Comparison Of Survival Outcomes Between Radical Hysterectomy And Definitive Radiochemotherapy In Stage IB1 And IIA1 Cervical Cancer

Accurate prediction of cancer control after definitive treatment for cervical cancer is important for patient counselling, follow-up, and treatment planning. We constructed a novel nomogram based on a model to predict 3- and 5-year OS for patients with invasive cervical cancer after surgical staging. The nomogram can be used to predict patients’ prognosis individually and more accurate than FIGO stage alone and is based on the following six easily available parameters: FIGO stage, tumour size, histologic type, LNR, age, and parametrial involvement. The model was derived from a European cohort with the entirety of the age spectrum.

FIGO stage is the most important prognostic parameter in patients diagnosed with cervical cancer. Many, but not all, of the known prognostic factors are captured by the FIGO staging system. The FIGO staging system is largely based upon physical examination and a limited number of diagnostic studies (Pecorelli et al, 2009). The limitations of FIGO clinical staging are well appreciated and under-staging occurs (Hricak et al, 2005; Quinn et al, 2006). Tumour size is an established prognostic parameter that is independent of stage and was originally reflected in FIGO stages IA-IB2. During the 2009 FIGO staging system changes tumour size was added as parameter for stage IIA to distinguish between tumour diameters smaller (IIA1) and larger (IIA2) than 4 cm tumours due to its considerable prognostic value (Delgado et al, 1990; Pecorelli et al, 2009). The effect of tumour histology on outcome for women with cervical cancer has been actively debated. Recent studies have shown that adenocarcinomas are more aggressive and are associated with decreased survival for both early and advanced-stage carcinomas (Galic et al, 2012).Sponsored: Gambar Koala

Lymph node ratio is a parameter incorporating not only information on the number of positive nodes but also the number of removed nodes. Lymph node ratio is a useful prognostic parameter for patients with cervical cancer and allows stratification of patients into distinctive outcome groups (Polterauer et al, 2010). It is important to highlight that assigning LNR requires surgical staging with node dissection. The authors are aware that the therapeutic value of pretreatment surgical lymph node staging for patients with advanced stage cervical cancer is still controversial (Brockbank et al, 2011). At our institution extended-field radiotherapy is recommended when paraaortic nodal disease is proven and we observed excellent pelvic control in these high-risk patients using this concept (Pötter et al, 2011). For patients with stage IA1 disease lymph node, dissection was only performed when LVSI was present. As previous studies have shown that the risk for nodal involvement is less than 1% for patients with stage IA1 disease and without LVSI, we assumed negative nodal status and this group of patients was allocated a LNR of 0.0.

Recent studies suggested that younger age is an unfavourable prognostic factor, especially in more advanced stages (Chen et al, 1999). Survival analysis revealed that younger patients showed impaired survival in cohort and therefore age was included into our model. Parametrial involvement significantly influences outcome of patients with cervical cancer but is not reflected in FIGO stages III-IV (Takeda et al, 2002). In the Gynecologic Oncology Group (GOG), 49 study patients with apparent stage I disease with and without parametrial involvement were shown to have 70% and 85% 3-years disease-free intervals, respectively (Delgado et al, 1990). Patients found to have parametrial involvement will usually receive primary or adjuvant radiation therapy after surgery (Committee on Practice Bulletins-Gynaecology, 2002).

A nomogram is a graphic prediction tool that incorporates clinical risk factors already included in established staging systems, as well as other additional, clinical, and pathologic factors known to have an impact on outcome. A distinct advantage of a nomogram is that all the critical variables that determine outcome can be graphically displayed (Iasonos et al, 2008). Prognostic nomograms attempt to combine important clinical factors to quantify the risk as precisely as possible to accurately predict clinical outcome. Clinical nomograms have been developed as predictive tools for outcomes in gynaecologic malignancies such as endometrial and ovarian cancer (Chi et al, 2008; Abu-Rustum et al, 2010). For the estimation of outcome of patients with cervical cancer, few nomograms have been published so far (Kim et al, 2010; Tseng et al, 2010). This nomogram is the first to predict OS through stages I–IV that was constructed based on data of a mainly Caucasian patient cohort.

Despite having achieved prognostic accuracy, our study is not devoid of limitations. The multi-institutional nature of our data set may be interpreted as a limitation, given that it groups the contribution of multiple surgeons and pathologists and relies on different surgical approaches, in addition to other differences that might distinguish the two contributing centres. However, this limitation could also be seen as strength, as it makes our conclusions more general and increases the available sample size, which is important given the low event rate. Despite combining data from two institutions, we observed a limited number of events (i.e., cancer-related deaths). To take this into account, the model coefficients were computed using the recently developed ‘ridge’ method. This approach was originally designed to deal with situations where the number of predictors exceeds the number of events by large (‘P>>n’) such as in genomic applications, and was recently found to be superior to other methods also for prediction in ‘classical’ (‘P<n’) settings, where less than 10 events per variable are available (Ambler et al, 2012). In ridge regression, the amount of penalisation is driven by predictive performance in cross-validation (cross-validated partial likelihood). This ‘automatism’ guards against over-fitting a model. However, shortage of data, caused by too many variables compared with the number of events, may lead to higher penalisation, and reduce ability of a model to predict the outcome in the extremes. Therefore, we created calibration plots based on internal cross-validation by bootstrap resampling, which showed an almost perfect agreement of observed and predicted event rates. Nevertheless, external validity of our model is a crucial prerequisite to clinical applicability, and can only be assessed by confirming results in a reasonably large independent validation cohort with an adequate follow-up period. Another potential limitation of our model as typical for nomograms is that construction is based on retrospective data. Therefore, our study might be limited by biases such as lack of random assignment, patient selection, and incomplete data acquisition. Nomogram variables were missing in a significant number (n=164) of patients of our cohort. Mostly information on parametrial involvement and exact tumour size was incompletely documented in the cases that were excluded from analysis, especially in patients with advanced stage cervical cancer.

In summary, we present the first model to predict 3- and 5-year survival for patients with cervical cancer after surgical staging that is applicable through stages I–IV. The model was developed in a relatively large European, mainly Caucasian, cohort and can be applied for prediction by means of the presented nomogram or, more conveniently, by an online prediction tool (available at

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Nomogram prediction for overall survival of patients diagnosed with cervical cancer