883

Sensors and Materials, Vol. 31, No. 3 (2019) 883–888
MYU Tokyo
S & M 1822

Performance Improvement ofOvarian Cancer Classification ModelUsing Multiple Biomarkers and Menopause Information
Hye-Jeong Song,1 Min-Sun Kyung,2* Eun-Suk Yang,1Jong-Dae Kim,1 Chan-Young Park,1 and Yu-Seop Kim1
1Bio-IT Research Center, Hallym University, 1, Hallymdaehak-gil, Chuncheon 24252, Korea
2Department of Obstetrics and Gynecology, Hallym University Medical Center,
7, Keunjaebong-gil, Hwaseong-si 18450, Korea(Received May 31, 2018; accepted January 8, 2019)

Keywords: ovarian cancer, IVDMIA, marker combination, cancer classification, menopause

The tumor biomarker test used for the early diagnosis of ovarian cancer is a relatively simple
test using blood. In previous studies, an optimal combination of 2 or 3 biomarkers from 16
cancer biomarkers showing a specific response to ovarian cancer was designed to obtain anovarian cancer classification model. Menopause is an important information for diagnosing
ovarian cancer. In this study, we applied the menopausal status to the classification model andconfirmed the performance results. The classification model has better performance when
including menopausal clinical information.

1.

Introduction

Ovarian cancer is a malignant tumor of the ovary that occurs most often between the ages of
50 and 70. Epithelial ovarian cancer, which accounts for about 90% of ovarian cancer, is foundin more than three phases, so the 5-year survival rate after cancer diagnosis is less than 40%.
However, 25% of patients with early diagnosis have a survival rate of more than 90% over 5years, and the survival rate of second-stage patients is over 70%. The early diagnosis of ovariancancer provides a great possibility to improve clinical outcome and the survival rate can beincreased. Therefore, early diagnosis of ovarian cancer is important.(1)The use of cancer biomarkers is a relatively simple method of screening using blood,and cancer screening is possible at a lower cost than by other diagnostic methods. A cancer
biomarker is a molecule that indicates the presence of cancer in the body. It transmits
information on the basis of specific changes or mutations of genes, RNA, proteins, andmetabolites, and biomarkers detect molecular changes that occur during tumor development.
Cancer can be discovered and prognosis can be determined, and disease progression andtherapeutic response monitoring become possible.(2)*Corresponding author: e-mail: msfeel@hallym.or.kr
https://doi.org/10.18494/SAM.2019.2095
ISSN 0914-4935 © MYU K.K.
https://myukk.org/

884

Sensors and Materials, Vol. 31, No. 3 (2019)In cancer diagnosis, the use of the in vitro diagnostic multivariate index assay presupposesthat there is no biomarker with a sufficiently high specificity close to 100% in a specific cancer.
Therefore, it is possible to combine multiple biomarkers and quantify the analysis by statisticalmethods. In other words, a method that can scientifically combine several biomarkers related
to a specific cancer to increase the detection rate of advanced cancer is the in vitro diagnostic
multivariate index assay.(1–4)
The in vitro diagnostic multivariate index assay has proposed classification models using avariety of data mining techniques to combine multiple biomarkers, analyze them with statisticaltechniques, quantify them, and improve diagnostic accuracy.(1–7)
In previous studies, an ovarian cancer classification model was developed using serum
protein biomarkers specific to ovarian cancer from ovarian cancer patients and normal humanserum.(1–4) The ovarian cancer classification model was designed by finding the optimal
biomarker combination from 16 serum biomarkers showing a specific response to ovariancancer.
Menopause is an important information for diagnosing ovarian cancer.(2,5–7) In this study,
we applied the menopausal status to the classification model and confirmed the performance
results. The classification model including menopausal clinical information showed better
performance than the classification model that did not include clinical information.

2.

Materials and Methods

2.1

Data collection

The samples used in this study are serum samples from 92 healthy Korean women and 101
patients with ovarian cancer including menopausal status. The sera were provided by Hallym
University Medical Center (HUMC) and Asan Medical Center. These samples were reactedwith Luminex beads attached to 16 biomarkers, and the fluorescence from the antibodies on thebeads was measured.
Unlike in our previous work, we additionally use menopause information to improve
the performance of classification in this experiment. The detailed statistics for data withmenopause information are shown in Table 1. According to our previous work, we find two
2-biomarker combinations and two 3-biomarker combinations that can be used to classify wellcancer and normal samples. Please refer to our previous work for a detailed experiment forfinding the combinations.(1–4)

Table 1
Data statistics.
Diagnosis
Healthy
Ovarian cancer

Premenopausal Postmenopausal Overall3260
923863101

Sensors and Materials, Vol. 31, No. 3 (2019)

2.2885

Receiver operating characteristic area under the curve (ROC AUC)

To assess the test performance, sensitivity and specificity are commonly used, and throughtwo indicators, we can find how well a classifier can distinguish between patients and healthypeople. When a certain diagnosis system is used, sensitivity is a measure of how well thesystem can distinguish between the samples, which is associated with condition. Specificity isa measure of how well the system can distinguish between the samples, which does not have anassociated condition. In addition, the ROC curve is widely used to determine the accuracy ofdiagnosis.(8,9)

3.

Results

Table 2 shows the performance of a model with and without menopause information. One of
the most popular screening tests for ovarian cancer is the CA-125 or HE4 blood test. However,checking the CA-125 level has led to the misdiagnosis of ovarian cancer. The problem withusing CA-125 as a screening test for ovarian cancer is that common conditions other than cancer
can also cause a high CA-125 level.
For detecting ovarian cancer, in our previous studies, we showed that the multiple biomarker
has a better performance than a single biomarker.(3–8) We compare the performance of 4
combinations derived from previous work.(1–4) Menopause (training data) in Table 2 represents
the type of training data. The hyphen (‘-’) in the menopause field indicates using the data
regardless of the menopause information and it is our baseline. Sensitivity and specificity (fourthcolumn in Table 2) indicate the proportion of cancer patients who are correctly identified ashaving the condition and the proportion of healthy women who are correctly identified as nothaving the condition, respectively. Except the Prolactin-TTR combination (first combination),
all combinations show a better performance than each baseline. In the case of the HE4-ELISAProlactin combination (second combination), AUC was improved from 0.969 to 0.98 andsensitivity increased from 0.78 to 0.91 when premenopause data was used. In the case of theTable 2
Performance of model with and without menopause information.
Menopause
Biomarker nameAUC
(training data)
0.975
Prolactin, TTPre
0.975
Post0.975
0.969
HE4-ELISA, Prolactin
Pre0.98Post
0.969
0.985
ApoCIII, HE4-ELISA, Prolactin
Pre0.99
Post
0.982
0.981
HE4-ELISA, Prolactin, TTRPre
0.992
Post
0.984

Sensitivity

Specificity

0.93070.92080.93070.78220.91090.83170.901
0.95050.92080.94060.95050.9406

0.93480.94570.93480.93480.92390.92390.95650.97830.95650.95650.96740.9565

886

Sensors and Materials, Vol. 31, No. 3 (2019)

ApoCIII-HE4-ELISA-Prolactin combination (third combination), AUC improved from 0.985 to0.99 when we used premenopause information.
Furthermore, sensitivity and specificity also increased from 0.901 to 0.9505 and 0.9565
to 0.9783, respectively. Similarly to other combinations, the HE4-ELISA-Prolactin-TTR
combination (last combination) also showed a better performance for AUC, sensitivity, andspecificity when we used premenopause information. Unfortunately, the Prolactin-TTR
combination showed almost the same performance for specificity with the baseline. Figure 1
shows the ROC curve for the performance of a model trained with and without the menopausal
status for each combination. For clarity, all figures are enlarged to the top-left side of theoverall graph.

Fig. 1.

(Color online) ROC curve for each combination of a model with and without menopause information.

Sensors and Materials, Vol. 31, No. 3 (2019)

887

Table 3
Performance for each premenopause and postmenopause data with a model trained with premenopause.
Menopause
Biomarker name
AUC
Sensitivity
Specificity(test data)
0.975
0.92080.9457
Prolactin, TTR
Pre
0.965
0.89470.9375
Post
0.979
0.932
0.95
0.98
0.91090.9239
HE4-ELISA, Prolactin
Pre
0.989
0.89470.9375Post
0.977
0.92060.91670.99
0.95050.9783
ApoCIII, HE4-ELISA, Prolactin
Pre0.994
0.97370.9688Post
0.993
0.93650.98330.992
0.95050.9674HE4-ELISA, Prolactin, TTR
Pre
0.989
0.9474
0.9688Post
0.993
0.95240.9667

As shown in Table 2, the performance improved when we used premenopause information
rather than postmenopause information. Table 3 shows the performance for each premenopause
and postmenopause data with a model trained with premenopause. Menopause (test data)
in Table 3 represents the type of test data. The hyphen (‘-’) represents all the data regardlessof the menopausal status. Unlike our expectation where a model trained with premenopause
data predicts premenopause data well, postmenopause data is predicted well compared withpremenopause data.

3.

Conclusions

In our previous work, we found four optimal biomarker combinations that can be used toclassify cancer and normal samples well. To improve the performance of those combinations,
we additionally used the menopausal status. In this study, we conducted a classification
experiment for detecting ovarian cancer. The Prolactin-TTR model showed a similar
performance and the remaining models showed better results in the learned model including
menopause. Menopausal status information is very important in the classification model forthe early diagnosis of ovarian cancer. Specifically, a model trained with premenopause dataclassifies normal and cancer well compared with that with postmenopause data. Thus, in thispaper, a method of combining human information with a sensed biomarker for the diagnosis ofovarian cancer is proposed.

Acknowledgments
This research was supported by Hallym University Research Fund 2015 (H20150664) andBasic Science Research Program through the National Research Foundation of Korea (NRF)
funded by the Ministry of Science, ICT & Future Planning (NRF-2017R1A2B4006297).

888

Sensors and Materials, Vol. 31, No. 3 (2019)

References
1234
5
6
7
89Y. S. Kim, M. K. Jang, C. Y. Park, H. J. Song, and J. D. Kim: Int. J. Bio-Sci. Bio-Technol. 5 (2013) 67.
M. K. Jang, Y. S. Kim, C. Y. Park, H. J. Song, and J. D. Kim: Int. J. Bio-Sci. Bio-Technol. 5 (2013) 215.
H. J. Song, S. K. Ko, J. D. Kim, C. Y. Park, and Y. S. Kim: Int. J. Bio-Sci. Bio-Technol. 5 (2013) 41.
Y. S. Kim, M. K. Jang, C. Y. Park, H. J. Song, and J. D. Kim: SoftTech ASTL 19 (2013) 85.
B. Nolen, A. Marrangoni, L. Velikokhatnaya, D. Prosser, M. Winans, E. Gorelik, and A. Lokshin: Gynecologic
Oncol. 112 (2009) 47.
T. Van Gorp, I. Cadron, E. Despierre, A. Daemen, K. Leunen, F. Amant, D. Timmerman, B. Moor, and I.
Vergote: Br. J. Cancer 104 (2011) 863.
B. W. Yin, A. Dnistrian, and K. O. Lloyd: Int. J. Cancer 98 (2002) 737.
J. A. Swets: Science 240 (1988) 1285.
M. Greiner, D. Pfeiffer, and R. Smith: Preventive Veterinary Med. 45 (2000) 23.