Document Type : Original Article


1 Department of Oral Medicine, School of Dentistry, Birjand University of Medical Sciences, Birjand, Iran

2 Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

3 Department of Oral Medicine and Dental Research Center, Faculty of Dentistry, Tehran University of Medical Sciences, Tehran, Iran

4 Department of Health Information technology, Ferdows School of Paramedical and Health, Birjand University of Medical Sciences, Birjand, Iran


Background: Due to the complexity of prognosis, diagnosis, and treatment in the process of providing care for patients with oral cancer, a large amount of data elements have been processed. The present study was conducted to provide a minimum data set for managing the data generated in the diagnosis and treatment processes of oral cancer by reviewing the specialized literature, medical records and by gathering expert opinions.
Method: This research was a descriptive cross-sectional study with the following steps: reviewing texts and records, developing a draft of data elements, organizing a panel of experts, Delphi techniques, and creating a final pattern.
Results: The framework proposed in this study for managing the data generated in the diagnosis and treatment processes of oral cancer was divided into six sections: management data with four-axis, historical data with four-axis, paraclinical indicators with two-axis, clinical indicators, data related to the therapeutic measures, and mortality data.
Conclusion: The systematic collection of the data associated with the diagnosis and treatment of the patients with oral cancer could provide a good basis for identifying patients or those who are susceptible to this type of cancer in the community. These data can also be used in programs to prevent the development and/or emergence of the disease, thus the health of the community.


How to cite this article:

Akbari N, Safdari R, Mansourian A, Ehtesham H. Development of a national consensus minimum data set for the diagnosis and treatment of oral cancer: towards precision management. Middle East J Cancer. 2022;13(2):343-51. doi: 10. 30476/mejc.2021.87375.1414.

1. Asmarian NS, Ruzitalab A, Amir K, Masoud S, Mahaki
B. Area-to-Area Poisson Kriging analysis of mapping
of county-level esophageal cancer incidence rates in
Iran. Asian Pac J Cancer Prev. 2013;14(1):11-3. doi:
2. Kumar V, Abbas A, Aster J. Robbins and cotran
pathologic basis of disease. 9th ed. Netherlands:
Elsevier; 2014.
3. Krishna Rao SV, Mejia G, Roberts-Thomson K, Logan
R. Epidemiology of oral cancer in Asia in the past
decade--an update (2000-2012). Asian Pac J Cancer
Prev. 2013;14(10):5567-77. doi: 10.7314/apjcp.
4. Chen XJ, Zhang XQ, Liu Q, Zhang J, Zhou G.
Nanotechnology: a promising method for oral cancer
detection and diagnosis. J Nanobiotechnology.
2018;16(1):52. doi: 10.1186/s12951-018-0378-6.
5. Ehtesham H, Safdari R, Mansourian A, Tahmasebian
S, Mohammadzadeh N, Pourshahidi S. Developing a
new intelligent system for the diagnosis of oral
medicine with case-based reasoning approach. Oral
Dis. 2019;25(6):1555-63. doi: 10.1111/odi.13108.
6. Jeyaraj PR, Samuel Nadar ER. Computer-assisted
medical image classification for early diagnosis of
oral cancer employing deep learning algorithm. J
Cancer Res Clin Oncol. 2019;145(4):829-37. doi:
7. Davey CJ, Slade SV, Shickle D. A proposed minimum
data set for international primary care optometry: a
modified Delphi study. Ophthalmic Physiol Opt.
2017;37(4):428-39. doi: 10.1111/opo.12372.
8. Zahmatkeshan M, Farjam M, Mohammadzadeh N,
Noori T, Karbasi Z, Mahmoudvand Z, et al. Design
of infertility monitoring system: Minimum data set
approach. J Med Life. 2019;12(1):56. doi: 10.25122/
9. Ahmadi M, Alipour J, Mohammadi A, Khorami F.
Development a minimum data set of the information
management system for burns. Burns.
2015;41(5):1092-9. doi: 10.1016/j.burns.2014.12.009.
10. Hornby K, Shemie SD, Appleby A, Dodd N, Gill J,
Kim J, et al. Development of a national minimum
data set to monitor deceased organ donation
performance in Canada. Can J Anaesth. 2019;66(4):
422-31. doi: 10.1007/s12630-018-01290-8.
11. Sheykhotayefeh M, Safdari R, Ghazisaeedi M,
Khademi SH, Seyed Farajolah SS, Maserat E, et al.
Development of a minimum data set (MDS) for Csection
anesthesia information management system
(AIMS). Anesth Pain Med. 2017;7(2):e44132. doi:
12. Stone E, Rankin N, Phillips J, Fong K, Currow DC,
Miller A, et al. Consensus minimum data set for lung
cancer multidisciplinary teams: Results of a Delphi
process. Respirology. 2018;23(10):927-34. doi:
13. Schaller M, Hackl WO, Ianosi B, Ammenwerth E.
Towards a systematic construction of a minimum data
set for delirium to support secondary use of clinical
routine data. Stud Health Technol Inform. 2019;264:
1026-30. doi: 10.3233/SHTI190380.
14. Safdari R, Ghazi Saeedi M, Masoumi-Asl H, Rezaei-
Hachesu P, Mirnia K, Mohammadzadeh N, et al.
National minimum data set for antimicrobial resistance
management: Toward global surveillance system. Iran
J Med Sci. 2018;43(5):494-505.
15. Damanabi S, Abdolnejad S, Karimi G. Suggested
minimum data Set for speech therapy centers affiliated
to Tabriz University of Medical Sciences. Acta
Informatica Medica. 2015;23(4):243. doi: 10.5455/aim.
16. Kalankesh LR, Dastgiri S, Rafeey M, Rasouli N,
Vahedi L. Minimum data set for cystic fibrosis registry:
a case study in Iran. Acta Informatica Medica. 2015;
23(1):18. doi: 10.5455/aim.2015.23.18-21.
17. Hajesmaeel-Gohari S, Bahaadinbeigy K, Tajoddini S,
R Niakan Kalhori S. Minimum data set development
for a drug poisoning registry system. Digit Health.
2019;5:2055207619897155. doi: 10.1177/20552076
18. Jafar AJN, Sergeant JC, Lecky F. What is the interrater
agreement of injury classification using the WHO
minimum data set for emergency medical teams?
Emerg Med J. 2020;37(2):58-64. doi: 10.1136/
19. Sanson G, Alvaro R, Cocchieri A, Vellone E, Welton
J, Maurici M, et al. Nursing diagnoses, interventions,
and activities as described by a nursing minimum data
set: a prospective study in an oncology hospital setting.
Cancer Nurs. 2019;42(2):E39-E47. doi: 10.1097/NCC.
20. Hoben M, Poss JW, Norton PG, Estabrooks CA.
Oral/dental items in the resident assessment instrument–
minimum data set 2.0 lack validity: results of a
retrospective, longitudinal validation study. Popul
Health Metr. 2016;14(1):36. doi: 10.1186/s12963-
21. Sahu A, Krishna CM. Optical diagnostics in oral
cancer: an update on Raman spectroscopic applications.
J Cancer Res Ther. 2017;13(6):908. doi: 10.4103/0973-
22. Deng H, Sambrook P, Logan R. The treatment of oral
cancer: an overview for dental professionals. Aust
Dent J. 2011;56(3):244-52. doi: 10.1111/j.1834-
23. Chiou SJ, Lin W, Hsieh CJ. Assessment of duration
until initial treatment and its determining factors among
newly diagnosed oral cancer patients: A populationbased
retrospective cohort study. Medicine.
2016;95(50). doi: 10.1097/MD.0000000000005632.
24. Shenoi R, Devrukhkar V, Sharma B, Sapre S, Chikhale
A. Demographic and clinical profile of oral squamous
cell carcinoma patients: A retrospective study. Indian
J Cancer. 2012;49(1):21. doi: 10.4103/0019-509X.
25. Chuang SL, Su WWY, Chen SLS, Yen AMF, Wang
CP, Fann JCY, et al. Population-based screening
program for reducing oral cancer mortality in 2,334,299
Taiwanese cigarette smokers and/or betel quid chewers.
Cancer. 2017;123(9):1597-609. doi: 10.1002/
26. Inglehart R, Taberna M, Pickard R, Hoff M, Fakhry
C, Ozer E, et al. HPV knowledge gaps and information
seeking by oral cancer patients. Oral Oncol. 2016;
63:23-9. doi: 10.1016/j.oraloncology.2016.10.021.
27. Macià F, Pumarega J, Gallén M, Porta M. Time from
(clinical or certainty) diagnosis to treatment onset in
cancer patients: the choice of diagnostic date strongly
influences differences in therapeutic delay by tumor
site and stage. J Clin Epidemiol. 2013;66(8):928-39.
doi: 10.1016/j.jclinepi.2012.12.018.
28. Macleod U, Mitchell E, Burgess C, Macdonald S,
Ramirez A. Risk factors for delayed presentation and
referral of symptomatic cancer: evidence for common
cancers. Br J Cancer. 2009;101(S2):S92. doi: 10.1038
29. Pérez MGS, Bagán JV, Jiménez Y, Margaix M, Marzal
C. Utility of imaging techniques in the diagnosis of
oral cancer. J Craniomaxillofac Surg. 2015;43(9):1880-
94. doi: 10.1016/j.jcms.2015.07.037.
30. Varela-Centelles P, López-Cedrún JL, Fernández-
Sanromán J, Seoane-Romero JM, Santos de Melo N,
Álvarez-Nóvoa P, et al. Key points and time intervals
for early diagnosis in symptomatic oral cancer: a
systematic review. Int J Oral Maxillofac Surg.
2017;46(1):1-10. doi: 10.1016/j.ijom.2016.09.017.
31. Glick M. Burket's Oral Medicine. 12th ed: PMPHUSA,
Ltd: USA; 2015.
32. Mortazavi H, Safi Y, Baharvand M, Rahmani S, Jafari
S. Peripheral exophytic oral lesions: A clinical decision
tree. Int J Dent. 2017;2017:9193831. doi: 10.1155/2017
33. Sundermann BV, Uhlmann L, Hoffmann J, Freier K,
Thiele OC. The localization and risk factors of
squamous cell carcinoma in the oral cavity: a
retrospective study of 1501 cases. J Craniomaxillofac
Surg. 2018;46(2):177-82. doi: 10.1016/j.jcms.
34. Gupta P, Migliacci JC, Montero PH, Zanoni DK, Shah
JP, Patel SG, et al. Do we need a different staging
system for tongue and gingivobuccal complex
squamous cell cancers? Oral Oncol. 2018;78:64-71.
doi: 10.1016/j.oraloncology.2018.01.013.
35. Han S, Chen Y, Ge X, Zhang M, Wang J, Zhao Q, et
al. Epidemiology and cost analysis for patients with
oral cancer in a university hospital in China. BMC
Public Health. 2010;10(1):196. doi: 10.1186/1471-