The role of baseline 18 F-FDG PET/CT for survival prognosis in NSCLC patients undergoing immunotherapy: a systematic review and meta-analysis

Abstract

Background:

The value of pretreatment baseline ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET)/computed tomography (CT) as a prognostic factor for survival of patients with non-small-cell lung cancer (NSCLC) receiving immunotherapy remained uncertain.

Objectives:

To investigate the prognostic ability of baseline ¹⁸F-FDG PET/CT in patients with NSCLC receiving immunotherapy.

Design:

A systematic review and meta-analysis.

Data sources and methods:

We searched the PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases until May 7, 2024, and extracted data related to patient characteristics, semiquantitative parameters of ¹⁸F-FDG PET/CT, and survival. We pooled hazard ratios (HRs) to evaluate the prognostic value of the maximum standardized uptake value (SUV_max), mean standardized uptake value (SUV_mean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for overall survival (OS) and progression-free survival (PFS).

Results:

A total of 22 studies (1363 patients, average age range 30–88 years) were included. Baseline ¹⁸F-FDG PET/CT-derived MTV was significantly associated with both OS (HR: 1.124, 95% confidence interval (CI) 1.058–1.195, I² = 81.70%) and PFS (HR: 1.069, 95% CI: 1.016–1.124, I² = 71.80%). Other baseline ¹⁸F-FDG PET/CT-derived parameters, including SUV_max (OS: HR: 0.930, 95% CI: 0.718–1.230; PFS: HR: 0.979, 95% CI: 0.759–1.262), SUV_mean (OS: HR: 0.801, 95% CI: 0.549–1.170; PFS: HR: 0.688, 95% CI: 0.464–1.020), and TLG (OS: HR: 0.999, 95% CI: 0.980–1.018; PFS: HR: 0.995, 95% CI: 0.980–1.010), were not associated with survival. Sensitivity analyses by removing one study at a time did not significantly alter the association between MTV and PFS or between MTV and OS. There was no evidence of publication bias.

Conclusion:

Pretreatment baseline ¹⁸F-FDG PET/CT-derived MTV might be a prognostic biomarker in NSCLC patients receiving immunotherapy. Further studies are needed to support routine use.

Plain language summary

Using PET/CT scans to predict survival in lung cancer patients receiving immunotherapy: a study review

Aims and Purpose of the Research We wanted to know if a type of scan called ¹⁸F-FDG PET/CT can help predict how long people with a type of lung cancer (NSCLC) will live after treatment with immunotherapy. Background of the Research This research matters because NSCLC is a common and serious type of lung cancer. Knowing how long patients might live after treatment can help doctors plan better care. Many people are affected by this disease, so finding good ways to predict survival can help a lot of patients. Methods and Research Design They reviewed and analyzed data from 22 different studies involving 1363 patients, with ages ranging from 30 to 88 years.We focused on certain measurements from the scans, like SUV_max, SUV_mean, MTV, and TLG. We checked if these measurements were linked to how long patients lived and how long they lived without their cancer getting worse. Results and Importance We found that one of these measurements, the Metabolic Tumor Volume (MTV), was linked to how long the patients lived and how long they stayed free of disease after treatment. Specifically, higher MTV was associated with poorer overall survival and progression-free survival. The other measurements (SUV_max, SUV_mean, and TLG) did not show a significant connection to patient survival. In conclusion, the MTV from PET/CT scans might help doctors predict the outcomes for lung cancer patients undergoing immunotherapy. However, more studies are needed to confirm these findings and to consider using this measurement regularly in clinical practice.

Keywords

immune checkpoint inhibitor immunotherapy non-small-cell lung cancer PD-1/PD-L1 response assessment

Introduction

Lung cancer remains the most common cancer and the leading cause of cancer death worldwide, with an estimated 26,100 new cases and 20,500 deaths in the United States in 2021.^1,2 Non-small-cell lung cancer (NSCLC) accounts for most lung cancer cases and often remains undiagnosed at advanced stages, for which treatment options are limited.^3,4 For years, traditional treatments, including surgery, chemotherapy, radiation therapy, and targeted treatment, have been the standard treatment methods for NSCLC, but their effectiveness has remained suboptimal.⁵

Immunotherapy has emerged as a standard treatment after chemotherapy in NSCLC patients in recent years. One promising class of immunotherapeutic agents is immune checkpoint inhibitors (ICIs), which harness the intrinsic immune response against tumor antigens by removing the brake on T-cell activation through antigen-presenting cells.⁶ Randomized controlled trials have suggested that treatments with ICIs are associated with significantly longer overall survival (OS) and progression-free survival (PFS).^7,8 This evidence led to the approval of three ICI drugs for patients with advanced NSCLC that target either PD-1 (pembrolizumab and nivolumab) or its ligand PD-L1 (atezolizumab). However, the overall response rates for ICIs have been reported to be as low as 14%–20% in NSCLC patients^9,10; thus, early identiﬁcation of patients who are likely to beneﬁt from ICI therapies is crucial to ensure high-quality practice. Several prognostic factors are associated with poor outcomes with immunotherapy, such as the LIPI index,¹¹ performance status,¹² and co-mutations like KRAS/STK11.¹³ Recent efforts have been made to find prognostic factors related to imaging data since they are convenient and noninvasive. However, an effective imaging-based prognostic approach has not been established.

An increasing number of studies have suggested the potential of imaging biomarkers, derived from computed tomography (CT) or ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET), to serve as objective and reproducible approaches to predict the optimal duration of immunotherapy¹⁴ and long-term benefit in various cancers.^14–16 The maximum standardized uptake value (SUV_max) is a commonly used FDG PET parameter in clinical practice and has been found to be associated with prognosis in various cancers,^17–19 but its prognostic value in NSCLC is controversial.^20,21 The prognostic value of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) is frequently reported in many cancers,^22–25 as well as in NSCLC patients undergoing surgery, chemotherapy, or radiotherapy.²⁶ However, some published studies hold the opposite opinion.²⁷ Moreover, the prognostic value of baseline ¹⁸F-FDG PET/CT in the subgroup of NSCLC patients receiving immunotherapy is still unclear.

Thus, we conducted this systematic review and meta-analysis to comprehensively evaluate the association between pretreatment baseline ¹⁸F-FDG PET/CT and survival in NSCLC patients who received immunotherapy.

Methods

Our meta-analysis was reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.²⁸ A PRISMA checklist is provided in Supplemental Table 1.

Eligibility criteria

We included studies that (1) evaluated the association between baseline ¹⁸F-FDG PET/CT-derived metabolic parameters (SUV_max, SUV_mean, MTV, and TLG) and OS or PFS, (2) enrolled NSCLC patients who received immunotherapy, and (3) were published in English. The exclusion criteria were as follows: (a) patients diagnosed with other malignant tumors; (b) studies in phantom or animal models; (c) case reports or small case series (⩽10 patients), reviews, poster presentations, and letters; (d) studies that did not report sufficient data to extract hazard ratios (HRs) for the association; and (e) studies that used duplicate or overlapping populations. Overlapping patient populations were defined as those from the same hospital (or, if not specified, the hospital of the corresponding author), the first author, and the study period. In cases where duplicate or overlapping patient populations were identified, data from the most informative or most recent publication were selected for inclusion in our meta-analysis. Moreover, additional research studies of possible interest were identified from the reference lists of the included articles and reviewed for eligibility.

Search strategy and study selection

A comprehensive search of the literature was conducted in PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials up to May 7, 2024. We used MESH (and EMTree) terms, as well as free texts, related to the concepts of ICI therapy, NSCLC, and ¹⁸F-FDG PET, to compose the search strategy (Supplemental Table S2).

Two reviewers (M.H. and Y.Z.) independently screened titles, abstracts, and full texts for eligibility. Any disagreement was resolved through discussion.

Quality assessment and data extraction

Two reviewers (M.H. and Y.Z.) independently assessed the quality of the included studies and extracted the data. Any disagreement was resolved through discussion. The quality of each article included in the study was evaluated via the Newcastle–Ottawa Scale (NOS),²⁹ a systematic review tool specifically developed for assessing the quality of nonrandomized controlled trials. Articles scoring 6 points or higher on the NOS were deemed high-quality and subsequently included in the meta-analysis. The following information was extracted from each study: author, country, sample size, study design, age, sex, cancer type, stage, ICI agent, baseline ¹⁸F-FDG PET/CT-derived parameters, HRs for OS or PFS, response assessment criteria, and other endpoints reported.

Statistical analyses

We used a random-effects model to pool HRs for the evaluation of the prognostic impact of baseline ¹⁸F-FDG PET parameters on both OS and PFS because heterogeneity from sampling schemes is notable. A pooled HR greater than 1 suggested a poorer prognosis for NSCLC patients who presented higher ¹⁸F-FDG PET-derived parameters. The heterogeneity among studies was evaluated by applying Cochran’s Q-test and Higgins I-squared statistics. A p-value < 0.1 or I² > 50% indicates significant statistical heterogeneity among studies. Sensitivity analysis was conducted by removing one study at a time. Publication bias was assessed using funnel plots, Egger’s, and Begg’s tests. p < 0.05 was used to determine statistical significance. Data from each study were analyzed via Stata version 15.0 (Stata Corp LP, College Station, TX, USA).

Results

Eligible studies and characteristics

The initial search identified 406 relevant records, and according to the titles and abstracts, 318 records were excluded. After full-text screening, 22 studies enrolling 1363 patients were included in this systematic review, and all studies were pooled in the meta-analysis. The PRISMA flowchart of literature selection is illustrated in Figure 1. Twenty-two studies reported OS, and 19 reported PFS. We included both parameters derived from primary lesions^20,30–35 and metastatic lesions^27,36–39 in the analysis. Most studies were retrospectively designed (17, 77%), focused on the East Asian population (18, 82%), enrolled patients solely in advanced stages (18, 82%), and collected metabolic parameters before treatment (22, 100%). Detailed information on the basic characteristics of the patients is shown in Table 1.

Figure 1.

Flow chart of selection studies and specific reasons for exclusion.

Table 1.

The basic characteristics of the included studies.

Author	Year	Country	Sample size	Study design	Age	Gender	Cancer type	Stage	Treatment	Detection time	Response assessment criteria		Parameters	Endpoint	Follow-up (months)
Author	Year	Country	Sample size	Study design	Age	Gender	Cancer type	Stage	Treatment	Detection time	Category	Details	Parameters	Endpoint	Follow-up (months)
Kaira	2017	Japan	24	Prospective study	66 (47–82)	18/6	Recurrent advanced NSCLC	III/IV/Ope rec	Nivolumab	¹⁸F-FDG-PET/CT × 2: baseline and after 1 month nivolumab therapy	PET semi-quantitative modified (CT), conventional (PET)	RECISTv1.1, PERCIST	SUV_max, MTV, and TLG	OS and PFS	10.5 (3.6–16.7)
Takada	2019	Japan	89	Single-institution retrospective study	67 (36–88)	75/14	Metastatic NSCLC	IIIB or IV	Pembrolizumab/nivolumab	¹⁸F-FDG-PET/CT × 1: baseline	PET semi-quantitative modified (CT)	RECIST1.1	SUV_max	OS, PFS, and tumor response	7.5 (0.2–31.0)
Seban 2	2019	France	80	Retrospective monocentric study	61.9 (34.2–84.8)	56/24	Advanced NSCLC	III/IV	Nivolumab/pembrolizumab/atezolizumab	¹⁸F-FDG-PET/CT × 1: baseline	PET semi-quantitative modified (CT)	RECISTv1.1	SUV_max, MTV, TMTV, TLG, NLR, and LDH levels	OS and PFS	11.6 (7.7–15.5)
Seban	2020	France	63	Retrospectively multicenter study	65	38/25	Advanced NSCLC	IV or IIIb	Pembrolizumab	¹⁸F-FDG-PET/CT × 1: baseline	PET semi-quantitative modified (CT)	RECISTv1.1	SUV_max, SUV_mean, MTV, and TLG	OS and PFS	13.4 (9.0–17.9)
Chardin	2020	France	75	Prospective study	64 (42–83)	59/16	Metastatic NSCLC	N/A	Pembrolizumab/nivolumab	¹⁸F-FDG-PET/CT × 1: baseline	PET semi-quantitative	N/A	SUV_peak, SUV_max, MTV, and TLG	OS and ETD	12.3 (6.1–19.3)
Hashimoto	2020	Japan	85	Retrospective study	Not reported	65/20	NSCLC	Advanced	Nivolumab or pembrolizumab	After previous treatment and before the initiation of anti-PD-1 antibody	PET semi-quantitative modified (CT)	RECISTv1.1	SUV_max, MTV, and TLG	PFS and OS	N/A
Seban 1	2020	France	63	Retrospective study, multicenter	65 (37–86)	38/25	NSCLC	IIIb/IV	Pembrolizumab	¹⁸F-FDG-PET/CT × 1: baseline before the initiation of anti-PD-1 antibody as a recurrent survey	Contrast-enhanced CT scan	RECIST1.1	TMTV and SUV_max	PFS, OS, DCR, and ORR	13.4 (9.0–17.9)
Valentinuzzi	2020	USA	30	Prospective study	65 (46–77)	15/15	NSCLC	IV	Pembrolizumab	¹⁸F-FDG PET/CT at baseline, months 1 and 4 after treatment	PET semi-quantitative modified (CT)	iRECIST	SUV_max	OS	21.4
Yamaguchi	2020	Japan	48	Retrospective study	69 (47–86)	39/9	NSCLC	Advanced	Pembrolizumab	¹⁸F-FDG-PET/CT × 1: baseline	PET semi-quantitative	RECIST1.1	MTV, TLG, and SUV_max	PFS and OS	11.5 (1–29.5)
Castello	2021	Italy	50	Prospective study	73	34/16	NSCLC	Advanced	Nivolumab, pembrolizumab, or atezolizumab	¹⁸F-FDG PET/CT at baseline, 6–8 weeks after treatment	PET semi-quantitative	iRECIST and EORTC	MTV, TLG, and SUV_max	PFS and OS	12.4 (9.7–15.2)
Lang	2021	Austria	85	Retrospective study	64 (38–81)	56/10	NSCLC	Advanced	Pembrolizumab plus carboplatin/pemetrexed or pembrolizumab plus carboplatin/paclitaxel	¹⁸F-FDG-PET/CT × 1: baseline	PET semi-quantitative	RECIST	MTV and BLR	PFS and OS	12 (10–14)
Vekens	2021	Belgium	30	Retrospective study	67 (41.0–92.0)	17/13	NSCLC	IV	Pembrolizumab	¹⁸F-FDG-PET/CT × 1: baseline	PET semi-quantitative modified (CT)	RECIST 1.1	TMTV and TLG	PFS and OS	20 (4.2–37.6)
Eude	2021	France	65	Retrospective study	64.1 ± 10.5	43/22	NSCLC	III/IV	Pembrolizumab	¹⁸F-FDG-PET/CT × 1: baseline			MTV, NTV, and TTV	OS	12
Kim	2022	Korea	52	Retrospective study	63 (33–84)	41/11	NSCLC	Advanced	Pembrolizumab combined with platinum-based chemotherapy	¹⁸F-FDG-PET/CT × 1: baseline	PET semi-quantitative modified (CT)	RECIST 1.1	SUV_max, MTV, and TLG	PFS and OS	16.7 (15.7–17.7)
Kudura	2022	Germany	50	Retrospective study	72.0 ± 9.5	64/61	NSCLC	I–IV	First line or second line	¹⁸F-FDG-PET/CT × 1: baseline	PET semi-quantitative	iRECIST	SUV_max, SUV_mean, MTV, and TLG	PFS and OS	18.93 ± 6.98
Silva	2022	Brazil	98	Retrospective study	67.5 (30–90)	59/39	NSCLC	III–IV	First line	¹⁸F-FDG-PET/CT × 1: baseline; 3 months before ICIs initiation	PET semi-quantitative modified (CT)	RECIST 1.1	SUV_max, wMTV, and wTLG	PFS and OS	Not reported
Andraos	2022	USA	124	Retrospective study	67 (38–88)	68/56	NSCLC	Advanced	First line	¹⁸F-FDG-PET/CT × 1: baseline; 3 months before ICIs initiation	PET semi-quantitative modified (CT)	N/A	MTV	PFS and OS	17
Rizzo	2023	UK	43	Retrospective study	67 (61–72)	25/18	NSCLC	IV	First-line ICI (i.e., pembrolizumab)	¹⁸F-FDG-PET/CT × 1: baseline;	PET semi-quantitative modified (CT)	RECIST 1.1	MTV and TLG	PFS and OS	18.2 (14.5–21.8)
Grambozov	2023	Austria	48	Retrospective study	67 (57–83)	35/13	NSCLC	I–IV	First line	¹⁸F-FDG-PET/CT × 1: baseline	PET semi-quantitative modified (CT)	N/A	SULpeak, SUV_max, MTV, and TLG	PFS and OS	16.7 (0.6–68)
Feng Yawen	2023	China	34	Retrospective study	62.5	28/6	NSCLC	III–IV	Sintilimab, tislelizumab, and pembrolizumab	¹⁸F-FDG-PET/CT × 1: baseline	PET semi-quantitative modified (CT)	RECIST 1.1	TLG and MTV	PFS and OS	16.47 (3.36–24.97)
Castello 2	2020	Italy	35	Prospectively	77 (51–86)	23/12	NSCLC	Advanced	Nivolumab, pembrolizumab, nivolumab plus ipilimumab, or atezolizumab	¹⁸F-FDG-PET/CT × 1: baseline	PET semi-quantitative	EORTC	MTV and TLG	PFS and OS	13.2 (4.9–21.6)
Monaco	2021	Italy	92	Retrospective study, two centers	70 (61–75)	65/27	NSCLC	I–IV	Nivolumab, pembrolizumab, or atezolizumab	¹⁸F-FDG-PET/CT × 1: baseline; 2 months before treatment	PET semi-quantitative	RECIST 1.1 or PERCIST1.1	wMTV, wTLG, SUV_max, and SUV_mean	PFS and OS	3

CT, computed tomography; DCR, disease control rate; EORTC, European Organization for Research and Treatment of Cancer; ETD, early tumor decrease; ¹⁸F-FDG, ¹⁸F-fluorodeoxyglucose; ICI, immune checkpoint inhibitor; iRECIST, Immune Response Evaluation Criteria in Solid Tumors; LDH, lactate dehydrogenase; PD-1, programmed cell death 1; MTV, metabolic tumor volume; N/A, not applicable; NLR, neutrophil-to-lymphocyte ratio; BLR, bone marrow to liver ratio; NTV, necrotic tumour volume; TTV, total tumour volume; NSCLC, non-small-cell lung cancer; ORR, overall response rate; OS, overall survival; PET, positron emission tomography; PERCIST, PET Response Criteria in Solid Tumors; PFS, progression-free survival; RECIST, Response Evaluation Criteria in Solid Tumors; SULpeak, peak standardized uptake lean; SUV_max, maximum standardized uptake value; SUV_mean, mean standardized uptake value; SUV_peak, peak standardized uptake value; TLG, total lesion glycolysis; TMTV, total metabolic tumor volume; wMTV, whole-body metabolic tumor volume; wTLG, whole-body total lesion glycolysis.

Quality assessment of included studies

The included studies were of acceptable quality. All studies were deemed of high quality in terms of representativeness of the exposed cohort, selection of the nonexposed cohort, ascertainment of exposure, control for important factors, additional factors, and assessment of outcomes. Five (23%) studies did not have adequate follow-up of cohorts, 3 (14%) did not follow-up long enough for outcomes to occur, and 3 (14%) did not ensure that outcomes were not present at the start of the study. The details are shown in Table 2.

Table 2.

Non-RCT quality assessment.

Selection (0–4)					Comparability (0–2)		Outcome
Study	REC	SNEC	AE	DO	SC	AF	AO	FU	AFU	Total
Kaira	1	1	1	1	1	1	1	1	1	7
Takada	1	1	1		1	1	1		1	8
Seban	1	1	1	1	1	1	1	1	1	8
Seban	1	1	1	1	1	1	1	1	1	9
Chardin	1	1	1	1	1	1	1	1	1	9
Hashimoto	1	1	1	1	1	1	1			7
Seban	1	1	1	1	1	1	1	1	1	9
Valentinuzzi	1	1	1		1	1	1	1	1	8
Yamaguchi	1	1	1	1	1	1	1	1		8
Castello	1	1	1	1	1	1	1	1	1	9
Lang	1	1	1	1	1	1	1	1	1	9
Vekens	1	1	1	1	1	1	1	1		8
Eude	1	1	1	1	1	1	1	1		8
Kim	1	1	1	1	1	1	1	1		8
Kudura	1	1	1	1	1	1	1	1	1	9
Silva	1	1	1	1	1	1	1			7
Andraos	1	1	1		1	1	1	1	1	8
Rizzo	1	1	1	1	1	1	1	1	1	9
Grambozov	1	1	1	1	1	1	1	1	1	9
Feng Yawen	1	1	1	1	1	1	1	1	1	9
Castello	1	1	1	1	1	1	1	1	1	9
Monaco	1	1	1	1	1	1	1	1	1	9

RCT, randomized controlled trial; AE, ascertainment of exposure; AFU, adequacy of follow-up; AF, additional factors; AO, assessment of outcome; FU, length of follow-up; DO, outcome not present at the start of the study; REC, representativeness of exposed cohort; SNEC, selection of nonexposed cohort; SC, control for important factors.

Summary of outcomes

Associations of baseline ¹⁸F-FDG PET/CT-derived parameters with OS in NSCLC patients receiving immunotherapy (Table 3).

Table 3.

The pooled HR of SUV_max, SUV_mean, MTV, and TLG on PFS and OS.

Overall	N	HR	95% CI	p	I ²	p	Model
OS
SUV_max	13	0.930	0.718–1.203	0.579	53.7%	0.011	Random
SUV_mean	6	0.801	0.549–1.170	0.251	1.10%	0.409	Random
MTV	19	1.124	1.058–1.195	0.001	81.70%	0.001	Random
TLG	14	0.999	0.980–1.018	0.883	84.10%	0.001	Random
PFS
SUV_max	11	0.979	0.759–1.262	0.868	62.1%	0.003	Random
SUV_mean	5	0.688	0.464–1.020	0.062	26.60%	0.244	Random
MTV	17	1.069	1.016–1.124	0.010	71.80%	0.001	Random
TLG	13	0.995	0.980–1.010	0.543	87.10%	0.001	Random

CI, confidence interval; HR, hazard ratio; MTV, metabolic tumor volume; OS, overall survival; PFS, progression-free survival; SUV_max, maximum standardized uptake value; SUV_mean, mean standardized uptake value; TLG, total lesion glycolysis.

SUV_max and SUV_mean

Thirteen studies investigated the association between the baseline SUV_max and OS in NSCLC patients receiving immunotherapy. The pooled results revealed that the SUV_max was not significantly associated with OS (HR: 0.930; 95% confidence interval (CI): 0.718–1.230, p = 0.579; I² = 53.70%, Figure 2(a)). When removing one study at a time, the pooled HR ranged from 0.859 to 1.006, with the lower bound of the 95% CI ranging from 0.673 to 0.797 and the upper bound of 95% CI ranging from 1.097 to 1.140 (Supplemental Figure S1(A)).

Figure 2.

Forest plot of a meta-analysis of the prognostic role of SUV_max (a) and SUV_mean (b) on overall survival.

Six studies investigated the association between the baseline SUV_mean and OS and found no significant association between the SUV_mean and OS (HR: 0.801; 95% CI: 0.549–1.170, p = 0.251; I² = 1.10%, Figure 2(b)). When removing one study at a time, the pooled HR ranged from 0.706 to 0.884, with the lower bound of the 95% CI ranging from 0.456 to 0.594 and the upper bound of the 95% CI ranging from 1.094 to 1.324 (Supplemental Figure S1(B)).

MTV and TLG

In all, 19 studies reported an association between the baseline MTV and OS in NSCLC patients receiving immunotherapy. The pooled HR showed that patients with higher MTV had significantly poorer OS than those with lower MTV, despite high heterogeneity (HR: 1.124; 95% CI: 1.058–1.195, p = 0.001; I² = 81.70%, Figure 3(a)). When removing one study at a time (Supplemental Figure S2(A)), the pooled HR ranged from 1.105 to 1.296, with the lower bound of the 95% CI ranging from 1.060 to 1.153 and the upper bound of the 95% CI ranging from 1.170 to 1.456.

Figure 3.

Forest plot of a meta-analysis of the prognostic role of MTV (a) and TLG (b) on overall survival.

In all, 14 studies investigated the association between baseline TLG and OS and reported no association between TLG and OS (HR: 0.999; 95% CI: 0.980–1.018, p = 0.883; I² = 84.10%, Figure 3(b)). When removing one study at a time, the pooled HR ranged from 0.997 to 1.004, with the lower bound of the 95% CI ranging from 0.967 to 0.981 and the upper bound of the 95% CI ranging from 1.042 to 1.050 (Supplemental Figure S2(B)).

Associations of baseline ¹⁸F-FDG PET/CT-derived parameters with PFS in NSCLC patients receiving immunotherapy.

SUV_max and SUV_mean

In all, 11 studies examined the association between the baseline SUV_max and PFS in NSCLC patients receiving immunotherapy. The pooled HR revealed no association between the SUV_max and PFS (HR: 0.979; 95% CI: 0.759–1.262, p = 0.868; I² = 62.1%, Figure 4(a)). When removing one study at a time, the pooled HR ranged from 0.913 to 1.040, with the lower bound of the 95% CI ranging from 0.702 to 0.809 and the upper bound of the 95% CI ranging from 1.157 to 1.336 (Supplemental Figure S3(A)).

Figure 4.

Forest plot of a meta-analysis of the prognostic role of SUV_max (a) and SUV_mean (b) on progression-free survival.

Five studies investigated the association between the SUV_mean and PFS in NSCLC patients receiving immunotherapy. The pooled HR showed that the SUV_mean was not significantly associated with PFS (HR: 0.688; 95% CI: 0.464–1.020, p = 0.062; I² = 26.60%, Figure 4(b)). When removing one study at a time, the pooled HR ranged from 0.624 to 0.791, with the lower bound of the 95% CI ranging from 0.388 to 0.500 and the upper bound of the 95% CI ranging from 0.875 to 1.253 (Supplemental Figure S3(B)).

MTV and TLG

In all, 17 studies reported an association between MTV and PFS in NSCLC patients receiving immunotherapy. The pooled HR showed that patients with higher MTV were significantly associated with poorer PFS compared to those with lower MTV, despite moderate heterogeneity (HR: 1.069; 95% CI: 1.016–1.124, p = 0.010; I² = 71.80%, Figure 5(a)). When removing one study at a time, the pooled HR ranged from 1.053 to 1.132, with the lower bound of the 95% CI ranging from 1.008 to 1.040 and the upper bound of the 95% CI ranging from 1.010 to 1.235 (Supplemental Figure S4(A)).

Figure 5.

Forest plot of a meta-analysis of the prognostic role of MTV (a) and TLG (b) on progression-free survival.

In all, 13 studies investigated the association between TLG and PFS outcomes and reported no association between TLG and PFS (HR: 0.995; 95% CI: 0.980–1.010, p = 0.543; I² = 87.10%, Figure 5(b)). When removing one study at a time, the pooled HR ranged from 0.992 to 0.998, with the lower bound of the 95% CI ranging from 0.970 to 0.982 and the upper bound of the 95% CI ranging from 1.009 to 1.021 (Supplemental Figure S4(B)).

Publication bias

The present study employed funnel plots, Egger’s test, and Begg’s test to assess publication bias in studies pertaining to OS and PFS, with Egger’s p-value of 0.060 and Begg’s p-value of 0.951 for OS, and Egger’s p-value of 0.195 and Begg’s p-value of 0.640 for PFS. The analysis indicated no significant publication bias for either OS (Figure 6(a)) or PFS (Figure 6(b)).

Figure 6.

The funnel plot of the prognostic role of SUV_max on overall survival (a) and progression-free survival (b).

Discussion

Main findings

This is the largest meta-analysis to date to examine the prognostic value of pretreatment baseline ¹⁸F-FDG PET/CT-derived parameters in NSCLC patients undergoing immunotherapy. We found that high MTV is weakly associated with both poor PFS and poor OS (with borderline HRs and high heterogeneity), while other parameters, including the SUV_max, SUV_mean, and TLG, were not predictive of survival outcomes.

The SUV is a widely used parameter in PET/CT interpretation, and its prognostic value in NSCLC has been assessed in many studies.⁴⁰ In our meta-analysis, both SUV_max and SUV_mean were found to be nonpredictive for survival. This finding is inconsistent with a published meta-analysis,⁴¹ in which a significant association was found between the baseline SUV_mean and PFS. This inconsistency might be explained by the application of random-effects versus fixed-effects models when pooling data, which has been frequently observed in previous practice of meta-analyses.⁴² We adopted a random-effects model given the clinical heterogeneity introduced during sampling and its stability was proven by sensitivity analysis. In addition, the previous meta-analysis pooled both univariate and multivariate HRs from the same population in one analysis, which might be another source of bias. In addition, the authors also acknowledged that the presence of pseudoprogression during immunotherapy would lead to inaccurate assessment of PFS, and, consequently, an unreliable association with the SUV_mean. The limited prognostic value of SUV_max in NSCLC patients might be explained by the partial-volume effect and dependence of the SUV_max on tumor size and T stage.⁴³ In addition, the SUV only reflects the metabolic activity of lesions and does not account for the overall volume of the tumor, which is more relevant to prognosis, particularly in those with intratumoral heterogeneity.^44,45

The MTV is a volume parameter that can be measured quantitatively and can reflect the metabolic volume of lesions in a certain anatomical location. Although we found a statistically significant association between MTV and both OS and PFS, we have to admit that the HRs were borderline with notable heterogeneity. Thus, the application of MTV in clinical practice shall be considered with caution. The prognostic value of MTV has been reported by published studies,⁴¹ but a few studies challenged this finding.^27,38,46 These inconsistencies might also be explained by heterogeneous patient characteristics, including a wide age range, different clinical stage and disease subtypes, and different software and the different ways used for the definition of the threshold of MTV.⁴⁷

The mechanisms underlying the predictive value of MTV are unclear. The prognostic value of MTV in oncology might be explained by its ability to quantify both the extent and metabolic activity of tumors. Specifically, a larger MTV reflects a greater number of tumor cells, which correlates with advanced disease stages and increased tissue invasion. An elevated MTV also indicates increased glucose uptake and metabolism and is a trait characteristic of aggressive tumors that demand substantial energy for rapid growth. Furthermore, larger tumors are prone to hypoxic regions, which contribute to resistance to certain therapies and promote more aggressive behavior.^48–50

TLG combines the volume and metabolic activity of lesions and is calculated by multiplying the MTV by the SUV_mean, providing an overall assessment of the tumor’s metabolic burden of the tumor. TLG is not prognostic according to our pooled results, which is, however, inconsistent with a published meta-analysis.⁴¹ This inconsistency might be explained by our update of the literature search, which resulted in the identification of additional new published studies.^{19,46,51–55} Moreover, we included a broader patient population by setting no limitation on the stage of disease. However, a subgroup analysis based on cancer stage was not applicable because many studies enrolling patients with broad stages did not provide data on the early-stage group (i.e., stages I–II). In addition, the published meta-analysis revealed high heterogeneity in the associations between TLG and both OS (83.7%) and PFS (86.8%). Thus, we believe that conclusions concerning the prognostic value of TLG cannot be drawn given the current evidence.

Clinical implications

¹⁸F-FDG PET/CT is a widely used in practice to evaluate advanced NSCLC patients before immunotherapy. Our findings suggest that PET/CT-derived parameters, such as MTV, may hold potential prognostic value and aid in treatment planning for patients with advanced NSCLC. However, given the high cost and limited accessibility of PET/CT, more evidence, particularly cost-effectiveness evidence, is warranted to support the routine use of this modality in advanced NSCLC patients prior to immunotherapy in daily practice.

Strengths and limitations

The strengths of our study include the inclusion of the largest number of studies to examine the efficacy of ¹⁸F-FDG PET/CT in predicting survival outcomes in patients with NSCLC receiving immunotherapy, encompassing nearly all relevant parameters of ¹⁸F-FDG PET/CT.

This study also has limitations. First, high heterogeneity was observed for the majority of outcomes, particularly between MTV and survival. We performed subgroup analysis and confirmed the stability of results. However, our findings should still be interpreted with caution. Second, the incorporation of other imaging modalities or biomarkers might provide a more comprehensive assessment of immunotherapy response since ¹⁸F-FDG PET/CT may not capture all aspects of tumor biology or treatment response. However, we did not include studies of combined imaging biomarkers from FDG PET/CT and other modalities due to insufficient data. Third, we did not assess the impact of tumor stage on outcomes since data on early-stage groups were not extractable from most studies.

Conclusion

In summary, this study suggested the prognostic value of pretreatment FDG-derived parameters in the prediction of survival in NSCLC patients receiving immunotherapy. Particularly, a high MTV might predict poorer PFS and OS. However, future prospective studies with larger sample sizes are warranted to support the value of ¹⁸F-FDG PET/CT in the prognosis of NSCLC patients.

Supplemental Material

sj-docx-1-tam-10.1177_17588359241293364 – Supplemental material for The role of baseline 18F-FDG PET/CT for survival prognosis in NSCLC patients undergoing immunotherapy: a systematic review and meta-analysis

Supplemental material, sj-docx-1-tam-10.1177_17588359241293364 for The role of baseline 18F-FDG PET/CT for survival prognosis in NSCLC patients undergoing immunotherapy: a systematic review and meta-analysis by Mingxing Huang, Yuheng Zou, Weichen Wang, Qianrui Li and Rong Tian in Therapeutic Advances in Medical Oncology

Supplemental Material

sj-docx-2-tam-10.1177_17588359241293364 – Supplemental material for The role of baseline 18F-FDG PET/CT for survival prognosis in NSCLC patients undergoing immunotherapy: a systematic review and meta-analysis

Supplemental material, sj-docx-2-tam-10.1177_17588359241293364 for The role of baseline 18F-FDG PET/CT for survival prognosis in NSCLC patients undergoing immunotherapy: a systematic review and meta-analysis by Mingxing Huang, Yuheng Zou, Weichen Wang, Qianrui Li and Rong Tian in Therapeutic Advances in Medical Oncology

Supplemental Material

sj-docx-3-tam-10.1177_17588359241293364 – Supplemental material for The role of baseline 18F-FDG PET/CT for survival prognosis in NSCLC patients undergoing immunotherapy: a systematic review and meta-analysis

Supplemental material, sj-docx-3-tam-10.1177_17588359241293364 for The role of baseline 18F-FDG PET/CT for survival prognosis in NSCLC patients undergoing immunotherapy: a systematic review and meta-analysis by Mingxing Huang, Yuheng Zou, Weichen Wang, Qianrui Li and Rong Tian in Therapeutic Advances in Medical Oncology

Footnotes

Acknowledgements

None.

Declarations

Supplemental material

Supplemental material for this article is available online.

References

The Lancet. Lung cancer: some progress, but still a lot more to do. Lancet (London, England) 2019; 394(10212): 1880.

Siegel

Miller

Fuchs

, et al. Cancer statistics, 2021. CA Cancer J Clin 2021; 71(1): 7–33.

Travis

Brambilla

Burke

, et al. Introduction to the 2015 World Health Organization classification of tumors of the lung, pleura, thymus, and heart. J Thorac Oncol 2015; 10(9): 1240–1242.

Arbour

Riely

GJ.

Systemic therapy for locally advanced and metastatic non-small cell lung cancer: a review. JAMA 2019; 322(8): 764–774.

Mazzone

Mekhail

Current and emerging medical treatments for non-small cell lung cancer: a primer for pulmonologists. Respir Med 2012; 106(4): 473–492.

Suresh

Naidoo

Lin

, et al. Immune checkpoint immunotherapy for non-small cell lung cancer: benefits and pulmonary toxicities. Chest 2018; 154(6): 1416–1423.

Garon

Rizvi

Hui

, et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med 2015; 372(21): 2018–2028.

Rittmeyer

Barlesi

Waterkamp

, et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial. Lancet 2017; 389(10066): 255–265.

Nishino

Ramaiya

Chambers

, et al. Immune-related response assessment during PD-1 inhibitor therapy in advanced non-small-cell lung cancer patients. J Immunother Cancer 2016; 4: 84.

10.

Brahmer

Reckamp

Baas

, et al. Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N Engl J Med 2015; 373(2): 123–135.

11.

Aldea

Benitez

Mezquita

The lung immune prognostic index (LIPI) stratifies prognostic groups in advanced non-small cell lung cancer (NSCLC) patients. Transl Lung Cancer Res 2020; 9(4): 967–970.

12.

Dall’Olio

Maggio

Massucci

, et al. ECOG performance status ⩾2 as a prognostic factor in patients with advanced non small cell lung cancer treated with immune checkpoint inhibitors—a systematic review and meta-analysis of real world data. Lung Cancer 2020; 145: 95–104.

13.

Proulx-Rocray

Routy

Nassabein

, et al. The prognostic impact of KRAS, TP53, STK11 and KEAP1 mutations and their influence on the NLR in NSCLC patients treated with immunotherapy. Cancer Treat Res Commun 2023; 37: 100767.

14.

Dercle

Ammari

Champiat

, et al. Rapid and objective CT scan prognostic scoring identifies metastatic patients with long-term clinical benefit on anti-PD-1/-L1 therapy. Eur J Cancer 2016; 65: 33–42.

15.

Seban

Nemer

Marabelle

, et al. Prognostic and theranostic ¹⁸F-FDG PET biomarkers for anti-PD1 immunotherapy in metastatic melanoma: association with outcome and transcriptomics. Eur J Nucl Med Mol Imaging 2019; 46(11): 2298–2310.

16.

Kim

Hwang

Kim

, et al. Predicting treatment outcomes using (18)F-FDG PET biomarkers in patients with non-small-cell lung cancer receiving chemoimmunotherapy. Ther Adv Med Oncol 2022; 14: 17588359211068732.

17.

Allal

Slosman

Kebdani

, et al. Prediction of outcome in head-and-neck cancer patients using the standardized uptake value of 2-[18F]fluoro-2-deoxy-D-glucose. Int J Radiat Oncol Biol Phys 2004; 59(5): 1295–1300.

18.

Wong

Lin

Schoder

, et al. Diagnostic and prognostic value of [18F] fluorodeoxyglucose positron emission tomography for recurrent head and neck squamous cell carcinoma. J Clin Oncol 2002; 20(20): 4199–4208.

19.

Kudura

Ritz

Templeton

, et al. Predictive value of total metabolic tumor burden prior to treatment in NSCLC patients treated with immune checkpoint inhibition. J Clin Med 2023; 12(11): 3725.

20.

Polverari

Ceci

Bertaglia

, et al. F-FDG pet parameters and radiomics features analysis in advanced NSCLC treated with immunotherapy as predictors of therapy response and survival. Cancers (Basel) 2020; 12(5): 1163.

21.

Tricarico

Chardin

Martin

, et al. 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(4): e007628.

22.

Son

Kang

Jeong

, et al. Prognostic value of volumetric parameters measured by pretreatment ¹⁸F FDG PET/CT in patients with cutaneous malignant melanoma. 2016; 41(6): e266–e273.

23.

Choi

Han

, et al. The prognostic value of (18)F-FDG PET/CT for early recurrence in operable breast cancer: comparison with TNM stage. Nucl Med Mol Imaging 2013; 47(4): 263–267.

24.

Han

Kim

Woo

, et al. Prognostic value of volumetric parameters of pretreatment ¹⁸F-FDG PET/CT in esophageal cancer: a systematic review and meta-analysis. Clin Nucl Med 2018; 43(12): 887–894.

25.

Kiamanesh

Ayati

Sadeghi

, et al. The value of FDG PET/CT imaging in outcome prediction and response assessment of lymphoma patients treated with immunotherapy: a meta-analysis and systematic review. Eur J Nucl Med Mol Imaging 2022; 49(13): 4661–4676.

26.

H-J

Pak

Cheon

, et al. Prognostic value of volumetric parameters of (18)F-FDG PET in non-small-cell lung cancer: a meta-analysis. Eur J Nucl Med Mol Imaging 2015; 42(2): 241–251.

27.

Vekens

Everaert

Neyns

, et al. The value of (18)F-FDG PET/CT in predicting the response to PD-1 blocking immunotherapy in advanced NSCLC patients with high-level PD-L1 expression. Clin Lung Cancer 2021; 22(5): 432–440.

28.

Page

McKenzie

Bossuyt

, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372: n71.

29.

Wells

Shea

, et al. The Newcastle–Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa: Ottawa Hospital Research Institute 2014.

30.

Castello

Toschi

Rossi

, et al. The immune-metabolic-prognostic index and clinical outcomes in patients with non-small cell lung carcinoma under checkpoint inhibitors. J Cancer Res Clin Oncol 2020; 146(5): 1235–1243.

31.

Takada

Toyokawa

Yoneshima

, et al. F-FDG uptake in PET/CT is a potential predictive biomarker of response to anti-PD-1 antibody therapy in non-small cell lung cancer. Sci Rep 2019; 9(1): 13362.

32.

Tao

, et al. The efficiency of F-FDG PET-CT for predicting the major pathologic response to the neoadjuvant PD-1 blockade in resectable non-small cell lung cancer. Eur J Nucl Med Mol Imaging 2020; 47(5): 1209–1219.

33.

Chardin

Paquet

Schiappa

, et al. Baseline metabolic tumor volume as a strong predictive and prognostic biomarker in patients with non-small cell lung cancer treated with PD1 inhibitors: a prospective study. J Immunother Cancer 2020; 8(2): e000645.

34.

Kaira

Higuchi

Naruse

, et al. Metabolic activity by F-FDG-PET/CT is predictive of early response after nivolumab in previously treated NSCLC. Eur J Nucl Med Mol Imaging 2018; 45(1): 56–66.

35.

Castello

Rossi

Toschi

, et al. Impact of antibiotic therapy and metabolic parameters in non-small cell lung cancer patients receiving checkpoint inhibitors. J Clin Med 2021; 10(6): 1251.

36.

Evangelista

Cuppari

Menis

, et al. ¹⁸F-FDG PET/CT in non-small-cell lung cancer patients: a potential predictive biomarker of response to immunotherapy. Nucl Med Commun 2019; 40(8): 802–807.

37.

Seban

R-D

Assie

J-B

Giroux-Leprieur

, et al. FDG-PET biomarkers associated with long-term benefit from first-line immunotherapy in patients with advanced non-small cell lung cancer. Ann Nucl Med 2020; 34(12): 968–974.

38.

Seban

Mezquita

Berenbaum

, et al. Baseline metabolic tumor burden on FDG PET/CT scans predicts outcome in advanced NSCLC patients treated with immune checkpoint inhibitors. Eur J Nucl Med Mol Imaging 2020; 47(5): 1147–1157.

39.

Monaco

Gemelli

Gotuzzo

, et al. Metabolic parameters as biomarkers of response to immunotherapy and prognosis in non-small cell lung cancer (NSCLC): a real world experience. Cancers (Basel) 2021; 13(7): 1634.

40.

Berghmans

Dusart

Paesmans

, et al. Primary tumor standardized uptake value (SUVmax) measured on fluorodeoxyglucose positron emission tomography (FDG-PET) is of prognostic value for survival in non-small cell lung cancer (NSCLC): a systematic review and meta-analysis (MA) by the European Lung Cancer Working Party for the IASLC Lung Cancer Staging Project. J Thorac Oncol 2008; 3(1): 6–12.

41.

Ling

Zhang

Peng

, et al. Prognostic value of (18)F-FDG PET/CT in patients with advanced or metastatic non-small-cell lung cancer treated with immune checkpoint inhibitors: a systematic review and meta-analysis. Front Immunol 2022; 13: 1014063.

42.

Lyu

, et al. Alternative statistical analysis shows exercise training-induced improvements in peak VO2 are clinically significant. Sports Med 2015; 45(5): 763–765.

43.

van Velden

FHP

Cheebsumon

Yaqub

, et al. Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur J Nucl Med Mol Imaging 2011; 38(9): 1636–1647.

44.

Rahim

Kim

, et al. Recent trends in PET image interpretations using volumetric and texture-based quantification methods in nuclear oncology. Nucl Med Mol Imaging 2014; 48(1): 1–15.

45.

Bosque

Calvo

Molina-García

, et al. Metabolic activity grows in human cancers pushed by phenotypic variability. iScience 2023; 26(3): 106118.

46.

Rizzo

Cantale

Mogavero

, et al. Assessing the role of colonic and other anatomical sites uptake by [(18) F]FDG-PET/CT and immune-inflammatory peripheral blood indexes in patients with advanced non-small cell lung cancer treated with first-line immune checkpoint inhibitors. Thorac Cancer 2023; 14(24): 2473–2483.

47.

Bradshaw

Solaiyappan

, et al. Current methods to define metabolic tumor volume in positron emission tomography: which one is better? Nucl Med Mol Imaging 2018; 52(1): 5–15.

48.

Oriuchi

Sugawara

Shiga

Positron emission tomography for response evaluation in microenvironment-targeted anti-cancer therapy. Biomedicines 2020; 8(9): 371.

49.

Matsumoto

Baba

Endo

, et al. Metabolic tumor volume by ¹⁸F-FDG PET/CT can predict the clinical outcome of primary malignant spine/spinal tumors. BioMed Res Int 2017; 2017(1): 8132676.

50.

Tian

Shen

, et al. Utility of volumetric metabolic parameters on preoperative FDG PET/CT for predicting tumor lymphovascular invasion in non-small cell lung cancer. AJR Am J Roentgenol 2021; 217(6): 1433–1443.

51.

Feng

Wang

Chen

, et al. ¹⁸F-FDG PET/CT for evaluation of metastases in nonsmall cell lung cancer on the efficacy of immunotherapy. Nucl Med Commun 2023; 44(10): 900–909.

52.

Grambozov

Kalantari

Beheshti

, et al. Pretreatment 18-FDG-PET/CT parameters can serve as prognostic imaging biomarkers in recurrent NSCLC patients treated with reirradiation-chemoimmunotherapy. Radiother Oncol 2023; 185: 109728.

53.

Andraos

Halmos

Cheng

, et al. Disease burden on PET predicts outcomes for advanced NSCLC patients treated with first-line immunotherapy. Clin Lung Cancer 2022; 23(4): 291–299.

54.

Silva

Wanderley

CWS

Gomes Marin

, et al. Tumor glycolytic profiling through (18)F-FDG PET/CT predicts immune checkpoint inhibitor efficacy in advanced NSCLC. Ther Adv Med Oncol 2022; 14: 17588359221138386.

55.

Kudura

Ritz

Kutzker

, et al. Predictive value of baseline FDG-PET/CT for the durable response to immune checkpoint inhibition in NSCLC patients using the morphological and metabolic features of primary tumors. Cancers (Basel) 2022; 14(24): 6095.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.72 MB

0.03 MB

0.01 MB

The role of baseline 18 F-FDG PET/CT for survival prognosis in NSCLC patients undergoing immunotherapy: a systematic review and meta-analysis

Abstract

Background:

Objectives:

Design:

Data sources and methods:

Results:

Conclusion:

Plain language summary

Keywords

Introduction

Methods

Eligibility criteria

Search strategy and study selection

Quality assessment and data extraction

Statistical analyses

Results

Eligible studies and characteristics

Quality assessment of included studies

Summary of outcomes

SUVmax and SUVmean

MTV and TLG

SUVmax and SUVmean

MTV and TLG

Publication bias

Discussion

Main findings

Clinical implications

Strengths and limitations

Conclusion

Supplemental Material

sj-docx-1-tam-10.1177_17588359241293364 – Supplemental material for The role of baseline 18F-FDG PET/CT for survival prognosis in NSCLC patients undergoing immunotherapy: a systematic review and meta-analysis

Supplemental Material

sj-docx-2-tam-10.1177_17588359241293364 – Supplemental material for The role of baseline 18F-FDG PET/CT for survival prognosis in NSCLC patients undergoing immunotherapy: a systematic review and meta-analysis

Supplemental Material

sj-docx-3-tam-10.1177_17588359241293364 – Supplemental material for The role of baseline 18F-FDG PET/CT for survival prognosis in NSCLC patients undergoing immunotherapy: a systematic review and meta-analysis

Footnotes

Acknowledgements

Declarations

Supplemental material

References

Supplementary Material

SUV_max and SUV_mean

SUV_max and SUV_mean