Various biomarkers have been studied intensively for their potential to help with diagnosing and monitoring progression of diseases, assessing patients’ prognosis and developing new drugs. In clinical practices, it has been widely applied to classify the biomarker implicated in a disease into two or more groups and the classification may serve as a clinical guideline to help make treatment decisions. For example, CA125 is an important biomarker in ovarian cancer with the cut point at 35 U/ml to monitor patients’ response to chemotherapy agents as well as for early detection of disease relapse.[12] In endometrial cancer, whether the depth of myometrial invasion exceeds 50% has been established as one of the criteria to differentiate between stage IA and IB tumors.[5]
As classification of biomarkers becomes more and more useful, it has also become increasingly important to employ proper methods for stratification. Although multiple statistical methods are already available, many biomedical studies have not been able to benefit from them. One probable reason is the difficulty to directly apply statistical algorithms on biological data. This study, therefore, may be deemed as an illustration on how to use proper statistical methods to find optimal cut points while at the same time, we also provided a ready-to-use package (supplementary files) for biomedical researchers to use for their own data.
In this study, we analyzed two clinical factors that have both been implicated in risk assessment of endometrial cancer, depth of myometrial invasion and tumor size. Previous studies have found that when the depth of myometrial invasion is less than 50% and the diameter of the tumor is smaller than 2 cm, the patient is at a low risk for lymph nodal involvement.[7, 8, 13, 14] However, to our knowledge, few started with identifying the best split(s), and most only compared the results of several tentative groupings. This study differed from the previous ones in that it was aimed to find the optimal cut points first. Our results echoed the role of myometrial invasion in predicting nodal status; however, tumor size was found not to be an independent risk factor of nodal involvement, yet it was one for overall survival.
The search for optimal cut points in this study was first based on the AIC value in that statistically the smallest AIC gave the best number of cut points. Based on this criterion, we found that the best choice for myometrial invasion was to group it into three risk levels with two cut points at 0.41 and 0.89 in response to lymph nodal involvement. As a matter of fact, 0.41 is quite close to the conventional threshold of 0.5, and in this study, the patients in the group of 0.41–0.89 indeed had a significantly higher risk for lymph node metastasis compared to those with less than 0.41 depth (OR = 2.68). However, our result showed that those with even deeper invasion (> 0.89) had a much higher risk (OR = 18.37). Comparison between stratifying patients with 0.5 only and with 0.41 and 0.89 also showed that the latter had better predictivity. Therefore, we propose that a nearly complete infiltration into myometrium be also included in assessing patients by gynecologists.
This study negated the role of tumor size in predicting nodal status. In fact, there has been some evidence to this end as it has already been suggested before that grade 1 tumors with less than 50% myometrial invasion are at low risk for lymph node metastasis regardless of tumor size.[8] Nevertheless, tumor size was found in this study to be an independent risk factor for overall survival together with FIGO stage and histological subtype. The optimal threshold in this case is around 4 cm, and we also showed that it had better prediction for survival than 2 cm.
On a side note, when applying this method in clinical studies, one may need to balance between the AIC criterion and clinical concerns; if the smallest AIC happens to correspond to a large number of cut points, it may complicate clinical practices, and this is why only 1 to 3 cut points were considered in this study. Therefore, when the smallest AIC value happens to correspond to a large number, one may take the liberty to choose the next best AIC, which may be more practical.
This study also has limitations. The study was a retrospective one and all limitations inherent in the retrospective design could not be avoided in this study. Another limitation is that the study comprised a limited number of patients from one single institution. Due to these limitations, the conclusion here cannot be readily generalized. Future studies will aim at obtaining a larger sample size from multiple institutions, and possibly prospective studies.