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J Health Info Stat > Volume 46(2); 2021 > Article
한국의 암 빅데이터 인식과 활용: 설문 조사



암 연구 및 치료를 위한 빅데이터에 대한 관심이 높아짐에 따라 헬스케어 관련 종사자들의 암 빅데이터 수요도를 조사하였다.


본 연구는 2019년 12월 3일부터 2020년 1월 7일까지 한국에서 실시되었다. 18개 헬스케어 관련 기관이 설문조사에 참여하였다. 회신 받은 172건의 설문지 중 164건의 설문지가 최종 분석에 활용되었다.


응답자의 대부분은 암 빅데이터에 대한 높은 인지도를 보였다(n=148, 90.2%). 다만 응답자의 절반 정도만이 암 빅데이터가 잘 활용되고 있다고(n=85, 51.8%) 답변했다. 빅데이터 사용 경험이 있는 응답자(n=83, 50.6%) 중 절반 이상은 1년에 한 번 정도만 빅데이터를 사용한 것으로 나타났다(n=43, 51.8%). 응답자들은 ‘항암제’(n=154, 94.5%) 정보 개방을 가장 필요로 하였으며, ‘암종별 진단 상태’, ‘임상 병기’, ‘재발 정보’ 등의 순서로 응답했다.


본 연구는 설문 조사를 통해 암 빅데이터에 대한 응답자들의 높은 인지도와 수요도를 확인할 수 있었다. 하지만 그에 비해 빅데이터는 잘 활용되지 못하는 것으로 확인하였다. 암 연구와 치료를 위한 빅데이터 활용도를 높이기 위해서는 의료 산업, 병원, 학계의 구체적인 요구사항에 적용할 수 있는 활용방안을 고려할 필요가 있다.



A growing interest in big data for cancer research and treatment motivated us to investigate the demand for it by healthcare users.


The survey was conducted from December 3, 2019 to January 7, 2020, in Korea. Respondents from 18 healthcare organizations participated in the survey. Among the 172 questionnaires received, 164 responses were used for the final analyses.


The majority of respondents showed a high awareness of big data related to cancer (n=148, 90.2%). However, only about half of the respondents were aware of how big data related to cancer is used (n=85, 51.8%). Among the respondents with experience using big data (n=83, 50.6%), more than half used big data only about once a year (n=43, 51.8%). The majority of respondents had particularly high demand for big data associated with “chemotherapy” (n=154, 94.5%), followed by “cancer type at diagnosis status”, “clinical stage,” and “recurrence.” The main considerations for releasing cancer big data were “trustworthiness of data” (63.2%), “provision of valuable data” (58.9%), and “improvement of data accuracy” (55.8%).


The study identified that even though respondents have a high awareness of and demand for big data related to cancer, it is not being sufficiently utilized at present. To increase the utilization of big data for cancer research and treatment, it is necessary to consider its purpose and how to make it available in line with the specific requirements of the health-care industry, hospitals, and academia.


Big data in healthcare refers to electronic health datasets so large and complex that they are difficult to manage with traditional software, hardware, or common data management tools and methods. Big data in healthcare is overwhelming not only because of its volume, but also because of the diversity of data types and the speed at which it must be managed [13].
Besides structured large-capacity data, the scope of big data has continually expanded in recent years to include unstructured information [16]. In particular, due to the development of information and communication technology and the change in the health paradigm that follows new technology, the amount of data in the healthcare field is rapidly increasing. Along with this, there is a growing interest in analyzing and using big data in the field of healthcare.
The potential of big data in healthcare relies on the ability to turn high volumes of data into actionable knowledge for precision medicine and decision-making. Big data analytics in healthcare is evolving into a promising field for the provision of insights from very large data sets while reducing costs [3,7]. Especially in the case of cancer, there is a large amount of data related to the diagnosis, decision-making, treatment, and prognosis of patients. There is high interest and unmet need for the utilization of such big data related to cancer in hospitals, industry, and academia.
Korea has a well-established system for utilizing large amounts of medical data accumulated through electronic medical records, and various attempts have been made to utilize this big data [812]. However, there have been no discussions on various demands or detailed methods to utilize big data for cancer [1315]. Therefore, the purpose of this study was to investigate the level of awareness and utilization demand of big data for cancer among workers in the academic, medical, and healthcare industries.


Study population and data collection

The target population of this study comprised individuals from tertiary hospitals, research institutes, pharmaceutical companies, contract research organizations, and academia in South Korea. The inclusion criteria were adults aged 19 years or older with the ability to understand a questionnaire, targeting students majoring in statistics, professors, researchers, data scientists, data managers, health information managers, and nurses. Those who refused to participate in this study were excluded. As a result, a total of 300 questionnaires were distributed. Data were collected either through paper surveys in face-to-face interviews or through questionnaires sent via e-mail. The responses were analyzed at the Yonsei Cancer Center, Korea.


Participation in this study was entirely voluntary, and anonymity was guaranteed. All participants agreed that the results of the survey may be used for research purposes.
The method was approved by the Ethics Committee of the Yonsei University College of Medicine, Seoul, Korea

Questionnaire items

We developed questionnaires to investigate the demand of users for big data related to cancer in healthcare contexts. Survey items were developed with reference to a previous study [16,17].
The questionnaires were originally written and conducted in Korean. The questionnaires consisted of five sections: (1) awareness of big data related to cancer; (2) big data usage status and purpose of use; (3) demand for the release of big data to the public; (4) utilization of big data; and (5) basic characteristics of the respondents. The total number of questions to which the respondents could reply was 18. The number of question responses differed because the questionnaire included some conditional questions (Appendix 1).

Statistical analysis

Categorical data were summarized as frequencies and percentages (%). A chi-square test analysis was performed to test for differences in proportions of continuous and categorical variables between two or more groups. Statistical analysis was performed using the SPSS version 25.0 (IBM Co., Armonk, NY, USA). p-values<0.05 were considered statistically significant. To ensure the clarity, precision, and accuracy of the results, the cases where respondents did not answer a sufficient number of questions, as well as the nonresponding cases, were excluded from the analyses.


Characteristics and knowledge of respondents

We conducted the survey from December 3, 2019 to January 7, 2020. A total of 300 questionnaires were distributed. A total of 172 questionnaire responses were received, and 164 responses were used for the final analyses. Eight respondents who skipped more than one-third of the questions were excluded from the final analyses. The majority of survey respondents worked in the healthcare industry (n=92, 56.1%) or in tertiary hospitals (n=51, 31.1%). Most respondents were in the field of oncology (n=113, 76.4%; multiple response question) and had more than 10 years of working experience (n=81, 49.4%) (Table 1).
Table 1
Baseline characteristics of the respondents (n=164)
Characteristics Respondents n (%)
 Healthcare industry 92 (56.1)
 Tertiary hospital 51 (31.1)
 Academia 21 (12.8)
Experience (y)
 ≥10 81 (49.4)
 5-< 10 41 (25.0)
 1-<5 28 (17.1)
 <1 14 (8.5)
Discipline (multiple response question)
 Oncology 113 (76.4)
 Bioinformatics 25 (16.9)
 Pharmacy 9 (6.1)
 Genetics 8 (5.4)
 Epidemiology 7 (4.7)
 Biochemistry 3 (2.0)
 Pathology 2 (1.4)
 Cardiology 2 (1.4)
 Radiology 2 (1.4)
 Pain management 2 (1.4)
 Others1 7 (4.7)

1 Includes infectious disease, neurology, chronic diseases, endocrinology, supportive care, surgery, gastroenterology, marketing, and regulatory.

In addition, we analyzed the results of the baseline characteristics in each group. There was a statistically significant difference in the years of experience of the group participating in the survey (p <0.001). However, in discipline comparisons, only oncology and bioinformatics were statistically significant (p <0.001 and p <0.001, respectively) (Supplementary Table 1).

Awareness of big data related to cancer

The question regarding the awareness of big data was segmented into four levels (Table 2). Most respondents (n=148, 90.2%) reported that they knew about big data for cancer. More than half of the respondents re-plied that they “know very well” (n=9, 5.5%) or “know a little” (n=83, 50.6%). The awareness of usage of big data in healthcare showed contra-dictory results, as about half reported “well” (n=81, 49.4%), which was closely followed by those who reported “not very well” (n=74, 45.1%) (Table 2). Most respondents were seen to agree (“strongly agree” (n=107, 65.2%) and “agree” (n=53, 32.3%)) on the need to use big data in health-care services, business, and research. The utilization of big data in the academia group was the highest with “strongly agree” (n=19, 90.5%). Overall, respondents answered that the use of big data in current health-care contexts is very necessary. Furthermore, the experience of participants in using big data, in terms of “yes” (n=83, 50.6%) and “no” (n=81, 49.4%), showed similar distributions. However, compared with each group, there was a higher proportion of “no” responses for experience using big data in the tertiary hospital group (n=36, 70.6%).
Table 2
Awareness of big data related to cancer (n=164)
Variables Total (n = 164)
Healthcare industry (n = 92)
Tertiary hospital (n = 51)
Academia (n = 21)
n (%) n (%) n (%) n (%)
How much do you know about big data related to cancer? 0.022
 Know very well 9 (5.5) 3 (3.3) 4 (7.8) 2 (9.5)
 Know a little 83 (50.6) 47 (51.1) 30 (58.8) 6 (28.6)
 Heard about it 56 (34.1) 34 (37.0) 15 (29.4) 7 (33.3)
 Don't know 16 (9.8) 8 (8.7) 2 (3.9) 6 (28.6)
Do you think big data is being used in healthcare? 0.057
 Very well 4 (2.4) 2 (2.2) 1 (2.0) 1 (4.8)
 Well 81 (49.4) 46 (50.0) 25 (49.0) 10 (47.6)
 Not very well 74 (45.1) 44 (47.8) 20 (39.2) 10 (47.6)
 Not at all 5 (3.0) 0 (0.0) 5 (9.8) 0 (0.0)
Do you think you need to use big data in your current health care, business or research? 0.053
 Strongly agree 107 (65.2) 62 (67.4) 26 (51.0) 19 (90.5)
 Agree 53 (32.3) 28 (30.4) 23 (45.1) 2 (9.5)
 Disagree 3 (1.8) 1 (1.1) 2 (3.9) 0 (0.0)
 Strongly disagree 1 (0.6) 1 (1.1) 0 (0.0) 0 (0.0)
Do you work with big data? 0.001
 Yes 83 (50.6) 57 (62.0) 15 (29.4) 11 (52.4)
 No 81 (49.4) 35 (38.0) 36 (70.6) 10 (47.6)

1 p-values for multiple response questionnaires are not applicable.

Status of usage of big data and purpose of use

Specifically, respondents with experience using big data were surveyed on the frequency, area and purpose of big data usage. Overall, responses for “more than once a year” (n=43, 51.8%) was the highest, followed by “at least once a month” (n=18, 21.7%) and “at least once a week” (n=11, 13.3%). Notably, the academia group had the most active big data users, selecting “at least once a day” (n=4, 36.4%) (Table 3). In addition, the utilization of “cancer diseases” big data (n=56, 68.3%) was the highest among all research areas. Regarding the purpose of using big data, responses for “collecting and utilizing data regarding work (service projects, proposals, reports, etc.)” (n=53, 65.4%) was the highest, followed by “collecting data for academic research” (n=48, 59.3%), and “collecting data for new drug development” (n=15, 18.5%).
Table 3
Usage of big data and purpose of use
Experience in big data In the case of having experience using big data
Total (n = 164)
Healthcare industry (n = 92)
Tertiary hospital (n = 51)
Academia (n = 21)
n (%) n (%) n (%) n (%)
How often do you use big data in your current business and research? (n = 83) 0.002
 At least once a day 5 (6.0) 1 (1.8) 0 (0.0) 4 (36.4)
 At least once a week 11 (13.3) 6 (10.5) 3 (20.0) 2 (18.2)
 At least once a month 18 (21.7) 13 (22.8) 3 (20.0) 2 (18.2)
 More than once a year 43 (51.8) 33 (57.9) 7 (46.7) 3 (27.3)
 Don't use it at all 6 (7.2) 4 (7.0) 2 (13.3) 0 (0.0)
With what type of data do you currently work in your research? (n = 82) (multiple response question) NA
 Cancer diseases 56 (68.3) 40 (70.2) 12 (85.7) 4 (36.4)
 Diabetes/Metabolic diseases 12 (14.6) 10 (17.5) 1 (7.1) 1 (9.1)
 Cardio-circulatory system disease 18 (22.0) 16 (28.1) 2 (14.3) 0 (0.0)
 Neurobiology diseases 7 (8.5) 7 (12.3) 0 (0.0) 0 (0.0)
 Digestive system diseases 2 (2.4) 0 (0.0) 2 (14.3) 0 (0.0)
 Respiratory diseases 1 (1.2) 1 (1.8) 0 (0.0) 0 (0.0)
 Infectious disease 7 (8.5) 7 (12.3) 0 (0.0) 0 (0.0)
 Dermatology disease 1 (1.2) 1 (1.8) 0 (0.0) 0 (0.0)
 DNA/RNA/protein sequence 7 (8.5) 4 (7.0) 1 (7.1) 2 (18.2)
 Others1 20 (24.4) 13 (22.8) 0 (0.0) 7 (63.6)
What is the purpose of using big data? (n = 81) (multiple response question) NA
 Development of web, application (App), and other services using the cancer data 4 (4.9) 1 (1.8) 2 (14.3) 1 (10.0)
 Collecting and utilizing data regarding work (service projects, proposals, reports, etc.) 53 (65.4) 46 (80.7) 3 (21.4) 4 (40.0)
 Acquiring information for starting companies or finding new business opportunities 9 (11.1) 8 (14.0) 0 (0.0) 1 (10.0)
 Collecting data for academic research 48 (59.3) 25 (43.9) 13 (92.9) 10 (10.0)
 Collecting data for policy research 7 (8.6) 4 (7.0) 3 (21.4) 0 (0.0)
 Collecting data for genome research 7 (8.6) 3 (5.3) 2 (14.3) 2 (20.0)
 Collecting data for new drug development 15 (18.5) 12 (21.1) 1 (7.1) 2 (20.0)
 Others2 1 (1.2) 0 (0.0) 1 (7.1) 0 (0.0)

1 Includes nonresponse as well as Health Risk Appraisal (HRA), pain data, gynecology, claim data, work-related, chemical compound data, older adults, autoimmune disease, drug safety, real estate data, and credit data.

2 Includes sales prediction for pharmaceuticals, and feasibility study on drugs.

3 p-values for multiple response questionnaires are not applicable.

Demand for releasing big data related to cancer

We analyzed the results of the questions pertaining to the demand for releasing big data related to cancer. The majority of respondents revealed a particularly high demand for data related to “chemotherapy” (n=154, 94.5%; multiple response question), followed by “cancer type at diagnosis status” (n=139, 85.3%), “clinical stage” (n=138, 84.7%), and “recurrence” (n=136, 83.4%). Overall, respondents wanted information mostly about the treatment and clinical status of patients (Supplementary Table 2).
Regardless of whether they had big data usage experience or not, the respondents said that they required big data in the form of “Excel based file (xls, xlsx, csv)” (n=139, 86.9%), followed by “LOD (Linked Open Data)” (n=19, 10.5%), and “File (json, xml)” (n=11, 6.1%) (Table 4). Most respondents (n=131, 80.4%) revealed a willingness to pay to utilize big data, and the proportion was higher in the group with experience using big data related to cancer (n=70, 84.3%). A higher proportion of academia group respondents with no big data usage experience reported a lack of willingness to pay for big data related to cancer (n=7, 70.0%). The main purpose of big data usage was for “collecting and utilizing data regarding work (service projects, proposals, reports, etc.)” (n=54, 65.1%) among the respondents with big data usage experience. The main considerations for the release/provision of big data related to cancer were: “trustworthiness of data” (n=103, 63.2%), “valuable data” (n=96, 58.9%), and “improvement of data accuracy” (n=91, 55.8%).
Table 4
Demand for the release of big data related to cancer
Experience in big data Total (n = 163)
Healthcare industry (n = 91)
Tertiary hospital (n = 51)
Academia (n = 21)
Yes n (%) No n (%) Yes n (%) No n (%) Yes n (%) No n (%) Yes n (%) No n (%)
In what data format do you want to receive big data related to cancer? (n = 160) (multiple response question) NA
 Excel-based file (xls, xlsx, csv) 72 (88.9) 67 (84.8) 49 (89.1) 29 (87.9) 14 (93.3) 32 (88.9) 9 (81.8) 6 (60.0)
 File (json, xml) 8 (9.9) 3 (3.8) 4 (7.3) 0 (0.0) 1 (6.7) 1 (2.8) 3 (27.3) 2 (20.0)
 Open API 5 (6.2) 1 (1.3) 3 (5.5) 0 (0.0) 0 (0.0) 0 (0.0) 2 (18.2) 1 (10.0)
 LOD 10 (12.3) 9 (11.4) 9 (16.4) 5 (15.2) 0 (0.0) 3 (8.3) 1 (9.1) 1 (10.0)
 Others 5 (6.2) 1 (1.3) 3 (5.5) 0 (0.0) 1 (6.7) 0 (0.0) 1 (9.1) 1 (10.0)
For statistical data using cancer big data, are you willing to pay the price? (n = 163) <0.001
 Yes 70 (84.3) 61 (76.3) 53 (93.0) 31 (91.2) 12 (80.0) 27 (75.0) 5 (45.5) 3 (30.0)
 No 13 (15.7) 19 (23.8) 4 (7.0) 3 (8.8) 3 (20.0) 9 (25.0) 6 (54.5) 7 (70.0)
How do you mainly intend to use the data? (n = 163) (multiple response question) NA
 Development of web, application (App), and other services using the cancer data 10 (12.0) 20 (25.0) 6 (10.5) 8 (23.5) 1 (6.7) 8 (22.2) 3 (27.3) 4 (40.0)
 Collecting and utilizing data regarding work (service projects, proposals, reports, etc.) 54 (65.1) 37 (46.3) 48 (84.2) 22 (64.7) 5 (33.3) 15 (41.7) 1 (9.1) 0 (0.0)
 Acquiring information for starting companies or finding new business opportunities 17 (20.5) 10 (12.5) 13 (22.8) 7 (20.6) 1 (6.7) 1 (2.8) 3 (27.3) 2 (20.0)
 Collecting data for academic research 52 (62.7) 62 (77.5) 30 (52.6) 23 (67.6) 14 (93.3) 29 (80.6) 8 (72.7) 10 (100.0)
 Collecting data for policy research 10 (12.0) 23 (28.8) 9 (15.8) 10 (29.4) 1 (6.7) 10 (27.8) 0 (0.0) 3 (30.0)
 Collecting data for genome research 13 (15.7) 23 (28.8) 4 (7.0) 9 (26.5) 6 (40.0) 13 (36.1) 3 (27.3) 1 (10.0)
 Collecting data for new drug development 24 (28.9) 39 (48.8) 19 (33.3) 21 (61.8) 2 (13.3) 14 (38.9) 3 (273) 4 (40.0)
 Others1 1 (1.2) 1 (1.3) 1 (1.8) 1 (2.9) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)
What do you think are important things to consider when releasing cancer big data? (n = 163) (multiple response question) NA
 A sufficient amount of data 29 (34.9) 30 (37.5) 19 (33.3) 18 (52.9) 3 (20.0) 6 (16.7) 7 (63.6) 6 (60.0)
 Trustworthiness of data 51 (61.4) 52 (65.0) 35 (61.4) 22 (64.7) 12 (80.0) 25 (69.4) 4 (36.4) 5 (50.0)
 Valuable data 46 (55.4) 50 (62.5) 30 (52.6) 20 (58.8) 9 (60.0) 22 (61.1) 7 (63.6) 8 (80.0)
 Improvement of data accuracy 46 (55.4) 45 (56.3) 35 (61.4) 17 (50.0) 7 (46.7) 21 (58.3) 4 (36.4) 7 (70.0)
 Timely data 21 (25.3) 16 (20.0) 17 (29.8) 6 (17.6) 3 (20.0) 9 (25.0) 1 (9.1) 1 (10.0)
 Up-to-date data 28 (33.7) 20 (25.0) 28 (49.1) 12 (35.3) 0 (0.0) 5 (13.9) 0 (0.0) 3 (30.0)
 Improvement of usability through standardization 34 (41.0) 41 (51.3) 24 (42.1) 18 (52.9) 6 (40.0) 18 (50.0) 4 (36.4) 5 (50.0)

LOD, linked open data; open API, Open application programming interface.

1 Includes development of evidence for insurance benefits of new drugs.

2 p-values for multiple response questionnaires are not applicable.


This survey was conducted to understand the awareness of big data for cancer from the perspective of users in the Korean healthcare environment. The main findings indicate that while most respondents recognize the necessity of using big data related to cancer in healthcare, business, and research, they are not using it frequently in practice. In addition, the majority of respondents indicated a particularly large demand for data on chemotherapeutic agents for treatments and other cancer-specific clinical information.
A higher percentage of people in academia, in comparison to other groups, do not know about big data, while the tertiary hospitals group reported the lowest rate of big data usage experience. Nevertheless, regardless of the institution, most respondents (over 95%) showed a willingness to use big data, especially in academia. The reasons for using big data were different among institutions, and as expected, the purpose of academic research received the highest response for the tertiary hospitals group (92.9%), with the healthcare industry tending to use big data for projects or reports (80.7%). Most respondents (80.4%) showed a willingness to pay for big data, and this willingness was highest among those in the healthcare industry, followed by tertiary hospitals and academia. The majority of responses were positive, indicating that respondents are aware of big data as an economically valuable resource.
Recently, there have been various attempts [11,16,1820] to analyze the needs for big data in the field of healthcare and to encourage big data utilization [21,22]. While European countries are supporting initiatives to utilize big data in the field of oncology [7], there has not been much effort in Korea to make legal and institutional improvements to encourage usage of big data for cancer. The term “big data” has become ex-tremely popular globally in recent years and almost every field of research, whether it relates to industry or academics, is generating and analyzing big data for various purposes [23]. Particularly in medicine and healthcare, big data analytics integrates the analysis of several scientific areas such as bioinformatics, medical imaging, as well as medical and health informatics. The application of big data analytics aids the discovery of comprehensive knowledge from the huge amounts of data available [24]. Big data analytics in medicine and healthcare enables analysis of large datasets from thousands of patients, identifying clusters and correlations between datasets, as well as developing predictive models using data mining techniques [25]. The combination of data analysis and artificial intelligence technologies such as machine learning and deep learning allows for innovation in healthcare services such as patient-specific clinical decision support system utilization and precision medicine in real time [26,27]. Additionally, in new drug development, a field that en-tails enormous time and high investment costs, a partial solution to the cost-efficiency problem is expected through the utilization of accumulated big data on cancer in clinical trials for diagnosis, treatment, results, and prescriptions.
Korea has recently started to promote the use of accumulated big data in cancer research and treatment along with enhancement of privacy and utilization through the enactment of relevant laws. The release of generated big data relating to cancer is a current trend creating added value. It is very important to consider the requirements of various stake-holders in big data usage and related analyses.
This study has several limitations. First, there are important differences that may limit the generalizability of the study findings to the Korean population and may not reflect the opinion of all survey respondents. Second, responses may differ depending on the public policies or legal frameworks in other countries. Third, the respondents in this study were primarily from the healthcare industry and tertiary hospitals, and nota-bly, the respondents from academia were few. Moreover, those who were not familiar with big data did not participate in the survey, and thus, the participants willing to answer the survey could be those who have more knowledge or awareness about the topic and might bias the results. Last-ly, the cross-validation of the same perception among institutions was not compared, and thus requires further investigation. Hence, the results of this survey may only reflect the current situation in South Korea. De-spite these limitations, this study forms an important baseline for future studies.
The study was able to identify the high awareness and demand for big data related to cancer among the respondents through the survey. How-ever, compared to the high demand indicated in the survey responses, big data is not being well utilized. There will likely be more demand for big data utilization in the “new normal” era following the coronavirus disease (COVID-19) pandemic. To increase the utilization of big data for cancer, it is necessary to consider ways to release the information in accordance with the purpose and the finer details necessary for using such data in the healthcare industry, hospitals, and academia.
Furthermore, it is necessary to lay the foundation for an environment that can enhance consumer-centered data accessibility and establish detailed policies regarding the scope of using such data, and the methods and procedures associated with rapid and secure release of cancer related big data.


This study is supported by a grant from the Big data Center at the National Cancer Center of Korea (Grant number: 2021-data-we06).


No potential conflict of interest relevant to this article was reported.

Supplementary Material

Supplementary Table 1
Supplementary Table 2


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Appendix 1

Questionnaire on utilization of cancer-related big data

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