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What is the prognostic impact of FDG PET in locally advanced head and neck squamous cell carcinoma treated with concomitant chemo-radiotherapy? A systematic review and meta-analysis

  • Pierluigi Bonomo
  • A. Merlotti
  • E. Olmetto
  • A. Bianchi
  • I. Desideri
  • A. Bacigalupo
  • P. Franco
  • C. Franzese
  • E. Orlandi
  • L. Livi
  • S. Caini
Open Access
Original Article

Abstract

Purpose

Evidence is conflicting on the prognostic value of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in head and neck squamous cell carcinoma. The aim of our study was to determine the impact of semiquantitative and qualitative metabolic parameters on the outcome in patients managed with standard treatment for locally advanced disease.

Methods

A systematic review of the literature was conducted. A meta-analysis was performed of studies providing estimates of relative risk (RR) for the association between semiquantitative metabolic parameters and efficacy outcome measures.

Results

The analysis included 25 studies, for a total of 2,223 subjects. The most frequent primary tumour site was the oropharynx (1,150/2,223 patients, 51.7%). According to the available data, the majority of patients had stage III/IV disease (1,709/1,799, 94.9%; no information available in four studies) and were treated with standard concurrent chemoradiotherapy (1,562/2,009 patients, 77.7%; only one study without available information). A total of 11, 8 and 4 independent studies provided RR estimates for the association between baseline FDG PET metrics and overall survival (OS), progression-free survival (PFS) and locoregional control (LRC), respectively. High pretreatment metabolic tumour volume (MTV) was significantly associated with a worse OS (summary RR 1.86, 95% CI 1.08–3.21), PFS (summary RR 1.81, 95% CI 1.14–2.89) and LRC (summary RR 3.49, 95% CI 1.65–7.35). Given the large heterogeneity (I2 > 50%) affecting the summary measures, no cumulative threshold for an unfavourable prognosis could be defined. No statistically significant association was found between SUVmax and any of the outcome measures.

Conclusion

FDG PET has prognostic relevance in the context of locally advanced head and neck squamous cell carcinoma. Pretreatment MTV is the only metabolic variable with a significant impact on patient outcome. Because of the heterogeneity and the lack of standardized methodology, no definitive conclusions on optimal cut-off values can be drawn.

Keywords

Head and neck cancer Radiotherapy 18F-Fluorodeoxyglucose (FDG) positron emission tomography Metabolic tumour volume 

Introduction

Head and neck cancer is the sixth most common malignant tumour, with increasing incidence worldwide [1]. In over 95% of cases, the disease arises from the epithelial layer of the mucosa lining the upper aerodigestive tract. Due to the absence of anatomical barriers, the abundant lymphatic drainage of the neck and the usually infiltrative pattern of growth of head and neck squamous cell carcinoma (HNSCC), in about 60% of patients the diagnosis is made at an advanced locoregional stage. In order to maximize the likelihood of disease cure, multimodality treatment is usually needed. Therapeutic management is often challenging: both primary radical surgery and concurrent chemoradiotherapy are burdened with a high rate of posttreatment complications, acute and long-term toxicities [2] and a marked detrimental effect on quality of life. Notwithstanding the refinement of treatment strategies that has taken place in last 20 years, the prognosis of HNSCC remains severe, with a cumulative 5-year overall survival (OS) rate of 45–55% [3] in patients with locally advanced disease. The prevalent pattern of failure in the overall population is locoregional: about 50% of first events of relapse occur at the primary tumour site and/or in the neck, in the vast majority (about 90%) within the first 2 years after treatment.

Taking into account that the patient’s outlook can be substantially influenced by clinical factors with large variability existing among the different subsites of disease, a series of common features contribute to the severe prognosis of locally advanced HNSCC; these include the suboptimal efficacy of the standard “one size fits all” multimodal approach, the large proportion of frail patients who are noncompliant with intensive therapy, and the absence of biomarkers. In this regard, the only notable exception is the human papillomavirus (HPV). In the last 15 years, a major epidemiological shift has taken place in western countries due to the rising incidence of HPV-associated oropharyngeal cancer [4], reducing the dominance of the classical phenotype of HNSCC resulting from alcohol and tobacco-induced field cancerization. A positive HPV status was recognized as an independent favourable prognostic factor in a series of correlative prospective studies and in an unplanned secondary analysis of the randomized phase 3 RTOG 0129 trial [5]. Overall, HPV positivity is associated with a reduction in the risk of death and disease progression of about 60%.

Although major progress has been achieved in unravelling key molecular pathways involved in HNSCC pathogenesis [6], at present no biomarkers are available in clinical practice apart from HPV status. Prognostic information is therefore critically lacking in the management of patients affected by HNSCC. Next to individual genomic profiling, an alternative strategy which has been explored in recent years is to integrate molecular imaging into precision oncology care, exploiting the potential of imaging as a biomarker. The possibility of linking the information obtained from medical images with personalized treatment forms the core of “theragnostics”, an term that has been used particularly in the context of radiation therapy [7]. In a hallmark review published in 2000, Ling et al. [8] suggested that the evolution of molecular imaging could facilitate the development of customized dose delivery in the era of intensity-modulated radiotherapy (IMRT). As foreseen by Ling and colleagues, in the last 15 years molecular imaging has been increasingly implemented in the management of HNSCC, in particular 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET). The fundamental prerequisite is the ability to image physiopathological processes occurring within a tumour or its microenvironment. The use of FDG allows the characterization of the metabolic activity of a defined tumour burden. In HNSCC, available evidence supports the role of FDG PET in primary target definition for radiotherapy planning [9], staging [10] and posttreatment response assessment [11]. However, its potential impact on patient outcomes is an unresolved issue. The aim of this work was to define the relevance of semiquantitative and qualitative FDG PET features as prognostic biomarkers in the curative setting of locally advanced head and neck cancer.

Materials and methods

In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement [12], a systematic review of the literature was conducted. Relevant articles were identified in two databases (MEDLINE and Embase) over a 10-year period (1 January 2007 to 28 February 2017) using the appropriate terminology as described in Appendix 1 of the Supplementary material. Conference proceedings of main international conferences (ASCO, ASTRO, ESMO, ESTRO, ECCO) were also searched. The reference lists of the articles reviewed as full texts were also searched manually. The literature search strategy was based on the PICO methodology [13], as discussed in the following sections.

Population

The target population of our analysis consisted of adult patients (>18 years of age) treated with curatively intended radiotherapy, concurrent chemoradiotherapy or radiotherapy combined with targeted therapy for locally advanced HNSCC. Primary surgery and induction chemotherapy were not allowed. In view of the known heterogeneity among different head and neck subsites, we sought to assess whether the impact of metabolic parameters could be observed in specific disease entities or in HNSCC taken as a whole. In addition, information on the radiotherapy technique used and the schedule of systemic therapy administered was collected when available. To provide evidence-based support for the analysis, the published literature was categorized according to the type of study design: all case series except those with fewer than 20 patients, literature reviews and consensus statements were eligible. Only studies in the English language were included.

Interventions

Upon inclusion in the analysis, adequate information on FDG PET metrics (semiquantitative parameters and/or qualitative scores) had to be retrieved from the studies analysed. Studies focusing on tracers other than FDG and on integrated PET/MRI were excluded. Since the main aim of this review was to investigate the potential impact of specific metabolic data on HNSCC prognosis, the following parameters were considered as main interventions: standardized uptake values (SUVmax, SUVmean, SUVpeak), metabolic tumour volume (MTV) and total lesion glycolysis (TLG). These parameters were defined according to reference guidelines [14], as follows:
  • SUV (body-weighted): the concentration of FDG in a given region of interest (ROI) or volume of interest (VOI; expressed in kilobecquerels per millilitre) divided by the ratio between administered activity (corrected for radioactive decay at the time of scanning) and the body weight of the patient

  • SUVmax: the highest SUV of pixels (or voxels) in a given ROI (or VOI)

  • SUVmean: the mean SUV of pixels (or voxels) in a given ROI (or VOI)

  • SUVpeak: SUVmean within a 1-cm3 spherical VOI centred on the voxels with the highest uptake

  • MTV: the VOI segmented using a fixed threshold (usually 41% or 50%) of FDG-avid lesions

  • TLG: the product of the VOI average SUV (SUVmean) and the corresponding MTV

Standardized qualitative interpretations of FDG PET scans were also considered interventions, if rigorously defined. In addition, the included studies were further analysed according to the timing of the FDG PET scans, whether performed before, during or after treatment.

Comparators

When available, different clinical factors other than the metabolic FDG PET parameters discussed above were defined as “comparators” if analysed as potential prognostic biomarkers.

Outcomes

Ultimately, we sought to assess whether intrinsic features on FDG PET retain prognostic significance in terms of outcome. Therefore, we searched for a potential correlation between the interventions (as described above) and locoregional control (LRC), progression-free survival (PFS) and OS at a minimum follow-up of 1 year. These outcome measures were defined as follows:
  • LRC: the time from randomization (or study initiation) to local and/or regional disease progression

  • PFS: the time from randomization (or study initiation) to disease progression or death

  • OS: the time from randomization (or study initiation) to death from any cause

Studies in which the main outcome measure was not consistent with the definition of the prespecified efficacy endpoints were excluded. Studies performed to assess the diagnostic accuracy of FDG PET as well as “in-silico” radiotherapy planning analyses were also excluded.

Statistical analysis

Baseline demographics, patient and disease characteristics, treatment features and outcome data were collected by three authors (P.B., A.M., E.O.), verified by two reviewers (I.D., S.C.) and summarized using descriptive statistics. From all studies included in the literature review, we extracted the most adjusted estimate of relative risk (RR), including odds ratio and hazard ratio (HR), for the association between each of the metabolic parameters (SUVmax, SUVmean, SUVpeak, MTV and TLG) and each of the patient outcomes (OS, PFS and LRC). When there were two or more independent RR estimates, these were transformed into logRR and the corresponding variance using the formula of Greenland [15] and pooled using random effects models to obtain a summary RR (SRR) and corresponding 95% confidence intervals (CI). We assessed the heterogeneity between studies using the I2 statistic, which is interpreted as the percentage of the variability that is attributable to true heterogeneity rather than chance. Larger values of I2 denote greater between-estimate heterogeneity; values of I2 below 50% are considered acceptable. We did not perform subgroup analysis and meta-regression because of the limited sample size. Finally, we evaluated the presence of publication bias using the funnel plot of Begg and Mazumdar [16] and the regression test of Egger et al. [17]. The meta-analysis was conducted using the metan command in Stata version 14 (Stata Corp, College Station, TX).

Results

Data collection and analysis

Two authors (P.B., A.M.) independently examined the titles and abstracts of each search record, and retrieved the full text articles for potentially eligible studies. The full texts were further examined according to the inclusion criteria. Discrepancies were resolved by consensus. Data were extracted by the two authors using a data collection form. Overall, of 180 studies identified using the predefined search criteria, 81 were screened by assessment of the abstracts (Fig. 1). Of these screened studies, 42 were evaluated for eligibility, and 25 [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44] satisfied the inclusion criteria and were therefore analysed fully. The whole reference lists of the eligible studies and the reasons for exclusion are available in Appendix 2 of the Supplementary material. In terms of study design, most included studies (21/25, 84%) were retrospective. Two papers were initially retrieved in abstract form [33, 41] and updated as soon as the full versions became available [34, 42]. One study [29] had limited data on the disease and treatment characteristics collected in most patients, but provided adequate information on FDG PET variables and outcomes.
Fig. 1

Flow chart of the literature search

Patient characteristics

The overall population consisted of 2,223 patients (Table 1). The median age of the whole cohort was 59 years (range 48–68 years). Most patients (1,875/2,223, 84.3%) were men. Only 3 of the 25 studies [31, 32, 40] provided information about tobacco exposure in terms of pack-years. Generic information on subjects with a smoking history was reported in six additional studies [22, 28, 29, 30, 39, 43] (81.4%, 82.6%, 67.3%, 80%, 90.9% and 72% of patients were current or former smokers, respectively). In addition, data on baseline ECOG Performance Status were reported for only 29% of the whole cohort (646/2,223).
Table 1

Design of the studies analysed and patient characteristics

Reference

Year

Study design

Patient characteristics

Total number

Age (years)

Gender, n (%)

Smoking (pack-years)

ECOG performance status, grade (%)

Median

Range

Male

Female

[18]

2015

Retrospective

62

57

23–83

44 (71)

18 (29)

ns

ns

[19]

2012

Retrospective

26

63

41–79

23 (89)

3 (11)

ns

ns

[20]

2017

Retrospective

122

61

ns

101 (83)

21 (17)

ns

ns

[21]

2014

Prospective

51

52

36–69

48 (94)

3 (6)

ns

ns

[22]

2013

Retrospective

70

52

37–86

66 (94)

4 (6)

ns

ns

[23]

2009

Retrospective

82

53.8

11–70

69 (84)

13 (16)

ns

ns

[24]

2012

Retrospective

88

59

26–83

74 (84)

14 (16)

ns

ns

[25]

2015

Retrospective

70

65

21–91

61 (87)

9 (13)

ns

ns

[26]

2016

Retrospective

78

62

24–79

63 (81)

15 (19)

ns

0/1 (98.8)

[27]

2014

Retrospective

108

67

43–85

93 (86)

15 (14)

ns

ns

[28]

2017

Retrospective

75

59

39–80

67 (89)

8 (11)

ns

ns

[29]

2014

Retrospective

214

58

ns

175 (82)

39 (18)

ns

ns

[30]

2017

Prospective

35

67.6

50–80

32 (91)

3 (9)

ns

0/1 (100)

[31]

2016

Retrospective

69

61

39–81

61 (88)

8 (12)

>10: 40 (60.9%)

ns

[32]

2015

Retrospective

72

60

39–75

61 (85)

11 (15)

>10: 41 (56.9%)

ns

[34]

2017

Retrospective

85

66

43–79

81 (95)

4 (5)

ns

ns

[35]

2011

Retrospective

47

55.1

15–86.1

39 (83)

8 (17)

ns

0/1 (95)

[36]

2016

Prospective

86

50

40–60

80 (93)

6 (7)

ns

ns

[37]

2013

Retrospective

81

65

34–81

74 (91)

7 (9)

ns

ns

[38]

2015

Retrospective

287

64

33–89

221 (77)

66 (23)

ns

0 (63)

[39]

2014

Retrospective

100

56

27–81

86 (86)

14 (14)

ns

ns

[40]

2015

Prospective

74

56

42–73

65 (88)

9 (12)

8.75 (median)

0/1 (100)

[42]

2016

Prospective

125

59

ns

93 (74)

32 (26)

ns

0/1 (100)

[43]

2014

Retrospective

40

48

21–69

30 (75)

10 (25)

ns

ns

[44]

2017

Retrospective

76

55

42–76

68 (89)

8 (11)

ns

ns

ns not stated

Disease-related features

The most frequent primary tumour site was the oropharynx (1,150/2,223 patients, 51.7%), followed by the hypopharynx (377, 16.9%), larynx (345, 15.6%), nasopharynx (197, 8.9%), oral cavity (98, 4.4%), and others (56, 2.5%; Table 2). Information on HPV status was available for fewer than half of those with oropharyngeal tumour (508/1,150, 44.1%), and of these (as extrapolable from 7 of the 25 studies) 247 (48.6%) were p16/HPV-positive. Overall, in the majority of patients (1,709/1,799, 94.9%; no information available in four studies) the disease was in aggregated stage III/IV (Table 3).
Table 2

Disease features: tumour site

Reference

Number of patients

Oropharynx

Larynx

Hypopharynx

Oral cavity

Nasopharynx

Other

Total

p16/HPV-positive

[18]

62

14 (22%)

ns

10 (16%)

12 (20%)

3 (5%)

14 (22%)

9 (15%)

[19]

26

12 (46%)

ns

9 (35%)

2 (8%)

0

3 (11%)

0

[20]

122

122 (100%)

32 (26%)

0

0

0

0

0

[21]

51

20 (39%)

ns

0

21 (41%)

0

10 (20%)

0

[22]

70

70 (100%)

13 (19%)

0

0

0

0

0

[23]

82

13 (16%)

ns

0

6 (7%)

0

63 (77%)

0

[24]

88

58 (66%)

ns

15 (17%)

0

1 (1%)

7 (8%)

7 (8%)

[25]

70

25 (36%)

ns

0

36 (51%)

0

9 (13%)

0

[26]

78

47 (61%)

ns

3 (4%)

19 (24%)

5 (6%)

0

4 (5%)

[27]

108

28 (26%)

ns

29 (27%)

34 (31%)

17 (16%)

0

0

[28]

75

56 (75%)

ns

11 (15%)

5 (6%)

3 (4%)

0

0

[29]

214

135 (63%)

123 (57%)

40 (19%)

0

11 (5%)

0

28: (13%)

[30]

35

9 (26%)

ns

11 (31%)

12 (34%)

3 (9%)

0

0

[31]

69

41 (59%)

ns

20 (30%)

5 (7%)

3 (4%)

0

0

[32]

72

47 (66%)

ns

16 (22%)

6 (8%)

3 (4%)

0

0

[34]

85

0

ns

35 (41%)

50 (59%)

0

0

0

[35]

47

21 (45%)

ns

7 (15%)

4 (8%)

2 (4%)

13 (28%)

0

[36]

86

45 (52%)

ns

0

41 (48%)

0

0

0

[37]

81

0

ns

57 (70%)

24 (30%)

0

0

0

[38]

287

129 (45%)

ns

44 (15%)

55 (19%)

29 (10%)

30 (11%)

0

[39]

100

100 (100%)

14 (14%)

0

0

0

0

0

[40]

74

58 (78%)

25 (34%)

9 (12%)

7 (10%)

0

0

0

[42]

125

69 (56%)

37 (30%)

21 (17%)

11 (9%)

8 (6%)

8 (6%)

8 (6%)

[43]

40

0

0

0

0

0

40 (100%)

0

[44]

76

31 (41%)

3 (4%)

8 (10%)

27 (36%)

10 (13%)

0

0

ns not stated

Table 3

Disease features: stage

Reference

Tx

T1

T2

T3

T4

N0

N1

N2

N3

I

II

III

IV

III/IV

[18]

0

8

20

14

20

ns

ns

ns

ns

0

10 (16%)

18 (29%)

34 (55%)

52 (84%)

[19]

0

3

9

6

8

3

7

16

0

0

1 (3%)

7 (27%)

18 (70%)

25 (97%)

[20]

0

7

36

52

27

14

21

80

7

0

0

ns

ns

122 (100%)

[21]

ns

ns

ns

ns

ns

ns

ns

ns

ns

0

0

16 (31%)

35 (69%)

51 (100%)

[22]

0

0

0

20

50

ns

ns

ns

ns

0

8 (11%)

43 (52%)

19 (27%)

62 (89%)

[23]

0

22

25

17

18

10

22

43

7

4 (5%)

12 (15%)

30 (36%)

36 (44%)

66 (80%)

[24]

ns

ns

ns

ns

ns

ns

ns

ns

ns

3 (3%)

1 (1%)

15 (16%)

70 (80%)

85 (96%)

[25]

0

0

34

16

20

29

10

18

13

ns

ns

ns

ns

ns

[26]

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

23 (30%)

55 (70%)

78 (100%)

[27]

0

26

37

13

32

51

14

40

3

18 (17%)

20 (18%)

19 (18%)

51 (47%)

70 (65%)

[28]

0

6

31

27

11

0

11

59

5

0

0

10 (13%)

65 (87%)

75 (100%)

[29]

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

[30]

0

0

13

16

6

13

4

16

2

0

4 (11%)

10 (29%)

21 (60%)

31 (89%)

[31]

0

4

28

27

10

16

10

39

4

0

6 (9%)

18 (26%)

45 (65%)

63 (91%)

[32]

0

6

25

31

10

9

11

47

5

0

0

18 (25%)

54 (75%)

72 (100%)

[34]

0

0

19

49

17

26

15

44

0

0

0

33 (39%)

52 (61%)

85 (100%)

[35]

ns

ns

ns

ns

ns

ns

ns

ns

ns

0

2 (4%)

11 (23%)

34 (73%)

45 (96%)

[36]

ns

ns

ns

ns

ns

ns

ns

ns

ns

0

0

4 (5%)

82 (95%)

86 (100%)

[37]

0

2

11

43

25

28

15

38

3

0

0

32 (39%)

49 (61%)

81 (100%)

[38]

0

32

92

78

85

30

32

190

35

0

0

54 (19%)

233 (81%)

287 (100%)

[39]

0

14

39

23

24

4

12

80

4

ns

ns

ns

ns

ns

[40]

0

0

29

26

19

0

0

ns

6

0

0

ns

ns

74 (100%)

[42]

7

ns

ns

ns

ns

0

0

119

6

0

0

ns

ns

125 (100%)

[43]

0

14

ns

ns

ns

0

19

ns

ns

7

ns

ns

ns

ns

[44]

0

2

10

27

37

12

7

54

3

1 (1%)

1 (1%)

11 (15%)

63 (83%)

74 (98%)

ns not stated

Treatment-related features

Most patients (1,467/1,544, 95%) were treated with IMRT, while 77 (5%) received 3D-conformal radiotherapy (3DCRT). No information on the radiotherapy technique used was available in seven studies (Table 4). The most adopted radiotherapy regimen consisted of conventional fractionation of 1.8 or 2 Gy per fraction for a total dose of 66–72 Gy in the majority of cases (22/23 papers; no available data in two studies). Concurrent chemoradiotherapy was the most frequent treatment schedule in our analysis, being used in 1,562/2,009 patients (77.7%; no available information in only one study). Standard three-weekly 100 mg/m2 cisplatin was the chosen regimen in almost half of the included studies (11/24). Finally, a very small group of patients received induction chemotherapy before radiotherapy (181/2,223, 8.1%) in seven studies. On the basis that these studies were not excluded by our entry search criteria, they were retained in the analysis.
Table 4

Treatment-related features

Reference

Radiotherapy

Induction chemotherapy

Concurrent chemotherapy

Number of patients

Regimena

Dose (Gy)

Number of patients

Type of chemotherapy

No. of cycles (median)

Number of patients

Type of chemotherapy

No. of cycles (median)

3DCRT

IMRT

Total (median)

Per fraction

[18]

32

30

Conventional

70

2

20

Docetaxel/cisplatin

ns

35

Cisplatin 40 mg/m2 every 7 days

6

[19]

0

26

Conventional

70

1.8

0

Not administered

0

26

Cetuximab

ns

[20]

0

122

Conventional

70

2

0

Not administered

0

122

Cisplatin 100 mg/m2 every 21 days

ns

[21]

0

51

Conventional

70

ns

0

Not administered

0

51

Cisplatin 100 mg/m2 every 21 days

ns

[22]

ns

ns

Conventional

72

2

0

Not administered

0

44

Cisplatin 100 mg/m2 every 21 days

ns

[23]

ns

ns

Conventional

72

2

0

Not administered

0

68

Cisplatin 100 mg/m2 every 21 days

ns

[24]

0

88

Conventional

70

2

0

Not administered

0

74

Cisplatin + 5-fluorouracil

ns

[25]

0

70

Conventional

66

1.8

0

Not administered

0

70

Cisplatin 100 mg/m2 every 21 days

ns

[26]

ns

ns

Conventional

70

2

0

Not administered

0

70

Other

ns

[27]

0

108

Conventional

70

2

48

Docetaxel/cisplatin

2

44

Cisplatin + 5-fluorouracil

6

[28]

0

75

Conventional

70

2

0

TPF

1

75

Cisplatin 100 mg/m2 every 21 days

ns

[29]

ns

ns

ns

ns

ns

0

Not administered

ns

ns

ns

ns

[30]

40

0

Conventional

70

2

0

Not administered

0

35

Cisplatin 30 mg/m2 every 7 days

ns

[31]

0

69

Conventional

70

2

15

ns

ns

46

Cisplatin 40 mg/m2 every 7 days

ns

[32]

0

72

Conventional

70

2

15

ns

ns

40

Cisplatin 30 mg/m2 every 7 days

6

[34]

ns

ns

Conventional

66

2

0

Not administered

0

85

Other

6

[35]

5

42

Conventional

66

2

0

Not administered

0

47

Cisplatin + 5-fluorouracil

ns

[36]

0

86

Conventional

72

2

0

Not administered

0

86

Other

ns

[37]

ns

ns

Conventional

70

2

0

Not administered

0

47

Cisplatin + 5-fluorouracil

ns

[38]

0

287

Conventional

66

2

26

ns

ns

86

Other

ns

[39]

0

100

Accelerated

69.96

2.12

0

Not administered

0

100

Cisplatin 100 mg/m2 every 21 days

ns

[40]

ns

ns

ns

ns

ns

0

Not administered

0

74

Cisplatin 100 mg/m2 every 21 days

ns

[42]

0

125

Conventional

70

2

0

Not administered

0

125

Cisplatin 100 mg/m2 every 21 days

ns

[43]

0

40

Conventional

70.2

1.8

40

Docetaxel/cisplatin

3

36

Cisplatin 100 mg/m2 every 21 days

ns

[44]

0

76

Conventional

72

2

0

Not administered

0

76

Cisplatin 100 mg/m2 every 21 days

ns

ns not stated, 3DCRT 3-D conformal radiotherapy, IMRT intensity-modulated radiotherapy, TPF docetaxel, cisplatin, 5-fluorouracil

aConventional: conventional once-daily fractionation. Accelerated: accelerated fractionation

Prognostic impact of FDG PET: descriptive analysis

The timing of FDG PET was different among the studies included in the analysis (Table 5). A single baseline assessment time-point was present in almost half of the studies (12/25) while a combination of pretreatment, interim (during treatment) and posttreatment scans was described in four (pretreatment plus interim), seven (pretreatment plus posttreatment) and two (pretreatment plus interim plus posttreatment) studies. Among those studies providing data on more than a single time-point, a time-weighted analysis exploring changes over time (“delta”) of specific metabolic semiquantitative or qualitative features was additionally reported in seven. As a single variable, MTV and SUVmax were the main metabolic parameters addressed in nine and seven studies, respectively. A qualitative analysis was used in three studies [29, 31, 42]. Zschaeck et al. [44] determined SUVmean in irradiated normal mucosa tissue to explore the impact of off-target hypermetabolism and its change over time. Only a limited number of alternative prognostic biomarkers (comparators) were reported in parallel with the metabolic evaluation (nine studies). The median overall follow-up time for all studies was 23.6 months (range 15–55.8 months). In terms of threshold or cut-off values to discriminate worse from better outcomes, a large variability was observed for each intervention. Finally, a large heterogeneity characterized the prognostic information which could be extracted from each paper.
Table 5

Prognostic impact of FDG PET: descriptive analysis

Reference

Timing of FDG PET

Main metabolic parameter

Significant threshold/cut-off value

Prognostic comparator

Median follow-up (months)

Primary outcome measure

Main message

[18]

Pretreatment

MTV

14 ml

ns

18

LRC, PFS

3-year LRFS and DFS lower in patients with MTV ≥14 ml

[19]

Pretreatment

Interim

Posttreatment

Analysis of change over time

SUVmax

ns

ns

29.2

PFS, DSS

Metabolic response on posttreatment PET correlated with 2-year PFS and DSS

[20]

Pretreatment

MTV

ns

T stage, N stage, HPV status

30.5

LRC, OS

PET-based nomogram: MTV as a continuous variable correlated with 2-year OS

[21]

Pretreatment

Interim

Analysis of change over time

SUVmax

ns

ns

23

DFS, OS

SUVmax reduction ratio <0.64 associated with inferior 2-year OS and DFS

[22]

Pretreatment

TLG

121.9 g

Uniformity (texture), HPV status

27

PFS, DSS, OS

TLG >121.9 g and uniformity ≤0.138 associated with inferior PFS, DSS and OS

[23]

Pretreatment

MTV

40 ml

ns

ns

DFS

Worse short-term outcome and shorter DFS in patients with MTV >40 ml

[24]

Pretreatment

SUVmean

ns

ns

15

DFS

SUVmean >7 (median of cohort) correlated with inferior 2-year DFS

[25]

Pretreatment

Posttreatment

SUVmax

5

Haemoglobin level

38.4

LRC, OS

Posttreatment SUVmax <5 and pretreatment haemoglobin >12 g/dl correlated with superior LRC and OS

[26]

Pretreatment

Posttreatment

SUVmax

4.4

ns

52.7

PFS, OS

Posttreatment SUVmax <4.4 correlated with superior 3-year PFS and OS

[27]

Pretreatment

MTV, uptake pattern

20 ml

ns

36.4

DFS, DSS

MTV >20 ml and qualitative uptake pattern (ring-shape) correlated with inferior DFS and DSS

[28]

Pretreatment

Interim

Analysis of change over time

TLG, MTV

2.95 g/ml

ns

28

LRC, DFS, OS

Index node SUVmean on interim PET <2.95 g/ml and TLG, MTV reduction >50% on interim PET correlated with superior LRC, DFS and OS

[29]

Pretreatment

Posttreatment

Analysis of change over time

Hopkins five-point scale

ns

HPV status

27

PFS, OS

Hopkins five-point qualitative response interpretation and HPV status able to discriminate PFS and OS

[30]

Pretreatment

Posttreatment

Analysis of change over time

SUVmax

ns

ns

ns

LRC

SUVmax reduction ratio <1.04 associated with inferior LRC

[31]

Pretreatment

Interim

Posttreatment

Analysis of change over time

Visual grading

ns

ns

28

LRC, DFS, OS

Visual grading response interpretation able to discriminate 2-year LRC, DFS and OS

[32]

Pretreatment, Interim

TLG

9.4 g

ns

25

LRC, DFS, OS

TLG on interim PET <9.4 g correlated with superior 2-year LRC, DFS and OS

[34]

Pretreatment

MTV

28.7 ml

T stage

ns

LRC, OS

MTV >28.7 ml correlated with inferior 3-year LRC and OS

[35]

Pretreatment

Posttreatment

MTV

ns

ns

34

DFS, OS

Increase in MTV2.0 (volume above SUV threshold of 2) of 21 ml associated with inferior DFS and OS

[36]

Pretreatment

SUVmax

19.4

kep-tumour, ve-node (MR parameters)

28

PFS, OS

kep-tumour, ve-node, SUVmax independently able to discriminate 3-year PFS and OS

[37]

Pretreatment

MTV

18 ml

Primary tumour site

40.4

LRC, OS

MTV >18 ml correlated with inferior 3-year LRC and OS

[38]

Pretreatment

SUVmax

13

Gross tumour volume, cisplatin delivery, smoke

32

DFS, OS

SUVmax <13 (median of cohort) correlated with superior DFS and OS

[39]

Pretreatment

MTV

9.7 ml

ns

55.8

LRC, PFS, OS

MTV <9.7 ml (median of cohort) correlated with superior 5-year LRC, PFS and OS

[40]

Pretreatment

Posttreatment

MTV

ns

ns

50

LRC, PFS

Pretreatment MTV above the median correlated with inferior LRC and PFS

[42]

Pretreatment

Interim

Analysis of change over time

Hopkins five-point scale

ns

HPV status

20.4

PFS, OS

Hopkins five-point qualitative response interpretation and HPV status able to discriminate PFS and OS

[43]

Pretreatment

MTV

ns

ns

32.5

OS

MTV3.0 (volume above a SUV threshold of 3) >23.01 ml associated with inferior OS

[44]

Pretreatment

Interim

SUVmean (in MST)

ns

ns

29.3

LRC, OS

SUVmean of MST on interim PET >2.3 g/ml (median of cohort) correlated with superior LRC and OS

ns not stated, MST mucosa and submucosa soft tissues, kep efflux rate constant of primary tumour on dynamic contrast-enhanced MR imaging, ve relative volume of extracellular extravascular space of largest metastatic lymph node on diffusion-weighted MR imaging, LRC locoregional control, PFS progression-free survival, DSS disease-specific survival, OS overall survival, DFS disease-free survival

Prognostic impact of pretreatment FDG PET: meta-analysis

A total of 11 [20, 21, 22, 25, 34, 35, 36, 37, 40, 43, 45], 8 [20, 21, 22, 23, 35, 36, 38, 40] and 4 [21, 25, 37, 40] independent studies provided RR estimates for the association between baseline FDG PET and OS, PFS and LRC, respectively. These studies were thus included in the meta-analysis aiming to assess the potential prognostic impact of pretreatment metabolic features on patient outcome. Of note, Castelli et al. [45] performed a secondary analysis in the same patient population analysed in a previous work [20] providing additional data with distinct RR estimates that were therefore worthy of inclusion. Instead, the results reported by Zschaeck et al. [44] were not considered, since the prognostic value of uptake in mucosa soft tissue was not investigated in any of the other included studies. In terms of baseline FDG PET parameters, the analysis was limited to MTV and SUVmax, for which there were six and seven RR estimates for OS (Figs. 2 and 3), three and seven for PFS (Figs. 4 and 5), and two and three for LRC (Figs. 6 and 7), respectively. Higher MTV values for the primary or primary and nodal disease combined were significantly associated with a worse OS (SRR 1.86, 95% CI 1.08–3.21), PFS (SRR 1.81, 95% CI 1.14–2.89) and LRC (SRR 3.49, 95% CI 1.65–7.35), Instead, we found no statistically significant association between SUVmax and any of OS, PFS and LRC. Given the large between-study heterogeneity (I2 > 50%) that affected the summary measures, no effort was made to define a cumulative threshold value for an unfavourable prognosis. Finally, apart from an unclear or high risk of bias in terms of patient selection (21/25 studies, 84%) because of the predominantly retrospective nature of the included studies, according to the QUADAS-2 tool [46] the overall quality was good with low risks of bias and concerns regarding applicability in the remaining domains (Supplementary Table 6).
Fig. 2

Impact of pretreatment MTV on overall survival

Fig. 3

Impact of pretreatment SUVmax on overall survival

Fig. 4

Impact of pretreatment MTV on progression-free survival

Fig. 5

Impact of pretreatment SUVmax on progression-free survival

Fig. 6

Impact of pretreatment MTV on locoregional control

Fig. 7

Impact of pretreatment SUVmax on locoregional control

Discussion

In the era of precision oncology, the lack of prognostic biomarkers has hindered the evolution of standard-of-care management in HNSCC. Apart from HPV status, no molecular stratification is currently available for use in daily practice. In the last two decades, steady technological progress has highlighted the potential of imaging as a comprehensive tumour biomarker [47]. In the field of functional imaging, FDG PET is the most widespread, easily accessible modality that is able to provide surrogate metabolic information on tumour burden. The aim of our work was to define whether distinct FDG PET features can be intrinsically associated with prognostic relevance in the context of nonmetastatic HNSCC. We acknowledge several limitations which have to be taken into account when interpreting the data presented. First, most studies included in our systematic review were retrospective. Although a strict search methodology was followed, their potential heterogeneity in terms of patient selection, treatment administration and outcome measures may have affected the consistency of our analysis. Second, the technical variability in the performance of FDG PET scans is also a factor that cannot be ignored with a retrospective study design; only a prospective design can ensure that consensus acquisition recommendations [14] are rigorously adopted. Third, among the included studies the methods used to calculate the FDG PET metrics were not consistent. Heterogeneity in their definition has to be taken into account particularly for SUVmax and MTV, for which several threshold values were shown to be significant in discriminating patients with different outcomes.

Renewed interest in the role of FDG PET in the management of HNSCC was recently prompted by the publication of the PET-NECK trial [11]. The findings of this large prospective, multicentre phase 3 trial are practice-changing, since the study provided definitive evidence in favour of a response evaluation centred on the high negative predictive value (NPV) of a 12-week posttreatment FDG PET scan. However, the study had two main limitations that prevented the clarification of other relevant issues on the role of FDG PET in the management of HNSCC. First, none of the 564 patients enrolled in the trial underwent a baseline FDG PET scan; a qualitative comparison between pretreatment and posttreatment scans was therefore not performed. Second, FDG PET semiquantitative metrics could not be evaluated due to nonuniform calibration among the different scanners. From this perspective, the PET-NECK trial did not add any new data to the available low-level body of evidence on the prognostic role of specific FDG PET semiquantitative and qualitative features in HNSCC. Although many investigators have focused on this topic in the last 15 years [48], the literature is characterized by inconclusive and heterogeneous findings [49].

A crucial aspect that needs again to be underlined is the strict dependence of FDG PET information on the image acquisition modality, which in turn may be influenced by a series of factors, ranging from the technical parameters of the scanner to the timing of the scan with respect to treatment. As also demonstrated in our descriptive analysis (Table 5), there is significant variability in the correlation between semiquantitative metrics and outcome measures in HNSCC. We have already pointed out that in the posttreatment scenario a negative PET scan at 12 weeks after chemoradiation is a prognostic biomarker of long-term complete remission based on level 1 evidence. However, standardized interpretation of response to treatment is lacking. In this context, the Hopkins criteria are the only proposed scoring system for qualitative interpretation of FDG PET in HNSCC. Marcus et al. [29] showed that a five-point scale based on prespecified qualitative descriptors is accurate in discriminating complete from incomplete responses. The application of the Hopkins criteria resulted in a high NPV of 91.1% with an overall diagnostic accuracy of 86.9%. Notably, the results of the ECLYPS study [42] prospectively confirmed the reliability of the Hopkins criteria applied 12 weeks after the end of treatment, with an overall NPV of 92.1% and a very low number of equivocal reports. As accurately described by Garibaldi et al. [50] in a recent systematic review, the potential prognostic and predictive relevance of an interim FDG PET scan (scan acquired during treatment) is a controversial matter. At present, no firm conclusions can be drawn as to the ideal metabolic parameter to analyse early in treatment, the most informative threshold value, or the best time to re-scan the patient.

Taking all together, the use of FDG PET in patients with HNSCC provides prognostic information through standardized qualitative assessment at a minimum of 12 weeks after chemoradiation, but no added value during its delivery. It is therefore a rational approach to investigate before treatment whether baseline semiquantitative metrics are intrinsically able to characterize the outcome in patients with locally advanced disease. Conflicting evidence is available from the literature. Pak et al. [51] performed a systematic review and meta-analysis of 13 studies (1,180 patients) to assess the prognostic role of MTV and TLG before treatment. The authors found that high values of both volumetric parameters correlated significantly with a worse outcome. The pooled HRs for OS were 3.51 (95% CI 2.62–4.72, p < 0.00001) and 3.14 (95% CI 2.24 – 4.40, p < 0.00001) for MTV and TLG, respectively. However, the generalizability of these results is open to question. First, loose criteria were followed in the literature search strategy and inclusion of articles. Second, for both parameters no threshold values portending a worse outcome were defined, thus preventing further analysis of the data.

In a prospective study in 77 patients affected by stage II–IV HNSCC, Schinagl et al. [52] consistently applied five different segmentation methods for coregistered CT and FDG PET scans at baseline. Among the different metrics obtained, only the gross tumour volume (GTV) visually delineated on FDG PET images was significantly correlated with outcome in oral cavity and oropharyngeal tumours, while all isocontour-based volumes, SUVmean and SUVmax, were not. A large single-centre [38] retrospective study on 287 patients receiving IMRT-based treatment showed different results. In a univariate analysis, increasing values of SUVmax (as a logarithmic variable) yielded a HR of 1.72 (95% CI 1.34–2.19) for a worse disease-free survival (DFS) and OS. Multivariate analysis showed an additive effect of increasing GTV (HR 1.74, 95% CI 1.33–2.27; increase in interquartile range from 25% to 75% corresponding to an increase in GTV from 27.4 cm3 to 95.8 cm3) and increasing SUVmax (HR 1.34, 95% CI 1.01–1.77; increase in interquartile range from 25% to 75% corresponding to an increase in SUVmax from 9.6 to 16.8) for a worse prognosis.

The link between FDG avidity and tumour volume has been further explored by different groups focusing on MTV. In this regard, the correlative, prospective imaging study of the randomized phase 3 RTOG 0522 trial [40] is noteworthy. Of the whole sample of 940 patients enrolled, 74 from 19 different centres provided both pretreatment and posttreatment FDG PET scans, as mandated upon inclusion. A prespecified acquisition imaging protocol was followed in all patients. Excellent centralized interobserver agreement (intraclass correlation coefficient ≥0.80) on semiquantitative metrics was reported. Based on voxels with a minimum of 40% SUVmax, baseline primary MTV above the median was the strongest prognosticator of worse LRC (HR 4.01, 95% CI 1.28–12.52, p = 0.2). Other retrospective studies [23, 27, 37] have underlined the prognostic value of baseline MTV, reporting different cut-off values as most significant for a worse outcome (combined primary and nodal MTV >40 ml, >20 ml and >18 ml correlating with worse DFS [23], LRC and OS [27], and disease-specific survival [37], respectively). The prognostic value of MTV analysed as a continuous variable has also been reported.

In a single-centre retrospective analysis in 83 patients, Tang et al. [53] found that an increase in primary baseline MTV of 17 ml (from the 25th to the 75th percentile) was associated with a doubling of the risk of disease progression (p = 0.0002) and of death (p = 0.0048). Of note, combined primary and nodal MTV (as a continuous variable) was also associated with a shorter PFS (HR 4.23, p < 0.0001; CI not reported) and OS (HR 3.21, p < 0.0029; CI not reported) in the subgroup of 64 patients with p16-positive oropharyngeal cancer. In a larger cohort of 122 patients with oropharyngeal cancer, Castelli et al. [45] assessed whether the use of different absolute and relative thresholds of SUVmax result in different discriminatory power of MTV. Using a 51% relative SUVmax threshold, combined primary and nodal MTV was the only significant factor in a multivariate analysis predicting OS (HR 1.43 per 10 ml, CI 1.23–1.65, p < 0.001) and DFS (HR 1.43 per 10 ml;,CI 1.23–1.65, p = 0.03). The optimal cut-off value for MTV 51% was 22.7 ml, which was able to discriminate 2-year DFS with rates of 63.3% versus 32.9% and LRC with rates of 68% versus 35.3%.

The absence of a consensus methodology on VOI delineation is clearly a limitation when comparing different datasets on the prognostic relevance of MTV, since no single optimal cut-off value is recognized. In line with previous experience, our data reinforce the prognostic role of pretreatment MTV as the most informative semiquantitative metabolic feature. In line with our search inclusion criteria, the patient population analysed was extremely homogeneous (about 95% of the whole sample size) in terms of disease stage, radiotherapy technique used and schedule of concomitant chemoradiotherapy. With all due limitations, our analysis provides further evidence on the predominant impact of pretreatment MTV on HNSCC outcome compared with all other available FDG PET metrics. Further consideration of its role also as a predictive biomarker may be generated by pattern-of-failure data correlating baseline FDG PET and radiation dose distribution in HNSCC. Due et al. [54] performed a retrospective analysis in 304 HNSCC patients with the aim of correlating the pattern of disease failure with FDG uptake on pretreatment PET scans. By performing a deformable registration of CT scans acquired at the time of recurrence with the planning PET/CT scan, the authors showed that 96% of relapses (95% CI 86–99%) occurred in the high-dose region. In addition, they found that recurrence density was higher in the central part of the target volume (p < 0.0001), with a significant correlation with increasing FDG avidity (p = 0.036). In a smaller cohort of 44 patients enrolled in a prospective phase 2 trial, Leclerc et al. [55] showed that all ten recurrences arose in areas receiving >95% of the dose determined on PET-based plans. A similar finding was reported by Mohamed et al. [56], who hypothesized that a 1-cm margin in addition to the 50% SUVmax isocontour on pretreatment FDG PET scans would cover the majority of type A recurrences (according to the authors’ definition, those that arise in the central high-dose area).

Once again, it has to be underlined that, among others, the main limitations of FDG in HNSCC are its suboptimal specificity and the large variability in segmentation methods. Potentially, it could be hypothesized that hypoxia PET [57] and diffusion-weighted magnetic resonance imaging [58] may be more refined imaging biomarkers in the field of HNSCC. However, conclusive results on their prognostic impact have long been awaited, mainly due to the lack of reproducibility and cost issues preventing their adoption on a large scale. In our opinion FDG PET will remain the most widespread functional imaging modality used in clinical practice for many years to come.

Conclusion

The absence of prognostic biomarkers is a critical limitation in the management of locally advanced HNSCC. With all due limitations, our analysis showed that MTV defined from pretreatment FDG PET scans has the strongest impact on patient outcome after standard concurrent chemoradiotherapy. Prospective studies to corroborate this finding through standardized FDG PET acquisition and segmentation methods are warranted.

Notes

Acknowledgements

The publication of this article was supported by funds of the European Association of Nuclear Medicine (EANM).

Funding

None.

Compliance with ethical standards

Conflicts of interest

None.

Ethical approval

This article does not describe any studies with human participants performed by any of the authors.

Supplementary material

259_2018_4065_MOESM1_ESM.docx (68 kb)
ESM 1 (DOCX 68 kb)
259_2018_4065_MOESM2_ESM.docx (175 kb)
ESM 2 (DOCX 174 kb)
259_2018_4065_MOESM3_ESM.docx (21 kb)
ESM 3 (DOCX 21 kb)

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© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Radiation OncologyAzienda Ospedaliero – Universitaria Careggi, University of FlorenceFlorenceItaly
  2. 2.Radiation OncologyAzienda Ospedaliera S.Croce e CarleCuneoItaly
  3. 3.Nuclear Medicine DepartmentAzienda Ospedaliera S.Croce e CarleCuneoItaly
  4. 4.Radiation Oncology DepartmentOspedale Policlinico San MartinoGenoaItaly
  5. 5.Department of Oncology, Radiation OncologyUniversity of TurinTurinItaly
  6. 6.Department of Radiotherapy and RadiosurgeryHumanitas Cancer Center and Research HospitalRozzanoItaly
  7. 7.Radiotherapy 2 UnitFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
  8. 8.Cancer Risk Factors and Lifestyle Epidemiology UnitCancer Research and Prevention Institute (ISPO)FlorenceItaly

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