The role of 18F-FDG PET/CT-based quantitative metabolic parameters in patients with ovarian clear cell carcinoma
Abstract
BACKGROUND:
Ovarian clear cell carcinoma (CCC) is enriched in genes associated with glucose metabolism.
OBJECTIVE:
To evaluate the
METHODS:
We measured quantitative parameters including maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG).
RESULTS:
A total of 22 patients were included. PET/CT-based metabolic parameters were calculated for 20 patients because two had low glucose-uptake tumor. The median SUVmax was 7.25 (range 2.50–14.80). Spearman’s correlation test revealed that the level of pre-operative serum cancer antigen 125 (CA 125) correlated significantly with MTV (
CONCLUSIONS:
List of abbreviations
CCC: | clear cell carcinoma |
SUVmax: | maximum standardized uptake value |
MTV: | metabolic tumor volume |
TLG: | Total lesion glycolysis |
FIGO: | The International Federation of Gynecology and Obstetrics |
CRS: | Cytoreductive surgery |
PFS: | Progression-free survival |
OS: | Overall survival |
CA125: | Cancer antigen 125 |
1.Background
Ovarian clear cell carcinoma (CCC), as a subtype of epithelial ovarian cancer, has distinct morphologic and biologic features [1, 2]. Ovarian CCC patients tend to have worse survival when compared to the more common serous counterpart [1, 3]. Resistance to platinum-based chemotherapy might partly be responsible for the grave survival outcome [3, 4]. Researchers have been focused on the study of genomic landscape of ovarian CCC [5, 6, 7, 8], hoping to shed light on the underlying mechanism and possible treatment target. Colleagues from Japan identified a gene expression profile characteristic of ovarian CCC, which is enriched in genes associated with stress response and glucose metabolism [5]. According to a review article with profound influence, reprogramming energy metabolism especially glucose metabolism is considered as an emerging hallmark of cancer [9].
In the current study, we specifically evaluated the role of PET/CT-based variables in patients with ovarian CCC. The associations between metabolic parameters and clinicopathologic features including survival outcome were further investigated.
2.Methods
2.1Patients
The study was approved by the institutional review board and the requirement for the written informed consent was waived due to its retrospective design. We included all the patients with ovarian CCC who received
All the patients were staged by The International Federation of Gynecology and Obstetrics (FIGO) staging system [22]. Patients with early stage disease (FIGO I+II) underwent complete staging surgery, while those with late stage tumor (FIGO III+IV) received debulking surgery. Optimal cytoreductive surgery (CRS) was defined as residual disease less than (or including) 1 cm after primary debulking. Platinum-based chemotherapy was routinely administered after primary surgery. Patients were considered to have platinum-sensitive disease if the interval time was
2.218 F-FDG PET/CT protocol and image analysis
The
2.3Statistic analysis
Statistical Package for Social Science (SPSS) (Version 20.0, SPSS, Inc., Chicago, IL, USA) and GraphPad Prism (Version 6.0, GraphPad Software, Inc., La Jolla, CA, USA) were used for the analyses. Parametric Student’s t-tests were used in evaluating continuous variables, while chi-square tests for the categorical ones. Spearman correlation was applied in comparison between serum cancer antigen 125 (CA125) level and PET/CT-based parameters. Kaplan-Meier model and log-rank test were employed for univariate analysis of survival outcome. Multivariate analysis was not conducted due to the small sample size. All P values reported were two tailed, and
3.Result
3.1Patient characteristics
A total of 22 ovarian CCC patients were included into the study. Table 1 presents the clinicopathologic characteristics of the patients. Nine patients (40.9%) had early-stage disease (FIGO I
3.2Quantitative metabolic parameters
Among 22 patients, two presented with low glucose-uptake tumor. Therefore, PET/CT-based metabolic parameters were calculated for a total of 20 patients, which is demonstrated in Table 1. The mean SUVmax was 7.25 (range 2.50–14.80).
Table 1
Patient characteristics (
Variables | |
Age (years), median (range) | 52 (28–83) |
FIGO stage (%) | |
Early (I+II) | 9 (40.9%) |
Advanced (III+IV) | 13 (59.1%) |
Serum CA 125 (U/mL), median (range) | 162.1 (15.0–5000) |
Residual disease (%) | |
| 18 (81.8%) |
| 4 (18.2%) |
Follow up time (months), mean (range) | 20 (1–73) |
Disease recurrence (%) | |
Without recurrence | 14 (63.6%) |
With recurrence | 8 (36.4%) |
Platinum response (%)* | |
Sensitive | 14 (66.7%) |
Resistant | 7 (33.3%) |
Disease status at last follow up | |
Dead | 6 (27.3%) |
Alive with disease | 2 (9.1%) |
Alive without disease | 14 (63.6%) |
Metabolic parameters | |
SUVmax (g/mL), median (range) | 7.25 (2.50–14.80) |
MTV (mL), median (range) | 53.28 (0.09–668.77) |
TLG (g), median (range) | 197.85 (0.23–3156.59) |
MTV40 (mL), median (range) | 54.11 (4.16–168.06) |
TLG40 (g), median (range) | 215.07 (16.27–1027.57) |
MTV50 (mL), median (range) | 31.01 (2.81–85.54) |
TLG50 (g), median (range) | 144.76 (12.17–480.73) |
MTV60 (mL), median (range) | 14.60 (1.08–43.34) |
TLG60 (g), median (range) | 85.70 (4.22–271.74) |
3.3Associations between clinicopathologic variables and metabolic parameters
We further evaluated the relationship between clinicopathologic variables and metabolic parameters. The level of pre-operative serum CA 125 correlated significantly with MTV (
3.4Prediction of survival outcome
Lastly, we investigated the possible predictors for patients’ survival and the results were shown in Table 2. The following variables including stage, residual disease, platinum sensitivity and MTV50 were significant for both PFS and OS on univariate analysis. In addition, four metabolic parameters (MTV40, TLG40, TLG50 and TLG60) were significantly associated with patients’ overall survival. Surprisingly, ovarian CCC patients with higher level of volumetric parameters (MTV/TLG) tended to have better survival. Multivariate analysis was not conducted due to the small sample size.
Table 2
Univariate analysis for survival outcome (
Variables | Category | ||
Progression-free survival | Overall survival | ||
Stage | Early vs. Late | 0.010 | 0.038 |
CA-125 | 0.060 | 0.037 | |
Residual disease |
| 0.045 | |
Platinum sensitivity | Resistant vs. Sensitive |
| 0.001 |
SUVmax | 0.408 | 0.751 | |
MTV | 0.369 | 0.856 | |
TLG | 0.408 | 0.751 | |
MTV40 | 0.057 | 0.035 | |
TLG40 | 0.274 | 0.024 | |
MTV50 | 0.042 | 0.034 | |
TLG50 | 0.274 | 0.024 | |
MTV60 | 0.274 | 0.221 | |
TLG60 | 0.274 | 0.024 |
Abbreivations: SUVmax
4.Discussion
In our previous study, we assessed the prognostic roles of PET/CT-based metabolic parameters in patients with recurrent ovarian CCC [23]. The present work might be the first study, to the best of our knowledge, on the clinical utility of quantitative metabolic parameters measured on PET/CT in patients with primary ovarian CCC. However, the most significant limitation is the small sample size of the patients. Firstly, disease rarity might be partly the reason. According to a previous publication from our institution, a total of 122 ovarian CCC patients underwent primary surgery between 1999 and 2014 [24]. Secondly, a significant proportion (57–81%) of the patients have early-stage disease and present with a large pelvic mass [1]. In this circumstance,
Table 3
A review of the studies focusing on the predictive value of PET/CT-based volumetric parameters in ovarian cancer
Authors | Metabolic parameters | MTV threshold | Sample size | Main findings |
---|---|---|---|---|
Chung et al. 2012 | SUVmax, SUVavg, MTV, TLG | 40% SUVmax | MTV and TLG were significant for PFS | |
Liao et al. 2013 | SUVmax, MTV, TLG | SUV | TLG obtained from background method was significant for OS | |
Konishi et al. 2014 | SUVmax | Not applicable | SUVmax was significant for 5-year survival | |
Lee et al. 2014 | SUVmax, MTV, TLG | 40% SUVmax | TLG was significant for PFS | |
Lee et al. 2015 | SUVmax, SUVavg, MTV, TLG, IFH | 40% SUVmax | Preoperative IFH was significantly associated with recurrence | |
Yamamoto et al. 2016 | SUVmax, MTV, TLG | 40% SUVmax | MTV correlated with CA125; TLG correlated with SUVmax and CA125; TLG was significant for PFS | |
Gallicchio et al. 2017 | SUVmax, MTV, TLG | 42% SUVmax | MTV was significant for OS (positive predictor) | |
Liu et al. 2018 | SUVmax, SUVavg, MTV | SUV | A higher SUVmax level was associated with chemosensitivity | |
Ye et al. 2019 | SUVmax, MTV, TLG | SUV | TLG60 was negative predicators of OS |
Abbreviations: SUVmax
Publications assessing the clinical utility of
In the present study we found that pre-operative serum CA 125 level correlated significantly with MTV and TLG. Volume-based metabolic parameters were significant for overall survival (MTV40, TLG40, MTV50, TLG50 and TLG60) and progression-free survival MTV50) on univariate analysis. However, ovarian CCC patients with higher level of volumetric parameters (MTV/TLG) tended to have better survival, which was inconsistent from some of the previous publications [11, 14, 17, 23]. It is noteworthy that in our study, patients who were platinum-sensitive had relatively higher level of volumetric metabolic parameters, though not statistically significant. It still needs to be investigated whether or not it is related to the survival analysis. Table 3 presents a very brief summary of the relevant literatures, which include all histologic subtypes. Gallicchio et al evaluated 31 ovarian cancer patients who underwent PET/CT after surgery [18]. They found that patients with higher MTV (42% threshold) had a significantly higher OS [18]. Our previous study, including 56 cases of high-grade serous carcinoma, also presented that higher FDG uptake was associated with better survival [19]. Several possible reasons might be suggested for the inconsistent conclusions: 1) Different patient population. As clearly seen from Table 3, most published works included various kinds of histology, whereas only ovarian CCC was involved in our study; 2) Different thresholds for MTV delineations. No consensus has ever been achieved when it comes to the selection of volume of interest in delineating tumor MTV.
As aforementioned, a more accurate and sufficient conclusion awaits future studies. First and foremost, given the disease rarity, a multicenter study should be designed to ensure a relatively large sample size. PET/CT images could be collected and sent to an experienced nuclear medicine physician to minimize inter-observer bias. Besides, both primary and recurrent cases should be included given the inconsistent findings of our study. If possible, the metabolic parameters in primary disease presentation and tumor recurrence pertaining to the same patient might be evaluated to investigate the possible changes in tumor progression.
5.Conclusions
PET/CT-based metabolic volumetric parameters might be predicators for survival in ovarian CCC patients. More patients should be included in further study.
Supplementary data
The supplementary files are available to download from http://dx.doi.org/10.3233/CBM-190904.
Acknowledgments
This study was funded by National Natural Science Foundation of China (81702558) and Fudan University Shanghai Cancer Center (YJ201603). The funding bodies didn’t participate in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Conflict of interest
The authors declare that they have no conflict of interest.
References
[1] | M.S. Anglesio, M.S. Carey, M. Kobel, H. Mackay and D.G. Huntsman, Clear cell carcinoma of the ovary: A report from the first Ovarian Clear Cell Symposium, June 24th, 2010, Gynecologic Oncology 121: ((2011) ), 407–15. |
[2] | M.G. del Carmen, M. Birrer and J.O. Schorge, Clear cell carcinoma of the ovary: A review of the literature, Gynecologic Oncology 126: ((2012) ), 481–90. |
[3] | S. Ye, J. Yang, Y. You, D. Cao, H. Huang, M. Wu, J. Chen, J. Lang and K. Shen, Comparison of clinical characteristic and prognosis between ovarian clear cell carcinoma and serous carcinoma: A 10-year cohort study of chinese patients, PloS One 10: ((2015) ), e0133498. |
[4] | H. Itamochi, J. Kigawa and N. Terakawa, Mechanisms of chemoresistance and poor prognosis in ovarian clear cell carcinoma, Cancer Science 99: ((2008) ), 653–8. |
[5] | K. Yamaguchi, M. Mandai, T. Oura, N. Matsumura, J. Hamanishi, T. Baba, S. Matsui, S.K. Murphy and I. Konishi, Identification of an ovarian clear cell carcinoma gene signature that reflects inherent disease biology and the carcinogenic processes, Oncogene 29: ((2010) ), 1741–52. |
[6] | T. Okamoto, M. Mandai, N. Matsumura, K. Yamaguchi, H. Kondoh, Y. Amano, T. Baba, J. Hamanishi, K. Abiko, K. Kosaka, S.K. Murphy, S. Mori and I. Konishi, Hepatocyte nuclear factor-1beta (HNF-1beta) promotes glucose uptake and glycolytic activity in ovarian clear cell carcinoma, Molecular Carcinogenesis 54: ((2015) ), 35–49. |
[7] | S.I. Kim, J.W. Lee, M. Lee, H.S. Kim, H.H. Chung, J.W. Kim, N.H. Park, Y.S. Song and J.S. Seo, Genomic landscape of ovarian clear cell carcinoma via whole exome sequencing, Gynecologic Oncology ((2017) ). |
[8] | R. Murakami, N. Matsumura, J.B. Brown, K. Higasa, T. Tsutsumi, M. Kamada, H. Abou-Taleb, Y. Hosoe, S. Kitamura, K. Yamaguchi, K. Abiko, J. Hamanishi, T. Baba, M. Koshiyama, Y. Okuno, R. Yamada, F. Matsuda, I. Konishi and M. Mandai, Exome sequencing landscape analysis in ovarian clear cell carcinoma shed light on key chromosomal regions and mutation gene networks, The American Journal of Pathology 187: ((2017) ), 2246–58. |
[9] | D. Hanahan and R.A. Weinberg, Hallmarks of cancer: the next generation, Cell 144: ((2011) ), 646–74. |
[10] | A. Amit, J. Schink, A. Reiss and L. Lowenstein, PET/CT in gynecologic cancer: present applications and future prospects – a clinician’s perspective, Obstetrics and Gynecology Clinics of North America 38: ((2011) ), 1–21, vii. |
[11] | H.H. Chung, H.W. Kwon, K.W. Kang, N.H. Park, Y.S. Song, J.K. Chung, S.B. Kang and J.W. Kim, Prognostic value of preoperative metabolic tumor volume and total lesion glycolysis in patients with epithelial ovarian cancer, Annals of Surgical Oncology 19: ((2012) ), 1966–72. |
[12] | S. Liao, X. Lan, G. Cao, H. Yuan and Y. Zhang, Prognostic predictive value of total lesion glycolysis from 18F-FDG PET/CT in post-surgical patients with epithelial ovarian cancer, Clinical Nuclear Medicine 38: ((2013) ), 715–20. |
[13] | H. Konishi, K. Takehara, A. Kojima, S. Okame, Y. Yamamoto, Y. Shiroyama, T. Yokoyama, T. Nogawa and Y. Sugawara, Maximum standardized uptake value of fluorodeoxyglucose positron emission tomography/computed tomography is a prognostic factor in ovarian clear cell adenocarcinoma, International Journal of Gynecological Cancer: Official Journal of the International Gynecological Cancer Society 24: ((2014) ), 1190–4. |
[14] | J.W. Lee, A. Cho, J.H. Lee, M. Yun, J.D. Lee, Y.T. Kim and W.J. Kang, The role of metabolic tumor volume and total lesion glycolysis on (1)(8)F-FDG PET/CT in the prognosis of epithelial ovarian cancer, European Journal of Nuclear Medicine and Molecular Imaging 41: ((2014) ), 1898–906. |
[15] | C.Y. Kim, S.Y. Jeong, G.O. Chong, S.H. Son, J.H. Jung, D.H. Kim, S.W. Lee, B.C. Ahn and J. Lee, Quantitative metabolic parameters measured on F-18 FDG PET/CT predict survival after relapse in patients with relapsed epithelial ovarian cancer, Gynecologic Oncology 136: ((2015) ), 498–504. |
[16] | H.A. Vargas, I.A. Burger, D.A. Goldman, M. Micco, R.E. Sosa, W. Weber, D.S. Chi, H. Hricak and E. Sala, Volume-based quantitative FDG PET/CT metrics and their association with optimal debulking and progression-free survival in patients with recurrent ovarian cancer undergoing secondary cytoreductive surgery, European Radiology 25: ((2015) ), 3348–53. |
[17] | M. Yamamoto, T. Tsujikawa, Y. Fujita, Y. Chino, T. Kurokawa, Y. Kiyono, H. Okazawa and Y. Yoshida, Metabolic tumor burden predicts prognosis of ovarian cancer patients who receive platinum-based adjuvant chemotherapy, Cancer Science 107: ((2016) ), 478–85. |
[18] | R. Gallicchio, A. Nardelli, A. Venetucci, D. Capacchione, A. Pelagalli, C. Sirignano, P. Mainenti, P. Pedicini, G. Guglielmi and G. Storto, F-18 FDG PET/CT metabolic tumor volume predicts overall survival in patients with disseminated epithelial ovarian cancer, European Journal of Radiology 93: ((2017) ), 107–13. |
[19] | S. Liu, Z. Feng, Z. Jiang, H. Wen, J. Xu, H. Pan, Y. Deng, L. Zhang, X. Ju, X. Chen and X. Wu, Prognostic predictive value of preoperative intratumoral 2-deoxy-2-(18F)fluoro-D-glucose uptake heterogeneity in patients with high-grade serous ovarian cancer, Nuclear Medicine Communications ((2018) ). |
[20] | S. Liu, Z. Feng, H. Wen, Z. Jiang, H. Pan, Y. Deng, L. Zhang, X. Ju, X. Chen and X. Wu, (18)F-FDG PET/CT can predict chemosensitivity and proliferation of epithelial ovarian cancer via SUVmax value, Japanese Journal of Radiology ((2018) ). |
[21] | F.Y. Liu, A. Chao, C.H. Lai, H.H. Chou and T.C. Yen, Metabolic tumor volume by 18F-FDG PET/CT is prognostic for stage IVB endometrial carcinoma, Gynecologic Oncology 125: ((2012) ), 566–71. |
[22] | FIGO staging for carcinoma of the vulva, cervix, and corpus uteri, International Journal of Gynaecology and Obstetrics: The Official Organ of the International Federation of Gynaecology and Obstetrics 125: ((2014) ), 97–8. |
[23] | S. Ye, S. Liu, L. Xiang, X. Wu and H. Yang, (18)F-FDG PET/CT-based metabolic metrics in recurrent tumors of ovarian clear cell carcinoma and their prognostic implications, BMC Cancer 19: ((2019) ), 226. |
[24] | J. Zhu, H. Wen, R. Bi, Y. Wu and X. Wu, Prognostic value of programmed death-ligand 1 (PD-L1) expression in ovarian clear cell carcinoma, Journal of Gynecologic Oncology 28: ((2017) ), e77. |
[25] | Y. Tanizaki, A. Kobayashi, M. Shiro, N. Ota, R. Takano, Y. Mabuchi, S. Yagi, S. Minami, M. Terada and K. Ino, Diagnostic value of preoperative SUVmax on FDG-PET/CT for the detection of ovarian cancer, International Journal of Gynecological Cancer: Official Journal of the International Gynecological Cancer Society 24: ((2014) ), 454–60. |
[26] | W. Yu, X.L. Fu, Y.J. Zhang, J.Q. Xiang, L. Shen, G.L. Jiang and J.Y. Chang, GTV spatial conformity between different delineation methods by 18FDG PET/CT and pathology in esophageal cancer, Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology 93: ((2009) ), 441–6. |