Predictive value of 18F-FDG PET/CT multi-metabolic parameters and tumor metabolic heterogeneity in the prognosis of gastric cancer

doi:10.21203/rs.3.rs-3117133/v1

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Research Article

Predictive value of ¹⁸F-FDG PET/CT multi-metabolic parameters and tumor metabolic heterogeneity in the prognosis of gastric cancer

https://doi.org/10.21203/rs.3.rs-3117133/v1

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published 12 Aug, 2023

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Objective: We aimed to investigate the predictive value of pre-treatment ¹⁸F-FDG PET/CT multi-metabolic parameters and tumor metabolic heterogeneity for gastric cancer prognosis.

Methods: Seventy-one patients with gastric cancer were included. All patients underwent ¹⁸F-FDG PET/CT whole-body scans prior to treatment and had pathologically confirmed gastric adenocarcinomas. Each metabolic parameter, including SUVmax, SUVmean, MTV, and TLG, were collected from the primary lesions of gastric cancer in all patients, and the slope of linear regression between the MTV corresponding to different SUVmax thresholds (40% × SUVmax, 80% × SUVmax) of the primary lesions was calculated. The absolute value of the slope was regarded as the metabolic heterogeneity of the primary lesions, expressed as the heterogeneity index HI-1, and the coefficient of variance of the SUVmean of the primary lesions was regarded as HI-2. Patient prognosis was assessed by PFS and OS, and a nomogram of the prognostic prediction model was constructed, after which the clinical utility of the model was assessed using DCA.

Results: A total of 71 patients with gastric cancer, including 57 (80.3%) males and 14 (19.7%) females, had a mean age of 61 ± 10 years; disease progression occurred in 27 (38.0%) patients and death occurred in 24 (33.8%) patients. Multivariate Cox regression analysis showed that HI-1 alone was a common independent risk factor for PFS (HR: 1.183; 95% CI: 1.010–1.387, P < 0.05) and OS (HR: 1.214; 95% CI: 1.016–1.450, P < 0.05) in patients with gastric cancer. A nomogram created based on the results of Cox regression analysis increased the net clinical benefit for patients. Considering disease progression as a positive event, patients were divided into low-, intermediate-, and high-risk groups, and Kaplan–Meier survival analysis showed that there were significant differences in PFS among the three groups. When death was considered a positive event and patients were included in the low- and high-risk groups, there were significant differences in OS between the two groups.

Conclusion: The heterogeneity index HI-1 of primary gastric cancer lesions is an independent risk factor for patient prognosis. A nomogram of prognostic prediction models constructed for each independent factor can increase the net clinical benefit and stratify the risk level of patients, providing a reference for guiding individualized patient treatment.

Gastric cancer

18F-FDG PET/CT

metabolic parameters

metabolic heterogeneity

prognosis

Gastric carcinoma is one of the most common gastrointestinal malignancies worldwide, and its pathogenesis is complex, multifactorial, multigenic, multistage, and has mutual promotion. Owing to the lack of obvious clinical symptoms and typical signs at the early stages of development, the early diagnosis rate is low, and mortality rate is high because patients do not seek a timely diagnosis. According to the International Agency for Research on Cancer, there will be approximately 1,089 million new cases of gastric cancer worldwide in 2020, ranking fifth in the incidence rate of malignant tumors; and approximately 769,000 deaths are due to gastric cancer, ranking fourth in the mortality rate of malignant tumors. Additionally, 43.9% of new gastric cancer cases and 48.6% of gastric cancer deaths worldwide occur in China[1]. Patients who undergo radical gastric cancer surgery, even if the surgery achieves R0 resection, still experience recurrence within 2 years after surgery, and 70% of patients die within 5 years[2]. Therefore, accurate prognostic assessment of patients with gastric cancer is crucial to assist in clinical risk stratification to achieve planned and rational application of surgery, chemotherapy, radiotherapy, and biological targeting to achieve radical or maximum tumor control, prolong patient survival, and improve quality of life. Tumor-node-metastasis (TNM) staging is widely used in clinical practice as an important reference for treatment decision selection and prognosis determination. However, even when similar treatment regimens are used in patients with the same staging, the treatment outcomes and prognosis still differ greatly[3], indicating that the TNM staging system remains flawed.

Deoxy-2-[fluorine-18]-fluoro-D-glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) reveals the biological behavior of tumors at the cellular metabolism level and is effective in staging, detecting recurrence, assessing treatment response, and predicting prognosis in patients with gastric cancer[4–8]. The maximum standardized uptake value (SUVmax) is a metabolic parameter routinely used for quantitative analysis in ¹⁸F-FDG PET/CT imaging and has been proposed as a predictor of tumor prognosis[9, 10]. Additionally, the volumetric parameters of ¹⁸F-FDG PET/CT, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) can describe tumor load and have good performance in predicting clinical outcomes in patients with gastric cancer[6, 11, 12]. Nevertheless, none of these parameters reflect the metabolic heterogeneity within the tumor.

Most solid tumors may exhibit a high degree of heterogeneity due to genetic alterations, epigenetic events, interactions between tumor cells and the microenvironment, and interactions between different tumor cell clones/populations within the tumor[13]. These interactions combine macroscopic and microscopic features and molecular changes, including not only inter-tumor heterogeneity but also intra-tumor heterogeneity. which has been shown to be strongly associated with treatment failure and poor patient prognosis[14–17]. Several heterogeneity indicators of ¹⁸F-FDG PET/CT, such as the coefficient of variance[18] and linear regression slope[19, 20], have been reported in previous studies and have a prognostic value for a variety of cancers, including pancreatic, breast, cervical, and oral cancers[19, 21–23]. However, the prognostic value of these heterogeneous indicators for gastric cancer has not been well-studied. We aimed to investigate the prognostic value of heterogeneity parameters of ¹⁸F-FDG PET/CT versus conventional prognostic parameters in patients with gastric cancer.

1. Patient selection

A total of 71 patients with gastric cancer, who underwent radical gastric cancer surgery and adjuvant chemotherapy, were included. We included patients who underwent ¹⁸F-FDG PET/CT and had positive ¹⁸F-FDG uptake in the primary lesion of gastric cancer (primary lesion SUVmax ≥ liver SUVmax); who underwent radical gastric cancer surgery within one month of ¹⁸F-FDG PET/CT, and postoperative pathology confirmed gastric adenocarcinoma; with no history of other malignant tumors; and with complete clinicopathological and follow-up data. We excluded any patients who underwent tumor-related interventions prior to ¹⁸F-FDG PET/CT, with poor-quality ¹⁸F-FDG PET/CT images, or with positive ¹⁸F-FDG drug uptake in the primary lesion.

2. ¹⁸FFDG PET/CT scanning

All patients were examined using a Discovery 690 PET/CT imaging system (GE Healthcare, WI, USA). ¹⁸F-FDG was produced using a medical cyclotron (Minitracer Qilin, GE Healthcare, WI) and synthesized using an automated synthesis module. ¹⁸F-FDG had a radiochemical purity of > 95%. Patients were instructed to fast and abstain from sugary drinks for at least 6 h before the scan to confirm that the peripheral blood glucose level was below 9.0 mmol/L before the injection of ¹⁸F-FDG and to record height and weight in detail. ¹⁸F-FDG was injected through the patient’s elbow vein at a dose of approximately 2.96–5.55 MBq/kg, followed by regular water and rest. PET/CT was performed with the patient in a supine position with calm breathing, ranging from the top of the head to the upper femur. The scan was performed using low-dose CT (120 kV, 50–220 mA) image acquisition with a layer thickness of 3.75 mm, followed by PET image acquisition with an average of 5–8 beds per patient depending on the height, 5–8 min per bed for the head and 2–3 min per bed for the rest of the body using the static TOF + 3D method with an image matrix of 256 × 256. CTAC iterative reconstruction was applied, and the reconstruction method was VUE Point FX (Filter Cutoff 1.0 mm, subsets 18, iterations 4).

3. Analysis of PET/CT images

The images were analyzed by two nuclear medicine physicians with more than 5 years of clinical diagnostic experience, and the region of interest (ROI) of the lesion was outlined independently according to ¹⁸F-FDG uptake. The relevant metabolic parameters, including SUVmax, SUVmean, MTV, and TLG, were automatically calculated by the workstation, and the tumor load three-dimensional (3D) ROI was 40% × SUVmax. When the two physicians disagreed with the image judgment results, agreement was reached through discussion; the mean value of the quantitative parameters measured by the two physicians was taken as the final result. The metabolic heterogeneity of gastric cancer was expressed as the heterogeneity index (HI) by calculating the slope of the linear regression between MTVs corresponding to different SUVmax thresholds (40% × SUVmax and 80% × SUVmax) of the primary lesions of gastric cancer, and the absolute value of the slope was considered as the HI-1 of the primary lesions. In addition, the ratio of the standard deviation (SD) of SUVmean to SUVmean, namely the variance coefficient, was regarded as HI-2.

4. Patient follow-up and clinical prognosis

Patients were reviewed every 3–6 months for the first 2 years after surgery and every 6–12 months for the next 3–5 years, with the choice of whole-body PET/CT, abdominal enhancement CT, or gastroscopy. Patients were followed-up by consulting the hospital information system and by telephone; postoperative recurrence, distant metastasis, or patient death were defined as the endpoint events for follow-up, and patient prognosis was assessed by progression-free survival (PFS) and overall survival (OS). PFS was defined as the time between complete tumor resection and tumor recurrence in situ, distant metastasis, patient death from any cause, or cutoff of follow-up. OS was defined as the time between complete tumor resection and patient death from any cause or cutoff of follow-up. Tumor recurrence and distant metastasis indicated by imaging and/or pathology were considered as disease progression.

5. Statistical analysis

All measures were statistically described by mean ± standard deviation (x̄ ± SD) or median (quartiles) [M (Qr)] according to their normal distribution, and group comparisons were made by Student’s t-test or Mann–Whitney U test; count data were statistically described by number of cases (%) [N (%)], and differences between groups were made by Chi-square test or Fisher’s exact test. The hazard ratios (HR) of each risk factor were calculated using single-factor and multi-factor Cox regression analysis, and a prognostic prediction model (nomogram) based on Cox regression analysis was established, followed by calculation of the area under the curve (AUC) to evaluate the predictive efficacy of the model. Clinical utility was then analyzed by decision curve analysis (DCA); the receiver operating characteristic curve (ROC) was used to determine the optimal threshold for each independent risk factor, and Kaplan–Meier survival analysis and log-rank test were applied to compare the PFS and OS of different groups of patients. SPSS 25.0 software and R 4.1.0 software were used for the analysis; P < 0.05 indicated a statistically significant difference.

1. Patient information

As of January 1, 2022 (end time of follow-up), 71 patients with gastric cancer were included (Table 1).

2. Prognosis by clinicopathological features and PET/CT metabolic parameters

2.1 Overall prognosis

The mean follow-up time for the 71 patients was 28 months, with a total follow-up time of 5–69 months. During the follow-up period, disease progression occurred in 27 (38.0%) patients, including 23 (32.4%) males and four (5.6%) females, and 24 (33.8%) patients died, including 18 (25.4%) males and six (8.5%) females. Mean PFS was 25 months, and the cumulative PFS rates at 1, 2, and 3 years were 76.1%, 67.6%, and 64.8%, respectively. The mean OS was 28 months, and the cumulative OS rates at 1, 2, and 3 years were 83.1%, 73.2%, and 69.0%, respectively.

2.2 Intergroup comparison in patients with different prognosis

Among the clinicopathological factors, the minimum tumor diameter, carcinoembryonic antigen (CEA), and Carbohydrate antigen 12-5(CA12-5) were 6.20 (3.15, 7.40) cm, 5.78 (2.60, 8.58) ng/ml, and 31.73 (21.10, 39.81) U/ml, respectively, which were significantly higher than those in patients without disease progression, at 2.05 (1.18, 3.28) cm, and 2.13 (1.03, 3.57) ng/ml, and 9.87 (6.69, 13.83) U/ml, respectively, with statistically significant differences (all P < 0.05). In PET/CT metabolic parameters and heterogeneity index, MTV, TLG, and HI-1 were 28.69 (14.27, 41.71) cm³, 156.13 (49.28, 253.02) g, and 6.47 (4.62, 10.00), respectively, in patients with disease progression, which were significantly higher than those without disease progression, 8.15 (5.55, 17.26 ) cm³, 34.26 (15.94, 76.45) g, and 2.53 (1.50, 3.65), respectively, with statistically significant differences (all P < 0.05). No other factors significantly correlated with patient prognosis (Table 2).

2.3 Cox regression analysis for PFS

Tumor minimum diameter, CA12-5, MTV, TLG, and HI-1 were significantly associated with PFS in gastric cancer patients (all P < 0.05). Multivariate Cox regression analysis showed that tumor minimum diameter (HR: 1.192, 95% CI: 1.025–1.387, P = 0.023), CA12-5 (HR: 1.028, 95% CI: 1.009–1.048, P = 0.003), and HI-1 (HR: 1.183, 95% CI: 1.010–1.387, P = 0.037) were independent risk factors for PFS in patients with gastric cancer (Table 3).

2.4 Cox regression analysis for OS

Univariate Cox regression analysis showed that MTV, TLG, and HI-1 were significantly associated with OS (all P < 0.05). Multivariate Cox regression analysis showed that HI-1 (HR: 1.214, 95% CI: 1.016–1.450, P = 0.032) was an independent risk factor for OS in patients with gastric cancer (Table 4).

3. Construction of the nomogram

3.1 PFS

The nomogram to predict PFS was constructed based on the results of the Cox multivariate regression analysis (Fig. 1). Probabilistic calibration curves for 1-, 2-, and 3-year PFS showed good agreement between the nomogram-predicted PFS and the actual results (Fig. 2). The AUCs at 1, 2, and 3 years were 0.845 (95% CI: 0.749–0.942), 0.869 (95% CI: 0.768–0.970), and 0.894 (95% CI: 0.785–1.000), respectively (Fig. 3). The nomogram was constructed using DCA to allow for a greater net clinical benefit to the patients (Fig. 4).

3.2 OS

The nomogram to predict OS was constructed based on the results of the Cox multivariate regression analysis (Fig. 5). Probabilistic calibration curves for 1-, 2-, and 3-year OS showed good agreement between the nomogram-predicted OS and the actual results (Fig. 6). The AUCs at 1, 2, and 3 years were 0.731 (95% CI: 0.530–0.931), 0.738 (95% CI: 0.583–0.892), and 0.739 (95% CI: 0.585–0.893), respectively (Fig. 7). The nomogram was constructed using DCA to allow for a greater net clinical benefit to the patients (Fig. 8).

4. Risk stratification and Kaplan–Meier survival analysis

With disease progression as a positive event, ROC curves were plotted for independent risk factors (tumor minimum diameter, CA12-5, and HI-1) for PFS in gastric cancer patients, and the optimal thresholds were calculated by the Youden index. The optimal thresholds for the three factors were 3.60 cm, 17.15 U/ml, and 3.80, respectively, and the patients were divided into low, medium, and high-risk groups, accordingly. Those with a tumor minimum diameter < 3.60 cm, CA12-5 < 17.15 U/ml, and HI-1 < 3.80 were entered into the low-risk group, totaling 23 (32.4%) cases; those with the tumor minimum diameter ≥ 3.60 cm, CA12-5 ≥ 17.15 U/ml, and HI-1 ≥ 3.80 were entered into the high-risk group, totaling 15 (21.1%) cases; the remaining patients were entered into the intermediate-risk group, totaling 33 (46.5%) cases. Kaplan–Meier survival analysis was used to compare the PFS of patients among the three groups, and the results showed a statistically significant difference (P < 0.05). After Bonferroni correction of P values for two-way comparisons between groups, the results showed statistically significant differences between all groups (all P < 0.017) (Fig. 9).

With death as a positive event, ROC curves were plotted for independent risk factors (HI-1) for OS in patients with gastric cancer, and the results showed that the optimal threshold for HI-1 was 6.12. Patients were divided into low-risk (HI-1 < 6.12) and high-risk (HI-1 ≥ 6.12) groups according to the threshold. The results showed that the number of patients in the low- and high-risk groups was 52 (73.2%) and 19 (26.8%), respectively. Kaplan–Meier survival analysis showed a statistically significant difference in OS between patients in the low- and high-risk groups (P < 0.05) (Fig. 10).

As medical research continues to advance, an increasing number of methods for the prognostic assessment of tumors are being applied clinically. The advent of imaging biomarkers (IBs) has provided clinicians with a new method to assess the prognosis of patients with tumors. In 2010, the European Society of Radiology published a white paper on IBs, highlighting their importance as noninvasive quantitative tools with different applications in oncology[24]. The Radiological Society of North America encourages the use of IBs in clinical trials[25].

Biological heterogeneity is present in most tumors[26–28], including heterogeneity in genomic subtypes, variation in the expression of growth factors, pro- and anti-angiogenic factors, and variation in the tumor microenvironment[29–34]. As tumor heterogeneity has been studied in depth, it has been found to be a major challenge to personalized treatment, often leading to treatment failure, which has increased focus thereof. ¹⁸F-FDG PET/CT integrates morphological and functional imaging. As the most mature molecular imaging technique for clinical applications, it has unique advantages in reflecting tumor heterogeneity. Some encouraging results on PET/CT tumor metabolic heterogeneity suggest great potential for the prognostic assessment of patients with malignant tumors[35, 36]. In this study, the prognosis of patients with gastric cancer was assessed using HI-1 in combination with ¹⁸F-FDG PET/CT imaging features, and the results showed that it was a common independent risk factor for PFS and OS with better efficacy than other metabolic parameters and clinicopathological indicators, which was similar to the results of Liu et al. [37]. However, unlike Liu et al., the present study generated a slightly different HI-1 method that used the absolute SUV threshold method (2.5, 3.0, and 3.5) to obtain the linear regression slopes for patients with gastric cancer. However, the gastric wall itself is characterized by physiological uptake as well as inflammatory uptake in the adjacent gastric wall due to tumors, so artificially setting 2.5 as the SUV cutoff may overestimate the MTV and thus lead to heterogeneity bias. Therefore, to exclude the interference of the metabolism of the tumor itself due to the physiological and inflammatory uptake of the gastric wall and the error of the metabolic range of the tumor due to the small volume and significant partial volume effect, we used 40% × SUVmax and 80% × SUVmax as thresholds to calculate HI-1, and obtained good results. In contrast to HI-1, although some studies have found the prognostic value of HI-2 in cervical[23] and ovarian[38] cancers, our results showed that HI-2 was not significantly associated with patients with gastric cancer and did not serve as an independent risk factor for patient prognosis. This indicates that HI-2 is inconsistent in assessing the prognosis of patients with different types of tumors, whereas HI-1 continues to perform well in the prognosis of patients with other tumors, such as sinonasal[36] and oral cancers[22]. The reason for this is probably related to the fact that the generation of HI-2 is influenced by more factors. The SUVmean changes when the number of included samples changes, and is dependent upon the SUV threshold, and different SUV can also produce different SUVmeans, which can lead to changes in HI-2 and eventually bias the results.

SUVmax, the most widely used semiquantitative parameter of ¹⁸F-FDG PET/CT in clinical applications, has been studied intensively for its prognostic value. However, the prognostic assessment in patients with gastric cancer in this study was negative, which was considered related to the properties of SUVmax. The measurement of SUVmax is affected by various factors such as patient body mass, blood glucose level, injection radioactivity, image reconstruction parameters, resolution, and ROI definition parameters[39]. Additionally, SUVmax only represents the single-monomer metabolic level of the lesion (the highest site of tumor metabolism), which has some limitations in reflecting the overall tumor load and heterogeneity; therefore, SUVmax may not be comprehensive in assessing the prognosis of patients with gastric cancer with significant heterogeneity. In recent years, volumetric parameters, including MTV and TLG, have performed well in predicting the clinical prognosis of patients with gastric cancer[11, 12]. However, these volumetric parameters are not free from volumetric effects. Owing to the low spatial resolution of PET images due to partial volume effects, the percentage threshold method is more dependent on the SUVmax of the tumor when measuring MTV and TLG, which leads to large differences between tumor lesions with high ¹⁸F-FDG uptake; whereas, in tumors with low ¹⁸F-FDG uptake, the physiological uptake of the adjacent gastric wall affects the accurate assessment of gastric cancer margins and thus reduces its own predictive efficacy.

Effective treatment depends on accurate pre-treatment evaluation and early prognostic judgment. With continuous research and a comprehensive understanding of the occurrence and development rules and biological characteristics of gastric cancer, a single diagnosis, treatment model, and prognosis evaluation method can no longer meet the needs of patients; the current development trend is to adopt a diversified and all-round survival prediction and evaluation system to guide the individualized treatment of gastric cancer patients. A nomogram is a quantitative analytical graph that represents the functional relationship between multiple variables using a cluster of disjoint lines in planar coordinates based on the results of a multifactorial regression analysis. It is a statistical model that can visually predict the probability of occurrence of specific clinical events (e.g., probability of disease, risk of recurrence, and prognosis), and has been used as an accurate visualization tool in several aspects of clinical practice[40–42]. In the present study, a nomogram was constructed by combining IBs and clinicopathological indicators, allowing for a more specific presentation of patient prognostic risk. The prediction model of PFS showed that, when the total score was > 108, the probability of progression-free survival at 1 year was only 50%, and the probability of progression-free survival at 2 and 3 years was even lower than 30%. This suggests that this patient had a higher probability of disease progression after conventional treatment and should be treated differently or the frequency of post-treatment follow-up should be increased. Similarly, the prediction model of OS showed that, when the total score was > 48, the probability of survival at 1 year was only 50%, and the probability of survival at 2 and 3 years was lower than 30%, suggesting that interventions for this patient should be strengthened. Subsequent validation of the efficacy and clinical utility of the model suggested that the PFS and OS prediction models for gastric cancer patients developed in this study could provide a better net benefit to the clinic and a new idea for prognosis prediction in gastric cancer patients.

Our study had some limitations. First, this was a single-center, small-sample, retrospective study, and only the prognosis of patients with gastric adenocarcinoma was studied. Second, cases with negative ¹⁸F-FDG uptake were artificially excluded because of the need to measure various metabolic parameters on ¹⁸F-FDG PET/CT. Third, the baseline information of patients with gastric cancer was used to predict the prognosis of the disease, and a dynamic assessment of the efficacy and metabolic changes of patients before and after treatment was lacking. Finally, the differences in adjuvant chemotherapy may have confounded the prognosis.

Our results showed that the heterogeneity index HI-1 of primary gastric cancer lesions is an independent risk factor for patient prognosis. Our nomogram of prognostic prediction models constructed for each independent factor can increase the net clinical benefit and stratify the risk level of patients, providing a reference to guide individualized patient treatment.

Conflicts of Interest/Competing Interests: The authors declare that they have no conflicts of interest.

Ethics approval and consent to participate: This retrospective study was approved by our institutional review board. Informed written consent was waived given the retrospective nature of the work.

Availability of Data and Material: All data generated or analysed during this study are included in this published article.

Acknowledgement: We would like to thank Editage (www.editage.cn) for English language editing.

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Tables 1 to 4 are available in the Supplementary Files section.

No competing interests reported.

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published 12 Aug, 2023

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Predictive value of ¹⁸F-FDG PET/CT multi-metabolic parameters and tumor metabolic heterogeneity in the prognosis of gastric cancer

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Abstract

Figures

Introduction

Materials and methods

1. Patient selection

2. ¹⁸FFDG PET/CT scanning

3. Analysis of PET/CT images

4. Patient follow-up and clinical prognosis

5. Statistical analysis

Results

Discussion

Conclusions

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1

Predictive value of 18F-FDG PET/CT multi-metabolic parameters and tumor metabolic heterogeneity in the prognosis of gastric cancer

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials and methods

1. Patient selection

2. 18FFDG PET/CT scanning

3. Analysis of PET/CT images

4. Patient follow-up and clinical prognosis

5. Statistical analysis

Results

Discussion

Conclusions

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1

Predictive value of ¹⁸F-FDG PET/CT multi-metabolic parameters and tumor metabolic heterogeneity in the prognosis of gastric cancer

2. ¹⁸FFDG PET/CT scanning