from baseline.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier progression free survival curves by 2-weeks median % change MTV (A), 2-weeks grouped MTV (B), 7-weeks median % change MTV (C), and 7-weeks grouped MTV (D). MTV=metabolic tumor volume; NQ=Not quantifiable.\u003c/p\u003e","description":"","filename":"Fig5.KaplanMeierontreatmentMTVplain.png","url":"https://assets-eu.researchsquare.com/files/rs-5941915/v1/e457ad81dd1d4d4b08522bb4.png"},{"id":76577579,"identity":"606a3faf-197e-4867-856d-76d63b38e29b","added_by":"auto","created_at":"2025-02-18 14:24:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2160229,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e[\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF]FDG PET/CT of a patient with early increased metabolic tumor volume after initial response.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT of a patient at baseline (A), week-2 (B) and week-7 (C). Maximum Intensity Projection without and with (in red) total tumor burden delineation (upper images). Transaxial fusion images of the liver (middle images) and pelvis (lower images). The images demonstrate a decrease in metabolic tumor volume at week-2, but an increase in MTV at week-7, however with an MTV still less than baseline (green arrows and tumor delineation in red).\u003c/p\u003e","description":"","filename":"Fig6.Casusillustrationplain.png","url":"https://assets-eu.researchsquare.com/files/rs-5941915/v1/f642d9f909cd16024715c61b.png"},{"id":76576400,"identity":"bd755bec-04d6-46f2-8490-ec658baf5947","added_by":"auto","created_at":"2025-02-18 14:16:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":206739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProgression free survival of patients with increased MTV during treatment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn nine patients an increase of MTV occurred on PET at week-7 compared to week-2. No patients had higher MTV at week-2 compared to baseline. MTV=metabolic tumor volume; NA=not available.\u003c/p\u003e","description":"","filename":"Fig7.PFSofontreatmentincreasedMTVplain.png","url":"https://assets-eu.researchsquare.com/files/rs-5941915/v1/b326aaf0bef0b65c8783f3f7.png"},{"id":83782795,"identity":"94f56dc0-407f-44ff-a476-a48db41cb482","added_by":"auto","created_at":"2025-06-02 16:05:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8570632,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5941915/v1/734a019d-f6e0-40ea-a002-74d94f46e16c.pdf"},{"id":76577571,"identity":"1e9b2c9f-a609-49c6-adb5-5a9a9b0ecb28","added_by":"auto","created_at":"2025-02-18 14:24:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":590486,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental 1. Kaplan-Meier progression-free survival by optimal cutoff for percentage change in SUVpeak, MTV and TLG on treatment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier progression free survival curves by 2-weeks optimal cutoff % change SUVpeak (A), MTV (B) and TLG (C) and 7-weeks optimal cutoff % change of SUVpeak (D), MTV (E) and TLG (F). SUV=standard uptake value; MTV=metabolic tumor volume; TLG-total lesion glycolysis; NQ=not quantifiable. P-values for log-rank test are displayed.\u003c/p\u003e","description":"","filename":"Sup1.KaplanMeierontreatmentoptimalcutoffplain.docx","url":"https://assets-eu.researchsquare.com/files/rs-5941915/v1/5bf1bdc67043e28dc0895329.docx"},{"id":76576338,"identity":"69105c26-2630-4a0d-889d-8594967661cc","added_by":"auto","created_at":"2025-02-18 14:16:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":296368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental 2. Kaplan-Meier progression-free survival by TLG on treatment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier progression free survival curves by 2-weeks median % change TLG (A), 2-weeks grouped TLG (B), 7-weeks median % change TLG (C), and 7-weeks grouped TLG (D). TLG=total lesion glycosysis; NQ=Not quantifiable.\u003c/p\u003e","description":"","filename":"Sup2.KaplanMeierontreatmentTLGplain.docx","url":"https://assets-eu.researchsquare.com/files/rs-5941915/v1/1adddf49223d083fb35cd6f8.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eMetabolic Parameters on Baseline and Early [18F]FDG PET/CT as a Predictive Biomarker for Resistance to BRAF/MEK Inhibition in Advanced Cutaneous BRAFV600-mutated Melanoma\u003c/p\u003e","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eMelanoma is a major health concern globally due to its aggressive nature and high mortality rate [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Fortunately, with the introduction of immune checkpoint inhibitors (ICI) and targeted therapy, the treatment landscape for melanoma patients has revolutionized dramatically over the past decade [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Targeted therapy with combined BRAF/MEK inhibitors (BRAF/MEKi) have emerged as effective treatment options for patients harboring a V600E/K mutation in the BRAF gene. This mutation, found in 40\u0026ndash;60% of melanomas, activates the Mitogen-activated protein kinase (MAPK) signaling pathway, driving uncontrolled cell growth and survival [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eClinical trials have shown that BRAF/MEKi offers substantial clinical benefit for patients with V600E/K BRAF-mutated melanoma, reinforcing its role in treatment of melanoma [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nevertheless, though most patients initially respond to BRAF/MEKi treatment, resistance often develops over time. Prediction or early detection of this acquired resistance would provide valuable insights for clinicians to make informed decisions regarding treatment modifications \u0026ndash; such as switching to immune checkpoint inhibitors \u0026ndash; to optimize patient outcomes.\u003c/p\u003e \u003cp\u003e18F-Fluorodeoxyglucose Positron Emission Tomography / Computed Tomography ([\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT) plays a crucial role in the evaluation of cancer patients and has gained considerable attention as a valuable tool for assessing treatment response in various malignancies, including BRAF-mutated melanoma [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The Standardized Uptake Value (SUV) is the primary quantitative measure to assess relative uptake of [\u003csup\u003e18\u003c/sup\u003eF]FDG in tumors, reflecting their metabolic activity. However, SUV measurements can be influenced by various factors, including patient body weight, blood glucose levels, and the timing between FDG injection and image acquisition [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The most common implementations involve SUVmax and SUVpeak, where only the most active voxels of tumor masses are evaluated. These variations and limitations in SUV measurements can undermine their reliability in accurately evaluating treatment response and predicting patient outcomes.\u003c/p\u003e \u003cp\u003eTo overcome the limitations of SUV-based measures, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) analysis have emerged as promising alternatives for a more comprehensive tumor assessment of several cancers [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. MTV encompasses the total volume of metabolically active tumor tissue, typically defined by a SUV-threshold. By considering the entire tumor burden rather than focusing on specific tumor areas, MTV provides a holistic perspective on the malignancy\u0026rsquo;s extent and biological behavior. In addition, MTV is less influenced by patient-related and technical factors, making it a more robust parameter for quantitative analysis in clinical practice. Emerging evidence suggests that MTV may serve as a predictive marker in various malignancies, including patients treated with BRAF/MEKi [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This predictive capability could enhance risk stratification, treatment planning, and monitoring therapeutic responses.\u003c/p\u003e \u003cp\u003eWhile MTV only represents the volume of metabolically active tumor tissue, TLG integrates the metabolic tumor volume with its actual metabolic activity (as represented by SUV). It is calculated by multiplying the MTV by the mean SUV within the tumor volume, offering a metric that considers both the volume and the metabolic intensity of the tumors. However, the value of TLG in addition to MTV for predicting treatment response in metastatic melanoma is unknown.\u003c/p\u003e \u003cp\u003eThe aim of this study is to assess the predictive value of different metabolic PET parameters on [\u003csup\u003e18\u003c/sup\u003eF]FDG-PET/CT and determine whether these parameters can serve as indicators for progression-free survival in patients with advanced melanoma undergoing BRAF/MEK-targeted therapy.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eSeventy-five patients diagnosed with histologically proven advanced or metastatic BRAF-mutated melanoma were enrolled in the REPOSIT-trial (NCT02414750) from March 2015 to February 2019. The study design has been published previously [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Briefly, this phase II, multi-center, single arm prospective study included BRAF-mutated unresectable stage IIIC or stage IV American Joint Committee on Cancer Classification (AJCC) 7th edition [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] melanoma patients with measurable lesions according to Response Evaluation Criteria In Solid Tumours version 1.1 (RECIST1.1) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Patients were treated with combined BRAF/MEK inhibitor vemurafenib plus cobimetinib until progression or uncontrollable toxicity. Patients were recruited from nine hospitals, which are part of the Dutch Melanoma and Skin Cancer group (DMSCG). The study was approved by the local Medical Ethical Committees. Written informed consent was obtained before inclusion. The study closed enrollment before reaching the anticipated sample size of 90 patients as outlined in the study protocol, due to slow patient accrual. Ultimately, a total of 75 patients were included. Comprehensive data such as patient demographics, clinical, histopathological, imaging, and laboratory data were collected.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImaging protocol\u003c/h3\u003e\n\u003cp\u003ePatients underwent baseline [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT within one month prior to the initiation of therapy and follow-up [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT on day 15 of Cycle 1 (week-2) and day 21 of Cycle 2 (week-7). Contrast-enhanced CT scans were also performed in accordance with protocol, every eight weeks, and whenever progressive disease was suspected.\u003c/p\u003e \u003cp\u003ePET/CT scans were performed in accordance with the European Association of Nuclear Medicine (EANM) guideline for oncology [18F]FDG PET/CT imaging [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Prior to PET/CT scan, patients fasted for at least 4 hours and had more than 400ml fluid intake. Serum glucose levels were below 11.0 mmol/L. [\u003csup\u003e18\u003c/sup\u003eF]FDG was administered intravenously, with an activity dosage in agreement to the local institutional protocol, ensuring compliance with the EANM Research Ltd. (EARL) standard 1 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Approximately 60 minutes (range 55\u0026ndash;65 min) after administration, PET/CT images were acquired from at least base of the skull to thighs at 2\u0026ndash;4 min per bed position in a supine position. A whole-body low-dose CT scan was also conducted for attenuation correction and anatomic localization.\u003c/p\u003e \u003cp\u003ePET/CT scans were performed on a Gemini TF PET/CT, TF Big Bore PET/CT, Ingenuity TF PET/CT (all Philips Medical Systems, Best, the Netherlands), or Siemens Biograph mCT PET/CT (Siemens Healtineers, Erlangen, Germany). For consistency, all PET/CT scans for an individual patient were performed on the same scanner, with a maximum variation of 10% in activity compared to baseline. All participating PET imaging centers were accredited for EARL standard 1 and performed image reconstruction accordingly (EANM resEARch4Life, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earl.eanm.org/\u003c/span\u003e\u003cspan address=\"https://earl.eanm.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eImage analysis and response evaluation\u003c/h3\u003e\n\u003cp\u003eThe PET/CT scans were sent for central review, where objective evaluation was performed by an experienced nuclear medicine physician (BvdH). Areas of increased uptake were identified for further quantification. In cases of uncertainty, a second experienced nuclear medicine physician (MS) reviewed the data to reach a consensus.\u003c/p\u003e \u003cp\u003eQuality control review of all PET/CT scans was conducted to assess their suitability for analysis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Scans were included when SUVmean of the liver fell within the range of 1.3-3.0, measured by placing a spherical VOI with a 3cm diameter in the right upper lobe of the liver, avoiding malignancies and organ boundaries [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In case of extensive liver metastases, the SUVmean blood pool was required to fall within 0.79\u0026ndash;2.32, measured by placing several VOIs in the thoracic aorta, ensuring the vessel wall was excluded [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Scans with SUVmean outside these normal ranges underwent further evaluation of protocol adherence to identify potential errors, and each scan was individually assessed for in- or exclusion.\u003c/p\u003e \u003cp\u003eSince at the initiation of the REPOSIT trial there was limited evidence-based data on assessing therapy response and resistance using [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT in patients with unresectable stage IIIc or metastatic melanoma treated with BRAF/MEK inhibitors, the PET imaging analyses were intentionally not pre-specified. Instead, we conducted our analyses in alignment with the most current literature available at the time of the study. For quantification of the PET images, a validated in-house developed software package (ACCURATE) was used [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. With the total tumor burden tool (TTB) in ACCURATE, PET-images were automatically delineated using the PET image-based segmentation method SUV40, resulting in a region of interest (ROI) of the summed lesions with a fixed SUV threshold of 4.0 and a volume of \u0026gt;\u0026thinsp;1mL, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The resulting ROI delineation was inspected visually and manually corrected if necessary. SUVpeak, defined as a 1-mL spherical volume of interest with the highest uptake, MTV and TLG were calculated.\u003c/p\u003e \u003cp\u003eOn follow-up scans, patients with tumor responses resulting in no measurable metastases (i.e. a complete metabolic response, or with remaining metastatic lesions with a SUV below the threshold of 4.0 and a volume of less than 1mL) were categorized as \u0026lsquo;not quantifiable\u0026rsquo;. For the remaining quantifiable [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT scans, parameters were calculated as the percentage difference compared to baseline using the formula: ∆%=100*(baseline value \u0026ndash; week 2 value)/baseline value. Similarly to baseline values, cutoffs were established, and percentage differences were categorized into two groups (above and below cutoff), with the \u0026lsquo;not quantifiable\u0026rsquo; category kept separate. In a separate analysis, patients with a \u0026lsquo;not quantifiable\u0026rsquo; response PET were combined with those showing a ∆% above median (indicating good responders). This combined group (labelled; GroupedSUVpeak, GroupedMTV or GroupedTLG) was compared to the patient group with a ∆% below the median (indicating lesser responders).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor summarizing patient characteristics, median and range for continuous variables, and frequency and percentage for categorical variables were displayed. PFS was defined as the time from commencement of BRAF/MEKi to disease progression (based on clinical findings and/or RECIST1.1) or death from any cause in the absence of progression, whichever occurred first. Patients without any of these events before the end of follow-up were censored at the date last known to be alive and progression/recurrence-free. Patients starting non-protocol treatment were censored at the date of start of this new treatment.\u003c/p\u003e \u003cp\u003eSurvival curves were generated using the Kaplan-Meier method, and compared using the log-rank test. Univariable and multivariable Cox regression analyses were performed, hazards ratio (HR) and the corresponding 95% confidence interval (CI) were reported. The likelihood of pairs of nested models was compared using a likelihood ratio test. Multivariable analyses were adjusted for baseline lactate dehydrogenase (LDH) level, Eastern Cooperative Oncology Group (ECOG) performance status and number of metastatic organs at baseline, since these are the most common prognostic biomarkers for progression-free survival (PFS) and overall survival (OS) in melanoma patients treated with BRAF/MEKi [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo evaluate the discriminating potential of MTV, TLG and SUV, time-dependent receiver operating characteristic (ROC) curve analyses were performed for survival data. The area under the curve at 6 months was computed. The survivalROC package in R was used for this purpose, using the nearest neighbor estimator method of the bivariate distribution of the censoring time and the failure time with moderate smoothing (span\u0026thinsp;=\u0026thinsp;5%) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRestricted cubic splines were used to evaluate the association between each PET parameter and PFS and the suitability of a threshold to dichotomize the PET parameter for further analysis. Since no clear threshold was observed, maximally selected rank statistics were used to determine an optimal cutoff achieving the maximum PFS benefit from each PET parameter. The method by Hothorn and Lausen [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] in the R package maxstat was used for approximating the p-value for the comparison between groups based on the optimal cutoff. Due to the explorative nature of this study and the limited sample size in relation to the number of tests performed, no adjustments for multiplicity were performed except for the adjustment of the \u003cem\u003eP\u003c/em\u003e-value in the maximally selected rank statistics analysis. All \u003cem\u003eP\u003c/em\u003e-values were 2-sided. Statistical analyses were performed using R statistical software (version 4.2.0; The R Foundation for Statistical Computing, Vienna, Austria) and SAS statistical software package (version 9.4; SAS Institute Inc. Cary, NC).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eSixty-nine out of 75 patients were included for [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT-analysis, including 36 males and 33 females, with a median age of 63 y (range, 30\u0026ndash;88 y). A flow chart of in- and exclusion is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, detailed patient demographics are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All but 3 (4.3%) patients were diagnosed with stage IV disease, 49 (71.0%) patients had metastases in at least three different tissue types. The median follow-up time among all patients regardless of censoring status was 15.0 months (IQR 9.1 to 24.9 months). In 36 patients (53.7%) treatment ended due to progression and in 17 patients (25.4%) due to adverse events.\u003c/p\u003e \u003cp\u003eAll 69 patients received baseline [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT. In 62 (89.9%) patients [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT was performed at week-2 and in 61 (88.4%) at week-7. In 58 (84.1%) patients [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT was performed at all three time points (baseline, week-2 and week-7). The median PFS of all included patients was 9.6 months (IQR 8 to 14.9 months).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;69 patients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years (median (range))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (30\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG performance status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (56.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC 7th edition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocally advanced (Stage IIIc)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic (Stage IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (95.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (6\u0026ndash;34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin-max\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1-128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of metastatic sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (71.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; ULN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; ULN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT scans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 15 Cycle 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (89.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 21 Cycle 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (88.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eECOG\u0026thinsp;=\u0026thinsp;Eastern Cooperative Oncology Group; AJCC\u0026thinsp;=\u0026thinsp;American Joint Committee on Cancer; LDH\u0026thinsp;=\u0026thinsp;Lactate dehydrogenase; ULN\u0026thinsp;=\u0026thinsp;Upper limit of normal.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe predictive power of baseline [F]FDG PET/CT for progression\u003c/h3\u003e\n\u003cp\u003eAt baseline, all patients presented with metastases that met the criteria of a SUV threshold\u0026thinsp;\u0026gt;\u0026thinsp;4.0 and a volume\u0026thinsp;\u0026gt;\u0026thinsp;1 mL, were included for automated delineation of ROIs to determine SUVpeak, MTV and TLG. Median SUVpeak was 12.59 (range 3.13\u0026ndash;50.59); median MTV was 159mL (range 0-1897 mL) and median TLG was 1013 (range 1-13162). Time-dependent ROC curve analyses demonstrated that MTV had the best predictive performance for identifying patients at risk of progression at 6 months, as evidenced by its highest AUC\u003csub\u003eT=6 months\u003c/sub\u003e =0.714 among the evaluated metrics. For TLG, AUC\u003csub\u003eT=6 months\u003c/sub\u003e =0.685, while for SUVpeak AUC\u003csub\u003eT=6months\u003c/sub\u003e =0.598.\u003c/p\u003e\n\u003ch3\u003eBaseline [F]FDG PET parameters using median and optimal cutoff values\u003c/h3\u003e\n\u003cp\u003eUsing median SUVpeak (12.6) as a cutoff, median PFS for patients with a SUVpeak below the median, was 14.7 months, versus 9.2 months for patients with a SUVpeak above the median (P\u0026thinsp;=\u0026thinsp;0.064), see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. For patients with MTV\u0026thinsp;\u0026gt;\u0026thinsp;159.2mL (median cutoff), the median PFS was 8.0 months, compared to 14.9 months for those with MTV\u0026thinsp;\u0026le;\u0026thinsp;159mL, P\u0026thinsp;=\u0026thinsp;0.094 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). For TLG, the median PFS for patients with a TLG above the median of 1013.2 was significantly higher than for those with TLG below the median (8.5 vs. 15.4 months, P\u0026thinsp;=\u0026thinsp;0.024), Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, but only in univariable analyses. Results from univariable and multivariable Cox regression analysis for these baseline PET parameters are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWhen using maximally selected rank statistics, no significant PFS differences were found between groups for SUVpeak. With the estimated best cutoff of 10.9, the median PFS for patients with SUVpeak\u0026thinsp;\u0026le;\u0026thinsp;10.9 was 16.8 months vs. 8.8 months SUVpeak\u0026thinsp;\u0026gt;\u0026thinsp;10.9, P\u0026thinsp;=\u0026thinsp;0.32, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD. For MTV, a significant difference in PFS was found with a cutoff of 45.3 mL: the median PFS for patients with MTV\u0026thinsp;\u0026gt;\u0026thinsp;45.3mL was 8.5 months vs. 21.6 months for those with MTV\u0026thinsp;\u0026le;\u0026thinsp;45.3mL, P\u0026thinsp;=\u0026thinsp;0.021, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE. At this cutoff, the sensitivity and positive predictive value from the time-dependent ROC curve at 6 months were 1.00 and 0.81, respectively, whereas using the median cutoff of 159.2mL, sensitivity decreased to 0.64. Patients with MTV\u0026thinsp;\u0026gt;\u0026thinsp;45.3 mL had a hazard of progression more than three times higher (HR\u0026thinsp;=\u0026thinsp;3.53, 95% CI 1.50\u0026ndash;8.36, P\u0026thinsp;=\u0026thinsp;0.021), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The prolonged PFS remained significant in the multivariable analysis adjusted for baseline LDH level, ECOG performance status and number of metastatic sites at baseline (HR\u0026thinsp;=\u0026thinsp;2.97, 95% CI 1.17\u0026ndash;7.52, P\u0026thinsp;=\u0026thinsp;0.022). Best cutoff for TLG was estimated to be 268, but since the patient distribution above and below this threshold was the same to the MTV cutoff (14 patients below and 55 patients above), the results for TLG were consistent with those for MTV, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable Cox regression results for progression-free survival SUVpeak, MTV and TLG at baseline.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultivariable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003e#\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003e##\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUVpeak using median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u0026ndash;2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u0026ndash;2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUVpeak using optimal cutoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u0026ndash;4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u0026ndash;3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTV using median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 159.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 159.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u0026ndash;2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u0026ndash;2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTV using optimal cutoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 45.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 45.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50\u0026ndash;8.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17\u0026ndash;7.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLG using median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 1013.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 1013.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u0026ndash;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u0026ndash;2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLG using optimal cutoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 268.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 268.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50\u0026ndash;8.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17\u0026ndash;7.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll patients presented with metastases that met the criteria of an SUV threshold\u0026thinsp;\u0026gt;\u0026thinsp;4.0 and a volume\u0026thinsp;\u0026gt;\u0026thinsp;1 mL, which were included for the automated delineation of regions of interest (ROIs) to determine SUVpeak, MTV and TLG.\u003c/p\u003e \u003cp\u003e \u003csup\u003e#\u003c/sup\u003e Log-rank test p-value. When maximally selected log-rank statistics are used to determine the optimal cutoff, the p-value is approximated using the method by Hothorn and Lausen.\u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003e##\u003c/em\u003e \u003c/sup\u003e From multivariable Cox regression model adjusted for LDH, ECOG performance status and the number of metastatic organs at baseline.\u003c/p\u003e \u003cp\u003eOf note: Due to the different tests used for obtaining p-values (log-rank with or without approximated p-value using the method by Hothorn and Lausen for the univariable analysis, Wald test for multivariable analysis) comparison between univariable and multivariable analyses should focus on HR estimates.\u003c/p\u003e \u003cp\u003e* p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (for median cutoff only). SUV\u0026thinsp;=\u0026thinsp;standard uptake value; MTV\u0026thinsp;=\u0026thinsp;metabolic tumor volume; TLG-total lesion glycolysis.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eChanges in early and late [\u003csup\u003e18\u003c/sup\u003eF]FDG PET parameters on treatment\u003c/h2\u003e \u003cp\u003eDuring BRAF/MEKi treatment, [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT performed at week-2 revealed in 23 (37.1%) patients a SUV\u0026thinsp;\u0026lt;\u0026thinsp;4.0 in all remaining metastases, preventing automated delineation of ROI. These scans were classified as \u0026lsquo;not quantifiable' and were considered good responders. At week-7, the number of not quantifiable scans increased to 32 (52.5%). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides an example of a patient with a not quantifiable scan at week-7.\u003c/p\u003e \u003cp\u003eFor the optimal cutoff percentage difference determined with maximally selected rank statistics the outcome was similar compared to the median percentage difference for both MTV and TLG. Therefore, for on treatment results, we focused on the median percentage difference (median ∆%) PET parameters. Kaplan-Meier curves for PFS with MTV and TLG grouped according to the optimal cutoff are summarized in Supplemental 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePercentage change from baseline SUVpeak\u003c/h2\u003e \u003cp\u003eKaplan-Meier curves for PFS, stratified by the percentage change in SUVpeak from baseline and a separate not quantifiable group, are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The median percentage difference of SUVpeak at week-2 (median ∆SUVpeak%\u003csub\u003eweek\u0026minus;2\u003c/sub\u003e) was 61% (range: -5\u0026ndash;100%), and at week-7 (median ∆SUVpeak%\u003csub\u003eweek\u0026minus;7\u003c/sub\u003e) it was 64% (range \u0026minus;\u0026thinsp;53\u0026ndash;100%). The not quantifiable group had the longest median PFS at both time-points (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). When combining the not quantifiable group with patients who had a ∆SUVpeak% above median, the PFS for this GroupedSUVpeak was significantly longer compared to ∆SUVpeak% below the median at week-7, but not at week-2 (P\u0026thinsp;=\u0026thinsp;0.0002 versus P\u0026thinsp;=\u0026thinsp;0.056, respectively), see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD. These results were corroborated in multivariable analyses (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable Cox regression results for progression free survival SUVpeak, MTV and TLG on treatment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eMultivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003e#\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003e##\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOn treatment Baseline \u0026minus;\u0026thinsp;2 weeks\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSUVpeak\u0026nbsp; % difference using median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quantifiable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 60.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u0026ndash;4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u0026ndash;3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 60.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21\u0026ndash;5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.77\u0026ndash;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUVpeak Grouped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quantifiable\u0026thinsp;+\u0026thinsp;SUVpeak\u0026thinsp;\u0026gt;\u0026thinsp;60.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u0026ndash;3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.69\u0026ndash;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMTV\u0026nbsp; % difference using median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quantifiable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 95.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u0026ndash;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36\u0026ndash;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 95.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33\u0026ndash;6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0070*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u0026ndash;4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTV Grouped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quantifiable\u0026thinsp;+\u0026thinsp;MTV\u0026thinsp;\u0026gt;\u0026thinsp;95.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;95.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17\u0026ndash;3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21\u0026ndash;4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTLG\u0026nbsp; % difference using median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quantifiable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 97.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u0026ndash;3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.44\u0026ndash;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 97.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29\u0026ndash;5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0089*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.87\u0026ndash;4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLG\u0026nbsp; Grouped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quantifiable\u0026thinsp;+\u0026thinsp;TLG\u0026thinsp;\u0026gt;\u0026thinsp;97.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;97.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12\u0026ndash;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.04\u0026ndash;4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.039*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOn treatment Baseline \u0026minus;\u0026thinsp;7 weeks\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSUVpeak\u0026nbsp; % difference using median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quantifiable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u0026ndash;2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.83\u0026ndash;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.88\u0026ndash;8.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.92\u0026ndash;10.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0006*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSUVpeak Grouped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quantifiable\u0026thinsp;+\u0026thinsp;SUVpeak\u0026thinsp;\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76\u0026ndash;7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.66\u0026ndash;8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0014*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMTV\u0026nbsp; % difference using median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quantifiable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 97.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u0026ndash;2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.61\u0026ndash;2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 97.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.19\u0026ndash;10.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.46\u0026ndash;9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0057*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTV Grouped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quantifiable\u0026thinsp;+\u0026thinsp;MTV\u0026thinsp;\u0026gt;\u0026thinsp;97.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;97.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.07\u0026ndash;8.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.40\u0026ndash;7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0062*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTLG\u0026nbsp; % difference using median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quantifiable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 98.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u0026ndash;2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.66\u0026ndash;2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 98.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.19\u0026ndash;10.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.68\u0026ndash;11.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0027*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLG Grouped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quantifiable\u0026thinsp;+\u0026thinsp;TLG\u0026thinsp;\u0026gt;\u0026thinsp;98.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;98.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.07\u0026ndash;8.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.54\u0026ndash;9.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0036*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePatients without metastases meeting the criteria of an SUV threshold\u0026thinsp;\u0026gt;\u0026thinsp;4.0 and a volume\u0026thinsp;\u0026gt;\u0026thinsp;1 mL were classified as not quantifiable. SUV\u0026thinsp;=\u0026thinsp;standard uptake value; MTV\u0026thinsp;=\u0026thinsp;metabolic tumor volume; TLG-total lesion glycolysis.\u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003e#\u003c/em\u003e \u003c/sup\u003e Log-rank test p-value.\u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003e##\u003c/em\u003e \u003c/sup\u003e From multivariable Cox regression model adjusted for baseline SUVpeak/MTV/TLG, LDH, ECOG performance status and the number of metastatic organs at baseline.\u003c/p\u003e \u003cp\u003e* p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eUsing the not quantifiable group as reference for good response, patients with ∆SUVpeak% below the median had a worse PFS at both time-points (∆SUVpeak%\u003csub\u003eweek\u0026minus;2\u003c/sub\u003e: HR\u0026thinsp;=\u0026thinsp;2.51, 95% CI 1.21\u0026ndash;5.18, P\u0026thinsp;=\u0026thinsp;0.048; ∆SUVpeak%\u003csub\u003eweek\u0026minus;7\u003c/sub\u003e: HR\u0026thinsp;=\u0026thinsp;4.00, 95% CI 1.88\u0026ndash;8.48, P\u0026thinsp;=\u0026thinsp;0.0003), though only the latter remained significant in multivariable analyses. This was also true for week-7 when not quantifiable was grouped with patients having a ∆SUVpeak% above the median (GroupedSUVpeak), at detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. It should be noted that this model had a poorer fit for the data than when no grouping was done, according to likelihood ratio test results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePercentage change from baseline metabolic tumor volume\u003c/h2\u003e \u003cp\u003eAt both week-2 (∆%\u003csub\u003eweek\u0026minus;2\u003c/sub\u003e) and week-7 (∆%\u003csub\u003eweek\u0026minus;7\u003c/sub\u003e), the groups specified by percentage change below and above median MTV were similar as those for TLG, resulting in the same outcomes for both PET-parameters. As a results, we focused on changes in MTV only. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays Kaplan-Meier curves for PFS, stratified by median percentage changes in MTV during treatment. See Supplemental 1 for Kaplan-Meier PFS by optimal cutoff for percentage change in MTV. Among the quantifiable scans, the median percentage change in MTV at week-2 (median ∆MTV%\u003csub\u003eweek\u0026minus;2\u003c/sub\u003e) was 96% (range 22\u0026ndash;100%), and at week-7 (median ∆MTV%\u003csub\u003eweek\u0026minus;7\u003c/sub\u003e) this was 98% (range \u0026minus;\u0026thinsp;41\u0026ndash;100%). Significant differences in PFS were seen between the 3 groups at both week-2 and week-7 (P\u0026thinsp;=\u0026thinsp;0.020\u003csub\u003eweek\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/sub\u003e; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003csub\u003eweek\u0026thinsp;\u0026minus;\u0026thinsp;7\u003c/sub\u003e), see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC. When combining not quantifiable with patients having a ∆MTV% above median, the PFS for this combined group (GroupedMTV) was significantly longer compared to those with ∆MTV% below median (median 13.9 vs. 6.9 months at week-2, and 14.7 vs. 4.5 months at week-7), see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD. See Supplemental 2 for the results of TLG.\u003c/p\u003e \u003cp\u003eWith the not quantifiable group used as reference for good response, at both time points the hazard of a PFS event was significantly higher for patients with ∆MTV% below the median, but not for patients with ∆MTV% above the median; HR\u0026thinsp;=\u0026thinsp;2.83 (95% CI 1.33\u0026ndash;6.03, P\u0026thinsp;=\u0026thinsp;0.0070) vs. HR\u0026thinsp;=\u0026thinsp;1.65 (95% CI 0.79\u0026ndash;3.43, P\u0026thinsp;=\u0026thinsp;0.1804) at week-2, and HR\u0026thinsp;=\u0026thinsp;4.7 (95% CI 2.19\u0026ndash;10.07, P\u0026thinsp;=\u0026thinsp;0.0001) vs. HR\u0026thinsp;=\u0026thinsp;1.36 (95% CI 0.66\u0026ndash;2.80, P\u0026thinsp;=\u0026thinsp;0.3988) at week-7, see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Only results for week-7 remained significant in multivariable analyses. The hazard of a PFS event remained significant when not quantifiable was grouped with median ∆MTV% above the median (Grouped MTV) in both univariable and multivariable analyses for week-2 and week-7, see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A likelihood ratio test indicated that this model was a worse fit for the data than the ungrouped model though.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOn treatment increase of metabolic tumor volume\u003c/h2\u003e \u003cp\u003eAt week-2 and week-7, all patients revealed a decrease in MTV compared to baseline. However, when PET/CT of week-7 was compared to week-2, an increase in MTV was measured in 9/58 (15.5%) patients, illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Median PFS of these 9 patients was 5.3 months compared to 12.6 months of the other patients with stable or ongoing decrease of MTV (P\u0026thinsp;=\u0026thinsp;0.0023), see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. A multivariable Cox regression analysis displayed a high HR but power was limited (HR\u0026thinsp;=\u0026thinsp;2.34, 95% CI 0.96\u0026ndash;5.74, P\u0026thinsp;=\u0026thinsp;0.062).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT has emerged as a powerful imaging tool for evaluating treatment response and predicting outcomes in various cancers, including BRAF-mutated melanoma [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this study, we present the results of baseline [\u003csup\u003e18\u003c/sup\u003eF]FDG PET parameters, such as SUVpeak, MTV, and TLG, in predicting PFS in patients with advanced BRAF-mutated melanoma undergoing BRAF/MEKi therapy. Additionally, the potential of these parameters as early indicators of treatment resistance was assessed.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBaseline SUVpeak, MTV and TLG\u003c/h2\u003e \u003cp\u003eAt baseline, the ability of median SUVpeak (12.59) to predict PFS was modest, with an AUC of 0.598 at six months. While lower baseline SUVpeak values suggested a trend toward prolonged PFS (median 14.7 vs. 9.2 months), the difference was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.064). Similarly, a cutoff of 10.9 showed a potential survival benefit, but without statistical significance (P\u0026thinsp;=\u0026thinsp;0.32). The limited predictive value of SUVpeak may be due to its focus on metabolic activity within a small tumor region, thus overlooking tumor heterogeneity and total disease burden. Additionally, SUVpeak is influenced by technical factors such as image acquisition and reconstruction methods, as well as biological factors like glucose metabolism [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThough moderate, our study findings indicate that MTV had a higher predictive value for PFS (AUC\u0026thinsp;=\u0026thinsp;0.714 at six months) than SUVpeak. Patients with a baseline MTV below 45.3mL had significantly longer PFS (21.6 vs. 8.5 months, P\u0026thinsp;=\u0026thinsp;0.021), while those with MTV above this threshold experienced a threefold increased risk of progression (HR\u0026thinsp;=\u0026thinsp;3.53). The multivariable analysis also indicated prolonged PFS in patients with a baseline MTV below 45.3 mL. However, it is important to note that the corresponding p-value cannot be directly approximated for selecting the optimal cutoff due to the multivariable nature of the analysis, which limits its interpretability. Nonetheless, these findings demonstrate MTV's potential to better capture overall disease burden and predict long-term outcomes.\u003c/p\u003e \u003cp\u003eThe results for TLG closely mirrored those of MTV, reinforcing the reliability and robustness of volumetric PET parameters. In contrast to SUVpeak, MTV provides a more comprehensive view of the total volume of metabolically active tumor tissue, capturing volumetric changes across all metastatic sites rather than focusing on selected lesions.\u003c/p\u003e \u003cp\u003eIn literature, two studies investigated baseline MTV on [\u003csup\u003e18\u003c/sup\u003eF]FDG PET as a predictor of survival following BRAF/MEK inhibition in patients with advanced BRAFV600-mutated melanoma. McArthur et al. prospectively evaluated 35 BRAFi/MEKi-na\u0026iuml;ve melanoma patients treated with vemurafenib and cobimetinib [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As in our study, they observed significant early and improving metabolic responses during therapy. [\u003csup\u003e18\u003c/sup\u003eF]FDG PET scans, performed during the first two treatment cycles (day 10\u0026ndash;15 and day 35\u0026ndash;49), showed marked reductions in tumor burden and metabolism, with patients achieving substantial decreases in MTV and SUVmax. While baseline tumor burden did not correlate with metabolic response, baseline MTV was a predictor of overall survival (OS), with lower baseline values linked to longer survival. In a retrospective cohort of 57 metastatic melanoma patients treated with BRAF/MEK inhibitors, Annovazzi et al. revealed that a total metabolic tumor volume (TMTV), i.e. the sum of metastases with a SUVmax\u0026thinsp;\u0026gt;\u0026thinsp;2 and with a volume\u0026thinsp;\u0026gt;\u0026thinsp;0.5mL, of over 56mL at baseline [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT and the presence of more than two metastatic organ sites were significantly correlated with shorter PFS and OS, with TMTV being the only independent predictor in multivariate analysis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Noteworthy, this cutoff is almost similar to the optimal cutoff of 45mL found in our study, where the minimal differences might be explained by the different threshold for automatic delineation in our study (SUVmax\u0026thinsp;\u0026gt;\u0026thinsp;4 and volume of \u0026gt;\u0026thinsp;1mL). These findings underscore that baseline MTV is a valuable predictive indicator for survival in advanced melanoma patients treated with BRAF/MEKi.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEarly changes of metabolic parameters during BRAF/MEKi treatment\u003c/h2\u003e \u003cp\u003eAt week-2, percentage changes in SUVpeak, when stratified above or below the median, were not predictive of PFS. Patients with not quantifiable lesions on PET (i.e. no metastases above the SUV threshold of 4) had the best PFS, with the hazard for an event significantly higher for patients with a percentage change in SUVpeak above or below the median. These results indicate that early response prediction for determining the best PFS is more accurately associated with an absolute SUV threshold rather than mean percentage differences. In contrast, percentage changes in SUVpeak became predictive at week-7, where a shorter PFS was seen for patients with percentage changes in SUVpeak below the median compared to patients with changes above the median or the not quantifiable patients. Therefore, in our study no incremental predictive benefit was observed using percentage changes of SUVpeak on early [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT at week-2.\u003c/p\u003e \u003cp\u003eOnly one study investigated the correlation between SUV on early [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT response and survival during BRAF/MEKi treatment of melanoma patients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this study by Schmitt et al., changes in SUVmax of the hottest lesion and of the least responsive tumor on follow-up [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT were calculated in 24 patients and correlated to PFS and OS. Mean time from baseline to follow-up [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT was 26 days, being approximately double the duration compared to our study. They observed a significant association between the smallest change in SUVmax and progression-free survival (P\u0026thinsp;=\u0026thinsp;0.01), but not overall survival (P\u0026thinsp;=\u0026thinsp;0.52). Though our results indicate that percentage change SUVpeak might be predictive at week-7, it might already be at an earlier time-point of four weeks, as was observed by Schmitt et al.\u003c/p\u003e \u003cp\u003eWhen examining the impact of MTV and TLG during treatment, significant reductions in MTV and TLG at both week-2 and week-7 follow-up scans were associated with improved PFS. Patients who demonstrated a\u0026thinsp;\u0026ge;\u0026thinsp;96% reduction in MTV at week-2 and \u0026ge;\u0026thinsp;98% reduction at week-7 experienced significantly longer PFS compared to those with lesser reductions. When adjusting for LDH, ECOG performance status and number of metastatic sites, the risk of progression for patients with MTV above this threshold remained significantly increased at both time-points compared to the Grouped MTV (HR\u0026thinsp;=\u0026thinsp;2.36 week-2 vs HR\u0026thinsp;=\u0026thinsp;0.0057). However, at both week-2 and week-7, best PFS was observed in the not quantifiable group, being classified based on a SUV threshold of 4. So, during treatment, patients with the best PFS are determined based on SUV rather than MTV, indicating that metabolic activity, as reflected by changes in SUV, may be a more sensitive and reliable predictor of treatment response and long-term outcomes than the overall tumor burden measured by MTV. However, these findings do emphasize the potential of MTV and TLG as early markers of treatment efficacy, with the possibility of identifying patients at risk of early progression even before clinical or radiographic evidence of disease worsening. Interestingly, patients whose MTV increased between week-2 and week-7 had a median PFS of only 5.3 months, compared to 12.6 months for patients with continued MTV reduction. This suggests that any increase in MTV during treatment may be an early indicator of resistance to BRAF/MEKi therapy. Such findings underscore the importance of serial [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT imaging in monitoring treatment response, as changes in MTV could provide critical insights into disease dynamics, allowing for timely modifications to treatment strategies. In this context, [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT might be a valuable tool for patients who are too frail to initiate treatment with first line ICI, but for whom a switch to immunotherapy is being considered later following BRAF/MEKi therapy.\u003c/p\u003e \u003cp\u003eA key limitation of our study is the relatively small sample size (n\u0026thinsp;=\u0026thinsp;69), which may limit the generalizability of our findings. Additionally, due to the application of the SUV threshold for MTV and TLG analysis during treatment to adequately distinguish tumor from physiologic uptake, the sample size of patients eligible for evaluation of MTV and TLG was further reduced. Nevertheless, to our knowledge, this is the only study to prospectively investigate early [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT during treatment of BRAF/MEK inhibition in advanced BRAF-na\u0026iuml;ve melanoma patients. Furthermore, the uniqueness of this cohort lies in the fact that patients were treated with BRAF/MEK inhibitors until disease progression, a treatment approach that is less feasible in current clinical practice due to changes in the therapeutic landscape.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, this study highlights the predictive value of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT in assessing BRAF/MEKi treatment response in advanced BRAF-mutated melanoma. Baseline metabolic tumor volume (MTV) was the best predictive indicator, with lower MTV linked to longer PFS, while SUVpeak had limited predictive power. During treatment, percentage changes in MTV and TLG all correlated with improved PFS already on early imaging and additional SUVpeak at week-7, with treatment response best predicted by an absolute SUV threshold of 4. Increase of MTV on serial [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT from week-2 to week-7 can identify early resistance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]FDG 18F-Fluorodeoxyglucose \u003c/p\u003e\n\u003cp\u003eAUC area under the curve\u003c/p\u003e\n\u003cp\u003eCI confidence interval\u003c/p\u003e\n\u003cp\u003eCT computed tomography \u003c/p\u003e\n\u003cp\u003eEANM European Association of Nuclear Medicine\u003c/p\u003e\n\u003cp\u003eEARL EANM Research Ltd.\u003c/p\u003e\n\u003cp\u003eECOG Eastern Cooperative Oncology Group\u003c/p\u003e\n\u003cp\u003eHR hazard ratio\u003c/p\u003e\n\u003cp\u003eIQR interquartile range\u003c/p\u003e\n\u003cp\u003eLDH lactate dehydrogenase\u003c/p\u003e\n\u003cp\u003eMAPK mitogen-activated protein kinase\u003c/p\u003e\n\u003cp\u003eMTV metabolic tumor volume\u003c/p\u003e\n\u003cp\u003eNA not available\u003c/p\u003e\n\u003cp\u003ePET positron emission tomography \u003c/p\u003e\n\u003cp\u003ePFS progression-free survival\u003c/p\u003e\n\u003cp\u003eRECIST response evaluation criteria in solid tumors\u003c/p\u003e\n\u003cp\u003eROC receiver-operating characteristic\u003c/p\u003e\n\u003cp\u003eROI region of interest\u003c/p\u003e\n\u003cp\u003eSUV standardized uptake value\u003c/p\u003e\n\u003cp\u003eTF time of flight\u003c/p\u003e\n\u003cp\u003eTLG total lesion glycolysis\u003c/p\u003e\n\u003cp\u003eTTB total tumor burden\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Medical Ethical Committee of the Netherlands Cancer Institute approved the study for all participating centers. Informed consent was obtained from all participants before entering the study. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that human research participants provided informed consent for publication of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvaillability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAvdE: Study grant: Roche, Idera, Travel expenses: Ipsen Advisory Board: Bristol-Myers Squibb, MSD Oncology, Ipsen, Janssen Cilag BV, Pierre Fabre EK: consultancy/advisory relationships with Bristol Myers Squibb, Novartis, Merck, Pierre Fabre, Lilly and Bayer, all paid to institute, received research grants not related to this paper from Bristol Myers Squibb, Delcath, Novartis and Pierre Fabre. GH: consultancy/advisory relationships with Amgen, Bristol-Myers Squibb, Roche, MSD, Pfizer, Novartis, Sanofi, Pierre Fabre, all paid to institute, received research grants from Bristol-Myers Squibb, Seerave, all paid to institute. MA: advisory board / consultancy honoraria from Amgen, Bristol Myers Squibb, Novartis, MSD-Merck, Merck-Pfizer, Pierre Fabre, Sanofi, Astellas, Bayer. Research grants Merck-Pfizer, all paid to institute and not related to current work. FdV: received research grant from Foundation STOPBraintumors.org, BMS, Novartis, Servier, CureVac, EORTC, all paid to institute. AvdV: consultancy roles (all paid to the institute) for BMS, MSD, Roche, Sanofi, Novartis, Pierre Fabre, Merck, Ipsen, Eisai, Pfizer, all paid to the institute. JH: advisory roles for BMS, CureVac, GSK, Ipsen, Iovance Biotherapeutics, Imcyse, Merck Serono, Molecular Partners, MSD, Novartis, Pfizer, Roche, Sanofi, Third Rock Ventures, member of SAB of Achilles Tx, BioNTech, Gadeta, Immunocore, Instil Bio, PokeAcell, Scenic, T-Knife and Neogene Tx, all paid to institute except Neogene Tx and Scenic, received grant support from Amgen, Asher Bio, BioNTech, BMS, MSD, Novartis, and Sastra Cell Therapy, all paid to institute. The other authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe REPOSIT-study is supported by an unrestricted grant by Roche Medical B.V. The company has approved the design of the study and provided cobimetinib free of charge. The company has no role in collection, analysis, and interpretation of data or in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBvdH, LdW, AvdE, MS, RB and JH contributed to the conception and design of the study. Data collection, including patient-related activities was done by BvdH, AvdE, JH, EK, GH, MA, FdV, MB, AvdV and JWdG. Data analysis, statistics and interpretation of data was performed by BvdH, MS, ML, RB, LdW and WV. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors want to thank Ms. I. Eggink, Dr. R.H.T. Koornstra, Drs. A. Arens, Drs. M.G.G. Hobbelink, Prof. dr. L.F. de Geus-Oei, Dr. W.H.J. Kruit, Prof. dr. J.F. Verzijlbergen, Prof. dr. F.M. Mottaghy, Dr. S. Knollema, Dr. A.H. Brouwers and Prof. dr. O.S. Hoekstra for their contributions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFerlay J, Ervik M, Lam F, Colombet M, Mery L, Pi\u0026ntilde;eros M, et al. Global cancer Observatory: cancer today. Lyon, France: international agency for research on cancer. 2018.\u003c/li\u003e\n\u003cli\u003eJenkins RW, Fisher DE. Treatment of Advanced Melanoma in 2020 and Beyond. Journal of Investigative Dermatology. 2021;141:23-31. doi:https://doi.org/10.1016/j.jid.2020.03.943.\u003c/li\u003e\n\u003cli\u003eFlaherty KT, Puzanov I, Kim KB, Ribas A, McArthur GA, Sosman JA, et al. Inhibition of mutated, activated BRAF in metastatic melanoma. N Engl J Med. 2010;363:809-19. doi:10.1056/NEJMoa1002011.\u003c/li\u003e\n\u003cli\u003eWellbrock C, Hurlstone A. BRAF as therapeutic target in melanoma. Biochem Pharmacol. 2010;80:561-7. doi:10.1016/j.bcp.2010.03.019.\u003c/li\u003e\n\u003cli\u003eLong GV, Menzies AM, Nagrial AM, Haydu LE, Hamilton AL, Mann GJ, et al. 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Incorporation of whole-body metabolic tumor burden into current prognostic models for non-small cell lung cancer patients with spine metastasis. Spine J. 2024. doi:10.1016/j.spinee.2024.09.012.\u003c/li\u003e\n\u003cli\u003eTricarico P, Chardin D, Martin N, Contu S, Hugonnet F, Otto J, Humbert O. Total metabolic tumor volume on (18)F-FDG PET/CT is a game-changer for patients with metastatic lung cancer treated with immunotherapy. J Immunother Cancer. 2024;12. doi:10.1136/jitc-2023-007628.\u003c/li\u003e\n\u003cli\u003eMcArthur G, Callahan J, Ribas A, Gonzalez R, Pavlick A, Hamid O, et al. Metabolic tumor burden for prediction of overall survival following combined BRAF/MEK inhibition in patients with advanced BRAF mutant melanoma. Journal of Clinical Oncology. 2014;32:9006-. doi:10.1200/jco.2014.32.15_suppl.9006.\u003c/li\u003e\n\u003cli\u003eAnnovazzi A, Ferraresi V, Rea S, Russillo M, Renna D, Carpano S, Sciuto R. Prognostic value of total metabolic tumour volume and therapy-response assessment by [(18)F]FDG PET/CT in patients with metastatic melanoma treated with BRAF/MEK inhibitors. Eur Radiol. 2022;32:3398-407. doi:10.1007/s00330-021-08355-1.\u003c/li\u003e\n\u003cli\u003evan der Hiel B, Haanen J, Stokkel MPM, Peeper DS, Jimenez CR, Beijnen JH, et al. Vemurafenib plus cobimetinib in unresectable stage IIIc or stage IV melanoma: response monitoring and resistance prediction with positron emission tomography and tumor characteristics (REPOSIT): study protocol of a phase II, open-label, multicenter study. BMC Cancer. 2017;17:649. doi:10.1186/s12885-017-3626-5.\u003c/li\u003e\n\u003cli\u003eBalch 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. doi:10.1200/jco.2009.23.4799.\u003c/li\u003e\n\u003cli\u003eEisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45:228-47. doi:10.1016/j.ejca.2008.10.026.\u003c/li\u003e\n\u003cli\u003eBoellaard R, O\u0026rsquo;Doherty MJ, Weber WA, Mottaghy FM, Lonsdale MN, Stroobants SG, et al. FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0. European journal of nuclear medicine and molecular imaging. 2010;37:181-200.\u003c/li\u003e\n\u003cli\u003eAide N, Lasnon C, Veit-Haibach P, Sera T, Sattler B, Boellaard R. EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies. European Journal of Nuclear Medicine and Molecular Imaging. 2017;44:17-31. doi:10.1007/s00259-017-3740-2.\u003c/li\u003e\n\u003cli\u003eBoellaard 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. European Journal of Nuclear Medicine and Molecular Imaging. 2015;42:328-54. doi:10.1007/s00259-014-2961-x.\u003c/li\u003e\n\u003cli\u003eBoktor RR, Walker G, Stacey R, Gledhill S, Pitman AG. Reference range for intrapatient variability in blood-pool and liver SUV for 18F-FDG PET. J Nucl Med. 2013;54:677-82. doi:10.2967/jnumed.112.108530.\u003c/li\u003e\n\u003cli\u003eWahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: Evolving Considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50 Suppl 1:122S-50S. doi:10.2967/jnumed.108.057307.\u003c/li\u003e\n\u003cli\u003eMeignan M, Barrington S, Itti E, Gallamini A, Haioun C, Polliack A. Report on the 4th International Workshop on Positron Emission Tomography in Lymphoma held in Menton, France, 3-5 October 2012. Leuk Lymphoma. 2014;55:31-7. doi:10.3109/10428194.2013.802784.\u003c/li\u003e\n\u003cli\u003eBoellaard R, Hoekstra O, Lammertsma A. Software tools for standardized analysis of FDG whole body studies in multi-center trials. Soc Nuclear Med; 2008.\u003c/li\u003e\n\u003cli\u003evan Sluis J, de Heer EC, Boellaard M, Jalving M, Brouwers AH, Boellaard R. Clinically feasible semi-automatic workflows for measuring metabolically active tumour volume in metastatic melanoma. Eur J Nucl Med Mol Imaging. 2021;48:1498-510. doi:10.1007/s00259-020-05068-3.\u003c/li\u003e\n\u003cli\u003eHauschild A, Larkin J, Ribas A, Dreno B, Flaherty KT, Ascierto PA, et al. Modeled Prognostic Subgroups for Survival and Treatment Outcomes in BRAF V600-Mutated Metastatic Melanoma: Pooled Analysis of 4 Randomized Clinical Trials. JAMA Oncol. 2018;4:1382-8. doi:10.1001/jamaoncol.2018.2668.\u003c/li\u003e\n\u003cli\u003eRobert C, Grob JJ, Stroyakovskiy D, Karaszewska B, Hauschild A, Levchenko E, et al. Five-Year Outcomes with Dabrafenib plus Trametinib in Metastatic Melanoma. New England Journal of Medicine. 2019;381:626-36. doi:10.1056/NEJMoa1904059.\u003c/li\u003e\n\u003cli\u003eHeagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics. 2000;56:337-44. doi:10.1111/j.0006-341x.2000.00337.x.\u003c/li\u003e\n\u003cli\u003eHothorn T, Lausen B. On the exact distribution of maximally selected rank statistics. Computational Statistics \u0026amp; Data Analysis. 2003;43:121-37. doi:https://doi.org/10.1016/S0167-9473(02)00225-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Melanoma, BRAF mutation, progression-free survival, Positron Emission Tomography, metabolic tumor volume, total lesion glycolysis, standardized uptake value, targeted therapy","lastPublishedDoi":"10.21203/rs.3.rs-5941915/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5941915/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003e[\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT plays a crucial role in evaluating cancer patients and assessing treatment response, including in BRAF-mutated melanoma. Metabolic tumor volume (MTV) and total lesion glycolysis (TLG) have emerged as promising alternatives to standardized uptake value (SUV)-based measures for tumor assessment. This study evaluates the predictive value of SUVpeak, MTV, and TLG in predicting progression-free survival (PFS) in advanced BRAF-mutated melanoma treated with BRAF/MEK inhibitors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSeventy-five patients with metastatic melanoma were enrolled in a multi-center trial and treated with vemurafenib/cobimetinib. [\u003csup\u003e18\u003c/sup\u003eF]FDG-PET/CT scans were performed at baseline, week-2, and week-7. Imaging analysis included SUVpeak, MTV, and TLG of summed metastases, as well as percentage changes over time (∆).\u003cstrong\u003e \u003c/strong\u003eBaseline median PET-parameters were SUVpeak 12.59 (range 3.13-50.59), MTV 159mL (range 0-1897 mL), and TLG 1013 (range 1-13162). MTV had the highest predictive performance for risk of progression (AUC\u003csub\u003eT=6 months\u003c/sub\u003e=0.714). Patients with TLG below the median had significantly prolonged PFS (15.4 vs. 8.5 months, P=0.024). MTV above optimal cutoff (45.3 mL) was associated with an increased risk of progression/death, even after adjusting for LDH, ECOG status, and metastatic sites (HR=2.97, 95% CI 1.17-7.52, P=0.022).\u003c/p\u003e\n\u003cp\u003eAt week-7, ∆SUVpeak% was predictive (median ∆SUVpeak%: 64); PFS was 5.0 months (95% CI: 4.3-NA) for patients below the median versus 14.7 months (95% CI: 9.2-20.2) for those above or with non-quantifiable scans (P=0.0002). Median ∆MTV was 95.5% at week-2 and 97.6% at week-7, with significant PFS differences at both time points (week-2: P=0.020, week-7: P\u0026lt;0.001). TLG mirrored MTV. Patients with MTV increases at week-7 after an initial response at week-2 had a median PFS of 5.3 vs. 12.6 months for those with stable or declining MTV (P=0.0023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study supports the use of MTV and TLG as robust predictive markers for PFS in advanced melanoma treated with BRAF/MEK-inhibitors. Monitoring early PET parameters changes can provide valuable insights into therapeutic response and disease progression.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Trial Registration\u003c/p\u003e\n\u003cp\u003eClinicaltrials.gov identifier: NCT02414750. Registered 10 April 2015, retrospectively registered.\u003c/p\u003e","manuscriptTitle":"Metabolic Parameters on Baseline and Early [18F]FDG PET/CT as a Predictive Biomarker for Resistance to BRAF/MEK Inhibition in Advanced Cutaneous BRAFV600-mutated Melanoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-18 14:16:01","doi":"10.21203/rs.3.rs-5941915/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2025-04-10T10:01:23+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-02-14T14:44:40+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-14T10:42:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-13T09:47:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"EJNMMI Research","date":"2025-02-01T08:00:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ea631b40-673d-4952-b016-b3bde630c08d","owner":[],"postedDate":"February 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T15:59:09+00:00","versionOfRecord":{"articleIdentity":"rs-5941915","link":"https://doi.org/10.1186/s13550-025-01259-x","journal":{"identity":"ejnmmi-research","isVorOnly":false,"title":"EJNMMI Research"},"publishedOn":"2025-05-28 15:57:04","publishedOnDateReadable":"May 28th, 2025"},"versionCreatedAt":"2025-02-18 14:16:01","video":"","vorDoi":"10.1186/s13550-025-01259-x","vorDoiUrl":"https://doi.org/10.1186/s13550-025-01259-x","workflowStages":[]},"version":"v1","identity":"rs-5941915","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5941915","identity":"rs-5941915","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}