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ORIGINAL RESEARCH |
From the Section of Gynecologic Oncology, Department of Obstetrics and Gynecology, and Department of Pathology, Trondheim University Hospital and; Section of Epidemiologic Research, SINTEF, UNIMED, Trondheim, Norway.
Address reprint requests to: Solveig Tingulstad, MD, Department of Gynecology and Obstetrics, Trondheim University Hospital, 7006 Trondheim, Norway; E-mail: solveig.tingulstad{at}medisin.ntnu.no.
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ABSTRACT |
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 ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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METHODS: Incidence and prognostic factors of survival within a population-based cohort of ovarian cancer patients from one health region in Norway were examined over the 10-year period 1987 through 1996. A total of 571 histologically verified cases of primary ovarian cancer originally registered either in the Cancer Registry of Norway or in the hospital’s discharge registers were included in the study. Pearson 
2 test was used in univariate analyses of cofactors by 5-year survival, and Kaplan-Meier survival curves were computed and tested statistically by the log rank test. A multivariable proportional hazard model (Cox) was applied to assess the prognostic significance of the different covariates.
RESULTS: The incidence and crude 5-year survival remained stable over the 10-year study period. The standardized incidence rate for the time periods 1987–1991 and 1992–1996 was 11.9/100,000 and 12.5/100,000, respectively. The crude 5-year survival rate for the cohort was 39%, whereas median survival was 32 months. Cox multivariable regression analysis showed that the only independent significant prognostic factors were International Federation of Gynecology and Obstetrics stage (P < .001), size of residual tumor at the end of primary surgery (P < .001), and age at diagnosis (P < .01). Variables such as time period, histologic type and grade, treating hospital, comorbidity, or CA 125 were insignificant in predicting 5-year survival.
CONCLUSION: The results underline the importance of improved surgical management of ovarian cancer, as residual tumor is the only prognostic factor achievable.
Ovarian cancer is the seventh most frequent cancer in women worldwide, and is the leading cause of death from gynecologic malignancies in most of the western world. The Scandinavian countries exhibit some of the highest incidence rates with an estimated lifetime risk of 2%.1 In Norway, about 480 incident cases of ovarian cancer are diagnosed annually,2 and approximately two-thirds of the patients experience disease recurrence, which will prove fatal in the great majority. At time for diagnosis, more than 60% of ovarian cancers present at an advanced stage, and the prognosis is poor with an expected 5-year survival rate in the range of 10–20%.3
There have been several recent reviews of prognostic factors in patients with ovarian cancer,4,5 and many investigators have emphasized the importance of these factors for treatment planning and outcome.4–7 In multivariable analyses, the International Federation of Gynecology and Obstetrics (FIGO) stage and the extent of residual disease after primary surgery are the most consistently reported independent prognostic factors for survival. Other factors, such as age, histologic type and grade, preoperative serum CA 125, ascites, performance status, and various molecular markers have less consistently been reported as independent predictors of survival.4,5 From the Nordic countries, however, there are few published data on prognostic factors.7,8
The aim of the present study was to assess incidence during a 10-year study period and to identify and discuss clinical relevance for prognostic factors of survival within a cohort of Norwegian ovarian cancer patients.
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MATERIALS AND METHODS |
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The incidence rate (20–79 years) was age adjusted to the World Standard Population, and was reported as number of new cases per 100,000 person-years.10
Information on potential prognostic factors, such as age at diagnosis, FIGO stage, histologic differentiation grade, histologic type, size of residual disease at the end of surgery, comorbidity, and serum level of CA 125 when available, were abstracted from the medical records at each hospital. Uniform determination of FIGO stage3 was based on the results of available preoperative diagnostic procedures, the operation notes, and pathology reports.
All histologic slides were revised by one pathologist (TBH) and classified according to the World Health Organization guidelines11 by histologic type and degree of differentiation. Volume of residual disease after primary surgery as documented in the surgical notes was categorized into largest diameter of residual nodule less than 1 cm or 1 cm or greater. Treating hospital was dichotomized into teaching hospital (n = 1) or nonteaching hospital (n = 7). Comorbidity was recorded according to the Charlson index score.12 This validated method for classifying comorbidity takes into account the number of and seriousness of concurrent diseases. The preoperative serum CA 125 level was categorized into three groups—less than 35 U/mL, 35–199 U/mL, and 200 U/mL or greater. For 287 patients (50%), preoperative serum CA 125 was unavailable, and thus categorized as unknown.
Crude survival was defined as the time from diagnosis to death from any cause, whereas disease-specific survival (corrected survival) was defined as the time from diagnosis to death from ovarian cancer. For the main analyses and throughout the text, survival is synonymous with crude survival unless clearly stated otherwise. Patients were followed until death or December 31, 2001. Information about the vital status and cause of death was obtained from the medical records at each hospital or from death certificates. Patients who survived were censored at 61 months (5-year survival). One woman who moved abroad after primary treatment was lost to follow-up, leaving 570 patients for survival analysis.
All statistical analyses were done in Statistical Package for Social Sciences 11.01 (SPSS Inc., Chicago, IL). Pearson 2 test was used in univariate analyses of cofactors by 5-year survival. Kaplan-Meier survival curves were computed and tested statistically by the log rank test. P values of <.05 were considered statistically significant. A multivariable proportional hazard model (Cox)13 was applied to assess the prognostic significance of the different covariates. Prognostic factors entering the stepwise Cox analysis were: age (younger than 45 years, 45–54, 55–64, 65–74, 75 or older), FIGO stage (I, II, III, IV), grade of tumor (1 = well differentiated, 2 = moderately differentiated, 3 = poorly differentiated), histologic type (serous, endometroid, mucinous, clear cell and malignant Brenner, mixed epithelial, undifferentiated, unclassified epithelial, sex-cord stromal, germ cell, unclassified nonepithelial), residual tumor (less than 1 cm, 1 cm or greater), treating hospital (teaching, nonteaching), comorbidity score (0, 1, 2, 3 or greater), serum CA 125 (less than 35 U/mL, 35–199 U/mL, 200 U/mL or greater, unknown), and time period (1987–1991, 1992–1996). Before multivariable analyses, graphic checks of proportionality assumptions were performed for all covariates. The models were generated by systematic removal of predictors that were not significant (P <.10) starting from a full model containing all determinants. Finally, possible interaction was assessed between the independent prognostic factors. The study was approved by The Regional Medical Research Ethics Committee, Central Norway, Trondheim.
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RESULTS |
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).
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. Univariate analysis suggested that age, FIGO stage, differentiation grade, histology, residual tumor, comorbidity scores, and serum CA 125 preoperatively were all significant predictors of survival. No such association was found for time period or treating hospital. In addition, there were no major differences in distribution of prognostic factors by time period (1987–1991, 1992–1996). However, more patients had primary surgery at the teaching hospital during the later period.
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shows the final results from the Cox regression analyses. Higher age, higher FIGO stage, and larger amount of residual disease were the only predictors of death during the first 5 years. Corresponding survival curves for the three independent prognostic factors are displayed in Figures 1–3. The relative risk of death compared with FIGO stage I was 4.2 for stage II, 8.0 for stage III, and 11.7 for stage IV (Figure 1). Patients with residual tumor of 1 cm or greater after primary surgery had a 1.7-fold higher risk of death compared with those who underwent optimal surgery (Figure 2). Patients older than 75 years had a 2.8-fold higher risk of dying compared with patients 45 years or younger (Figure 3). Variables such as time period, histologic type and grade, treating hospital, comorbidity score, or CA 125 did not reach significance in predicting 5-year survival in the multivariate analysis. We explored several models by recategorizing age (younger than 65, 65 or older), histologic type (good, moderate, and poor prognosis) (Table 2
),14 and comorbidy score (0, 1, 2 or greater) without significant alteration of the results. We also tried to reduce the sample size to include only those patients with known preoperative serum CA 125 value. However, CA 125 did not turn out as a predictor of survival in this reduced model. Finally, no interactions were found between the three significant predictors of 5-year survival.
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DISCUSSION |
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The 5-year survival rate of 39% in our study did not differ from the 5-year survival published from the Cancer Registry of Norway for the period 1989–1993 based on national figures (37%) or that reported from the Swedish Tumor Registries during the period 1984–1987 (38%).8,18 The above-reported 5-year survival figures are in accordance with the FIGO’s Annual Reports, in which 39% and 42% 5-year survival rates were published for the time periods 1987–1989 and 1990–1992, respectively.3
This study identifies only FIGO stage, size of residual tumor after primary surgery, and age at diagnosis as independent prognostic factors for survival. The FIGO stage has been recognized as a significant prognostic factor in most studies applying multivariable analyses.5,8,19–22 In the present study, the relative risk for dying was eight-fold having stage III disease and 11-fold having stage IV disease compared with stage I. These results are comparable to those of previous studies.22 Furthermore, the study showed that both differentiation grade and histologic type were correlated to FIGO stage. Seventy-eight percent of the patients with differentiation grade 3 had stage III or IV disease, whereas 76% of those with differentiation grade 1 had stage I or II disease. By histologic type, 79% of the serous carcinomas and 83% of the undifferentiated carcinomas occurred in stage III or IV disease. No undifferentiated carcinomas were found among stage I patients, and only 13% of the serous carcinoma patients had stage I disease. This reflects the overriding effect of FIGO stage on differentiation grade and histologic type. The lack of consistency in the literature with regard to the impact of differentiation grade and histologic type on survival4 could be an effect of various stage distributions across studies as well as inter-observer variability in histologic grading and typing.23,24 The present stage distribution (30% stage I, 11% stage II, 46% stage III, and 13% stage IV) corresponds to what is reported from other population-based studies.14,21
It is well known that serum CA 125 levels are highly correlated to FIGO stage, with the highest levels in patients with disseminated disease.25,26 Seventy-nine percent of our patients presenting with CA 125 above 200 U/mL had stage III or IV disease, whereas only 13% of those with CA 125 level below 35 U/mL had advanced disease. The high colinearity between stage and CA 125 explains why CA 125 did not become an independent risk factor of survival in our and other studies in which CA 125 is assessed in multivariate analyses.5
In agreement with the great majority of comparable previous studies, we found residual tumor size after primary surgery to be an independent prognostic factor for survival.5–8,20,22,27,28 In a recently published meta-analysis of 53 studies on the effect of maximal cytoreductive surgery on survival in patients with advanced ovarian carcinoma, a statistically significant positive correlation between percentage maximal cytoreduction and median survival time was reported (P < .001).29 The volume of tumor left after surgery depends on the number and the size of residual tumor sites, but it is controversial which cutoff value, 1 cm or 2 cm, is considered optimal residual disease at end of surgery.6,30–32 Only 32% of stage III patients and 25% of stage IV patients were left with residual disease of less than 1 cm after surgery in the present study. From specialized centers, optimal debulking has been achieved in as many as 87% of patients in advanced stages (III and IV).30,33 Our results revealed that there is a colinearity between treating hospital and size of residual tumor. In the final Cox model, residual tumor was a stronger predictor of survival than treating hospital, which explains why treating hospital did not become an independent prognostic factor.
A uniform surgical reporting is required to measure objectively the distribution and extent of residual disease, otherwise future comparison across studies will still be invalid. Whether the better prognosis of small residual tumors is an expression of the better penetrability of the cytotoxic drugs or whether it describes a biological phenomenon in tumors, which also are easily debulked, is unknown. Anyhow, the outcome of multivariable analyses uniformly indicates that aggressive cytoreductive surgery improves prognosis and should therefore be aimed at until future progress eventually defines a superior strategy.
The effect of adjuvant chemotherapy on survival compared with cytoreduction has been widely discussed. Bristow et al29 reported a positive correlation between percentage maximal cytoreduction and median survival time and found that platinum dosing did not affect survival. In this meta-analysis, studies that did not include the dosing schedule of the platinum agent were excluded. Other authors have found substantial survival associated with the use of platinum chemotherapy. However, detailed information of dosages and number of courses given are often missing.5,7,8,14 In our study, four groups of chemotherapy regimens did enter the Cox model (no chemotherapy, platinum only, platinum combined with another agent, and nonplatinum chemotherapy). In this analysis, chemotherapy did not become an independent risk factor. However, we have no consistent data on dosage and treatment schedule, but we have data on completed courses of chemotherapy. According to running treatment guidelines, we did create a variable on "complete/incomplete chemotherapy." The multivariable analyses revealed that "completeness of chemotherapy" had no prognostic value on survival.
The age at diagnosis was the third independent prognostic factor found in our study. Patients older than 65 years had a two-fold higher risk of death compared with patients younger than 45 years. Other studies5,8,16,19,21 also present old age at diagnosis as an important determinant of prognosis. The presence of coexisting diseases is an important factor in evaluating the outcome of a disease. In recent years, many studies have assessed comorbidity in long- and short-term outcome using Charlson score.34,35 In our study, the Charlson comorbidity index score had no major prognostic impact on overall survival, which can be explained by the close association between comorbidity and age. Among patients older than 65 years, 50% had a comorbidity score of 1 or higher, whereas only 20% of the younger patients (less than 65 years) had comorbidity, indicating the overriding effect of age upon comorbidity.
In conclusion, in this population-based study with meticulous revision of case inclusion and pathology, only FIGO stage, amount of residual tumor, and age were identified as significant prognostic factors. As long as various screening attempts for ovarian cancer fail to improve stage distribution,36,37 the only factor that is possible to influence is residual tumor by cytoreductive surgery. Thus, every effort to improve the organization and quality of ovarian cancer surgical management should have the highest priority.
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Footnotes |
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Received July 12, 2002. Received in revised form September 19, 2002. Accepted November 7, 2002.
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REFERENCES |
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17. Parazzini F, Franceschi S, La Vecchia C, Fasoli M. The epidemiology of ovarian cancer. Gynecol Oncol 1991;43: 9–23.[Medline]
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