|Year : 2020 | Volume
| Issue : 1 | Page : 187
Early detection of gestational trophoblastic neoplasia based on serial measurement of human chorionic gonadotrophin hormone in women with molar pregnancy
Roya Riahi1, Abbas Rahimiforoushani1, Keramat Nourijelyani1, Nooshin Akbari Sharak2, Mahmood Bakhtiyari3
1 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
2 Student Research Committee, Department of Biostatistics and Epidemiology, School of Public Health Mashhad University of Medical Sciences, Mashhad, Iran
3 Non-Communicable Diseases Research Center, School of Medicine; Department of Community Medicine, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran
|Date of Submission||05-Aug-2019|
|Date of Acceptance||26-Sep-2019|
|Date of Web Publication||11-Dec-2020|
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran
Source of Support: None, Conflict of Interest: None
Background: The majority of studies which investigate the predicted power of Human chorionic gonadotropin (hCG) levels to the occurrence of Gestational trophoblastic neoplasia (GTN) considered the effect of a single measurement of hCG or used classical statistical methods without considering the endogenous marker. The aim of this study is to investigate the association between weekly measurements of β-hCG with time to GTN occurring, using a robust Bayesian joint modeling. Methods: Data of 201 women with a molar pregnancy were considered for this retrospective cohort study. After the first measurement of β-hCG in 48 hours post evacuation of mole, the other titration was performed on a weekly basis until three consecutive normal titers. The association between serial measurements of β-hCG and risk of GTN occurring were assessed by the classic and Bayesian joint modeling and in separate analysis the mixed linear effect and Cox-PH model were used. Results: The mean age (SD) of participants was 26.6 (6.55) year. The GTN was occurred among 14.9% of patients. The association parameter using Bayesian approach was estimated as 1.30 (95% CI: 0.44 to 2.20) which showed one unit increase in the log β-hCG corresponds to the 2.80-times increase in the hazard for the occurrence of GTN (Hazard Ratio: 2.80, 95% CI: 1.55 to 8.98). Conclusions: Findings of this study revealed that weekly measurements of β-hCG are an important and reliable biomarker to early detection of developing of molar pregnancy to persistent GTN.
Keywords: Chorionic gonadotropin, gestational trophoblastic disease, Iran
|How to cite this article:|
Riahi R, Rahimiforoushani A, Nourijelyani K, Sharak NA, Bakhtiyari M. Early detection of gestational trophoblastic neoplasia based on serial measurement of human chorionic gonadotrophin hormone in women with molar pregnancy. Int J Prev Med 2020;11:187
|How to cite this URL:|
Riahi R, Rahimiforoushani A, Nourijelyani K, Sharak NA, Bakhtiyari M. Early detection of gestational trophoblastic neoplasia based on serial measurement of human chorionic gonadotrophin hormone in women with molar pregnancy. Int J Prev Med [serial online] 2020 [cited 2021 Jul 30];11:187. Available from: https://www.ijpvmjournal.net/text.asp?2020/11/1/187/303214
| Introduction|| |
Gestational Trophoblastic Neoplasia (GTN) is a collective term which refers to gestational trophoblastic diseases such as placental site trophoblastic tumor, invasive moles, and choriocarcinoma. GTN originates from abnormal multiplication of trophoblast after any gestation, especially molar pregnancy.
Molar pregnancy is a precancerous form of GTN. The incidence of molar pregnancy in Asia (2 per 1000 pregnancy) is three times higher than North America and Europe (0.6-1.1 per 1000 pregnancy). In Iran, the incidence was 7 per 1000 pregnancy (0.7%). Approximately about 15–20% of complete mole and 5% of the partial mole will continue to develop GTN., All women with molar pregnancy should be managed with uterine evacuation and chemotherapy due to their high risk of developing GTN. Although GTN is a curable disease the diagnosis in early stages is important in preventing the spread of it and choosing the less complex and expensive treatment method.,
Some previous studies have focused on the prediction of GTN based on the measurements of β-hCG level over time and showed that regression pattern for patients who were recovering was different from patients go on to have persistence GTN.,,,
Human chorionic gonadotropin (hCG) is a hormone produced by trophoblastic tissue and comprised of two subunits the alpha and beta. Serial measurements of β subunit of hCG (β-hCG) can effectively detect persistence GTN post-molar pregnancy., Most of the previous studies used general linear model, survival models,, or longitudinal ROC analysis., While, the values of the β-hCG biomarker at any time point can be affected by disease progression and occurrence of GTN at an earlier time point and this important characteristic of the endogenous biomarker may be ignored in these methods. Therefore, we aimed to investigate the association between longitudinal measurements of β-hCG titration and time to post-molar GTN occurring using the Bayesian joint modeling which takes into accounts the endogenous feature of β-hCG titration over time.
| Methods|| |
The data for this retrospective cohort study were collected from Imam Hossain, Shohada, Mahdieh, and Taleghani hospitals in Tehran provenience, from 2003 to 2013. All pregnant women with molar pregnancy were eligible for this study. Therefore, out of a total of 98,658 deliveries in the hospitals, 221 cases of molar pregnancy were identified; of these 20 cases were excluded owing to receiving coprophlaxi drugs, having had initial hysterectomy treatments, or having incomplete files with irrelevant information, respectively. Finally, data from 201 documents of women with molar pregnancy were considered for this study.
The longitudinal outcome was β-hCG titration at four different visiting times during to one-year follow-up post molar pregnancy. The first titer of β-hCG was measured at most 48 hours after evacuation of mole and other titrations were performed on a weekly basis until three consecutive normal titers in all patients. The sensitive and specific RIAs procedure was used to measurements β-hCG. The details of this procedure were described elsewhere. The survival outcome was time to occurrence of GTN which was measured as the number of days between molar pregnancy evacuating and occurrence of GTN. Time to occurrence of GTN was censored for pregnant women who were lost to follow-up or experienced hysterectomy surgery during follow-up or did not the event at the end of the study. Demographic and general characteristics including age (year), race, gestational age (week), vaginal bleeding (VB) (yes/no), parity, gravidity, history of abortion (yes/no), uterine height (week), and theca lutein cyst (yes/no) were considered as covariate for the separate survival and longitudinal model as well as joint model.
Statistical analysis and model specification
Categorical variables were presented as frequency (percent) and continuous variables as mean (standard deviation). All parameters were considered significant if corresponding 95% confidence interval (CI) did not include zero. The backward elimination method was used for selecting the best set of covariates for longitudinal and survival sub-models (P value higher than 0.10 were considered for dropped). All analysis was conducted using R software (version 3.5.0) packages (JM and JM-Bayes packages) which are free software.
The Linear Mixed effect Model (LMM) was used to investigate the effects of study covariates on the log transform of β-hCG titration over time.
The semi-parametric survival model was used to explain how the risk of GTN occurring at a given time is affected by the study covariates. In addition to Cox-PH regression, Weibull parametric model is also considered for this study. We used Akaike's Information Criteria (AIC) and Bayesian Information Criterion to choose the best survival model. The covariates in the survival model may or may not to be the same covariates in the LMM.
The main goal of our study investigates the association between longitudinally measured of the β-hCG biomarker with time to GTN occurring. After determining the appropriate longitudinal and survival sub-models separately, these sub-models joined using shared parameter association. This shared parameter associates longitudinally measured of β-hCG random-effects with time to GTN occurring. We used a Bayesian estimation process and a Markov chain Monte Carlo (MCMC) algorithm to parameters estimation and fit the joint modeling. In the Bayesian process, standard prior distribution was considered for all parameters. This procedure provides robust results when compared to the maximum likelihood approach. In addition, specifying a prior distribution for the parameters gives the investigator an opportunity to accommodate any existing information into the model. The DIC score was used to determine the appropriate joint model.,
| Results|| |
The demographic and general characteristics of patients are shown in [Table 1]. The mean (SD) age of patients was 26.6 (6.55) years and 6% of them were Afghan who living in Iran. Among 201 women with a molar pregnancy, the GTN was occurred in 30 patients (14.9%) during the one-year follow-up.
Based on the values of AIC and BIC we considered LMM model with random intercept and a random slope for this analysis [Table 2]. As shown in [Table 2] using backward elimination method the best set of effective covariates on log β-hCG titration was including race, vaginal bleeding, gestational age, and theca lutein cyst (P value < 0.10). In the final LMM model, log B-HCG had a significant positive association with gestational age (coefficient: 0.018, 95% CI: 0.004 to 0.040). Also, the mean of log β-hCG in women with theca lutein cyst was significantly different form women without theca lutein cyst (coefficient: 0.858, 95% CI: 0.509 to 1.21) [Table 3].
|Table 2: Linear mixed-effect model (LMM) and Cox-regression model selection|
Click here to view
|Table 3: Linear mixed-effect model (LMM) and Cox-regression model parameter's estimation separately|
Click here to view
The comparison of survival models is presented in [Table 2]. Using AIC Cox-PH survival model had a smaller value and therefore we used this model for analysis. After the backward elimination method, the best set of effective covariates on time to occurrence of GTN was including abortion, gestational age, and cervix height (P value < 0.10). As shown in [Table 3], in the final Cox-PH model one unit increase in the uterine height of the patient 17% decreases the risk of GTN occurring (HR: 0.829, 95% CI: 0.68 to 0.98) [Table 3].
The joint modeling of log β-hCG titration and time to GTN occurring is shown in [Table 4]. Results are presented as the parameter's estimation with corresponding 95% CI. Regarding the importance of the effect of age on GTN occurring, we considered it in two longitudinal and survival sub-model. The estimation of association parameter (95% CI) in classic and Bayesian joint modeling was as 0.580 (0.148 to 1.01) and 1.30 (0.44 to 2.20) respectively, which showed a positive significant association between the serial measurement of β-hCG titration with hazard for the occurrence of GTN. The Bayesian estimation showed a stronger association than the classic models. As shown in [Table 4] the history of abortion in survival sub-model were significantly associated with a higher hazard for GTN occurring (HR: 2.06, 95% CI: 1.001 to 4.71), while this association in Bayesian model was not significant. In longitudinal sub-model, the theca lutein cyst was positively associated with log β-hCG titration in two classic and Bayesian models (classic estimation: 0.849, 95% CI: 0.501 to 1.20 and Bayesian estimation: 0.779, 95% CI: 0.413 to 1.15, respectively).
| Discussion|| |
Findings of this study revealed that serial measurements of β-hCG titration post molar pregnancy were positively associated with a hazard rate of time to GTN occurring. According to the Bayesian joint estimation, one unit increase in the log β-hCG corresponds to the 2.80-times increase in the hazard for the occurrence of GTN. Consistent with our finding previous studies showed that β-hCG measurement after treated a molar pregnancy is a good marker to differentiate patients who will get spontaneous recovery from patients developing GTN.,,, A study on 3926 women with partial or complete mole showed that risk of GTN was clearly different based on the levels of β-hCG and rising with β-hCG level. In another study more than 50% of patients who will really develop GTN can be predicted using the slope of the regression line of β-hCG with 97.5% specificity. Similarly, Kim et al. reported that using comparing regression rate in two weeks after molar evacuation the occurrence of GTN can be estimated with 48.0% sensitivity and 89.5% specificity and post 2nd week both specificity and sensitivity continue to increase.
The β-hCG produced by the placenta during pregnancy. In addition to pregnancy, β-hCG can be secreted by abnormal embryonic tissues and gestational trophoblastic disease such as GTN. After the surgical evacuation of molar pregnancy, the level of β-hCG falls rapidly in patients who have trophoblastic tissue limited to the endometrial cavity, while in patients whose trophoblastic tissue has been trespassed the uterine wall or metastasized in other organs β-hCG level decrease slowly due to the presence of the residual β-hCG producing trophoblastic tissue. Indeed, a slow decrease in β-hCG levels post-molar evacuation can predict the presence of invasive trophoblastic tissue.,
We used Bayesian joint modeling to investigate the link between longitudinal β-hCG titration and risk of GTN. This model is a powerful approach that takes into account the dependency and association between longitudinal biomarker and ime to a specific event. While, in the majority of previous studies in this field the classical models such as simple regression model, linear mixed model, extended cox model, and ROC analysis were used which cannot consider dependencies between two different type of data.,, Furthermore, we used the Bayesian approach to estimate the parameter due to the low frequency of the event (GTN). This approach was the major difference between our study and other studies., Therefore, it is expected that the models were used in this study by taking into account more information lead to more reliable and accurate estimation than other studies.
In the present study according to the separate analysis of repeated measurements data, the gestational age and theca lutein cyst have a positive effect on log β-hCG titration. Two case studies reported that theca lutein cysts were correlated with elevated β-hCG level and large size cysts were seen in the maximum level of β-hCG., The theca lutein cyst occurred by an abnormal response of atretic follicles in the ovaries to flowing the β-hCG and typically are not seen in the first trimester of pregnancy due to the low level of β-hCG at this time.
Our finding in a separate analysis of time to event data showed that gestational age and history of abortion were positively associated with a high risk of GTN, while gestational age in survival sub-model of joint modeling was not significant. Consistent with our result in the overview by Steigrad the prior abortion was a risk factor for the occurrence of GTN. Similarly, other studies reported that the history of abortion is linked with risk of GTD such as hydatidiform and GTN and 25% of all cases of GTN occur post-abortion.,,,
This study has some strengths and limitations. Along with considering the longitudinal endogenous biomarker (GTN), Using Bayesian joint modeling to determine the association between study variables was the main strength of this study. Data for this study were collected by registration, so information error in data classification and incomplete registration of some covariates could occur which is the main limitation of our study.
| Conclusions|| |
The findings of this study using more robust methods reported that β-hCG trajectories post-molar pregnancy is an important marker to predict the occurrence of GTN. This malignancy in early stages has a low risk of metastasis and is more treatable even with single-agent chemotherapy. Therefore, follow-up with serial measurements of the β-hCG level is very important to early detection of GTN which can reduce cancer's financial impact and enable more effective and less complex treatment. Suggested that the β-hCG levels were monitored on a weekly basis until normal during three consecutive weeks, and then followed by monthly determinations up to six months. However, a new study illustrated that daily measurements of β-hCG give a better prediction of post-molar GTN. Further studies using robust statistical methods to determine the optimal duration of β-hCG monitoring are recommended.
We are thankful of Shahid Beheshti University of Medical Sciences for providing the data for this study and all the people who accompanied us in the process of completing this study.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Berkowitz RS, Goldstein DP. Current management of gestational trophoblastic diseases. Gynecol Oncol 2009;112:654-62.
Alazzam M, Young T, Coleman R, Hancock B, Drew D, Wilson P, et al
. Predicting gestational trophoblastic neoplasia (GTN): Is urine hCG the answer? Gynecol Oncol 2011;122:595-9.
Almasi A, Almassinokiani F, Akbari P. Frequency of molar pregnancies in health care centers of Tehran, Iran. J Reprod Infertil 2014;15:157-60.
Lurain JR. Gestational trophoblastic disease II: Classification and management of gestational trophoblastic neoplasia. Am J Obstet Gynecol 2011;204:11-8.
Mulisya O, Roberts DJ, Sengupta ES, Agaba E, Laffita D, Tobias T, et al
. Prevalence and factors associated with hydatidiform mole among patients undergoing uterine evacuation at mbarara regional referral hospital. Obstet Gynecol Int 2018;2018:9561413.
Kang WD, Choi HS, Kim SM. Prediction of persistent gestational trophobalstic neoplasia: The role of hCG level and ratio in 2 weeks after evacuation of complete mole. Gynecol Oncol 2012;124:250-3.
Wolfberg AJ, Berkowitz RS, Goldstein DP, Feltmate C, Lieberman E. Postevacuation hCG levels and risk of gestational trophoblastic neoplasia in women with complete molar pregnancy. Obstet Gynecol 2005;106:548-52.
Kim BW, Cho H, Kim H, Nam EJ, Kim SW, Kim S, et al
. Human chorionic gonadotrophin regression rate as a predictive factor of postmolar gestational trophoblastic neoplasm in high-risk hydatidiform mole: A case-control study. Eur J Obstet Gynecol Reprod Biol 2012;160:100-5.
Aminimoghaddam S, Yarandi F, Nejadsalami F, Taftachi F, Noor Bakhsh F, Mahmoudzadeh F. Human chorionic gonadotrophin as an indicator of persistent ges-tational trophoblastic neoplasia. Med J Islam Repub Iran 2014;28:44-7.
Betz, D, Fane K. Human chorionic gonadotropin (HCG). p. 1-328.
Thomas CMG, Kerkmeijer LGW, Ariaens HJW, van der Steen RCBM, Massuger LFAG, Sweep FCGJ. Pre-evacuation hCG glycoforms in uneventful complete hydatidiform mole and persistent trophoblastic disease. Gynecol Oncol 2010;117:47-52.
Shigematsu T, Kamura T, Saito T, Kaku T, Nakano H, Kinugawa N. Identification of persistent trophoblastic diseases based on a human chorionic gonadotropin regression curve by means of a stepwise piecewise linear regression analysis after the evacuation of uneventful moles. Gynecol Oncol 1998;71:376-80.
Khosravirad A, Zayeri F, Baghestani AR, Yoosefi M, Bakhtiyari M. Predictive power of human chorionic gonadotropin in post-molar gestational trophoblastic neoplasia: A longitudinal roc analysis. Int J Cancer Manag 2017;10:15-20.
Dantas PRS, Maestá I, Filho JR, Junior JA, Elias KM, Howoritz N, et al
. Does hormonal contraception during molar pregnancy follow-up influence the risk and clinical aggressiveness of gestational trophoblastic neoplasia after controlling for risk factors? Gynecol Oncol 2017;147:364-70.
Cole LA. Immunoassay of human chorionic gonadotropin, its free subunits, and metabolites. Clin Chem 1997;43:2233-43.
Ware JH. Random-effects models for longitudinal data.Biometrics 1982;38:963-74.
Fox J. An R companion to applied regression. 3rd
Rizopoulos D. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R.1st Edition. London: CRC Press, Taylor and Francis; 2012. p.1-275.
Spiegelhalter DJ, Best NG, Carlin BP, Linde A van der. Bayesian measures of model complexity and fit. R Stat Soc 2002;64:583-639.
Khoo SK, Baartz D, Sidhu M, Yip WL, Tripcony L. Analysis of risk factors for persistent gestational trophoblastic disease. Aust New Zeal J Obstet Gynaecol 2009;49:657-9.
Kimiaee P, Ashrafi-Vand S, Mansournia MA, Bakhtiyari M, Mirzamoradi M, Bakhtiyari Z. Predictive values of different forms of human chorionic gonadotropin in postmolar gestational trophoblastic neoplasia. Int J Gynecol Cancer 2014;24:1715-22.
Mousavi AS, Karimi S, Modarres Gilani M, Akhavan S, Rezayof E. Does Postevacuation β-Human chorionic gonadotropin level predict the persistent gestational trophoblastic neoplasia? ISRN Obstet Gynecol 2014;2014:1-4.
Lybol C, Sweep FCGJ, Ottevanger PB, Massuger LFAG, Thomas CMG. Linear regression of postevacuation serum human chorionic gonadotropin concentrations predicts postmolar gestational trophoblastic neoplasia. Int J Gynecol Cancer 2013;23:1150-6.
Korevaar TIM, Steegers EAP, de Rijke YB, Schalekamp-Timmermans S, Visser WE, Hofman A, et al
. Reference ranges and determinants of total hCG levels during pregnancy: The Generation R Study. Eur J Epidemiol 2015;30:1057-66.
Jagtap SV, Aher V, Gadhiya S, Jagtap SS. Gestational trophoblastic disease-Clinicopathological study at tertiary care hospital. J Clin Diagnostic Res 2017;11:EC27-30.
Ibrahim JG, Chu H, Chen LM. Basic concepts and methods for joint models of longitudinal and survival data. J Clin Oncol 2010;28:2796-801.
Quigley J, Bedford T, Walls L. Estimating rate of occurrence of rare events with empirical bayes: A railwayapplication.
Bakhtiyari M, Mirzamoradi M, Kimyaiee P, Aghaie A, Mansournia MA, Ashrafi-vand S, et al
. Postmolar gestational trophoblastic neoplasia: Beyond the traditional risk factors. Fertil Steril 2015;104:649-54.
Imtiaz S. Hyperreactio luteinalis with partial molar pregnancy. Appl Radiol 2017;45-7.
Conservative management of theca lutein cyst accident: A case report. Int J Med Res Heal Sci 2017;6:163-6.
Steigrad SJ. Epidemiology of gestational trophoblastic diseases. Best Pract Res Clin Obstet Gynaecol 2003;17:837-47.
Braga A, Mora P, Melo AC de, Nogueira-Rodrigues A, Amim-Junior J, Rezende-Filho J, et al
. Challenges in the diagnosis and treatment of gestational trophoblastic neoplasia worldwide. World J Clin Oncol 2019;10:28-37.
Ngan HYS, Seckl MJ, Berkowitz RS, Xiang Y, Golfier F, Sekharan PK, et al
. Update on the diagnosis and management of gestational trophoblastic disease. Int J Gynecol Obstet 2018;143:79-85.
Biscaro A, Braga A, Berkowitz RS. Diagnosis, classification and treatment of gestational trophoblastic neoplasia. Rev Bras Ginecol Obstet 2015;37:42-51.
Berek JS. Novak Gynecology. 14th
ed. Philadelphia, PA: Lippincott, Williams and Wilkins; 2007.
Zhao P, Shuangyan W, Xiuli Z, Lu W. A novel prediction model for postmolar gestational trophoblastic neoplasia and comparison with existing models. Int J Gynecol Cancer 2017;27:1028-34.
[Table 1], [Table 2], [Table 3], [Table 4]